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Platnica SV-JME 2-2010_08.pdf 1 15.2.2011 10:38:19

Strojniški vestnik – Journal of Mechanical Engineering (SV-JME)

© 2011 Strojniški vestnik - Journal of Mechanical Engineering. All rights reserved. SV-JME is indexed / abstracted in: SCI-Expanded, Compendex, Inspec, ProQuest-CSA, SCOPUS, TEMA. The list of the remaining bases, in which SV-JME is indexed, is available on the website. The journal is subsidized by Slovenian Book Agency.

Strojniški vestnik - Journal of Mechanical Engineering is also available on http://www.sv-jme.eu, where you access also to papers’ supplements, such as simulations, etc.

Editor in ChiefVincenc ButalaUniversity of Ljubljana Faculty of Mechanical Engineering, Slovenia

Co-EditorBorut BuchmeisterUniversity of MariborFaculty of Mechanical Engineering, Slovenia

Technical EditorPika ŠkrabaUniversity of Ljubljana Faculty of Mechanical Engineering, Slovenia

Editorial OfficeUniversity of Ljubljana (UL)Faculty of Mechanical EngineeringSV-JMEAškerčeva 6, SI-1000 Ljubljana, SloveniaPhone: 386-(0)1-4771 137Fax: 386-(0)1-2518 567E-mail: [email protected]://www.sv-jme.eu

Founders and PublishersUniversity of Ljubljana (UL)Faculty of Mechanical Engineering, Slovenia

University of Maribor (UM)Faculty of Mechanical Engineering, Slovenia

Association of Mechanical Engineers of Slovenia

Chamber of Commerce and Industry of SloveniaMetal Processing Industry Association

International Editorial BoardKoshi Adachi, Graduate School of Engineering,Tohoku University, JapanBikramjit Basu, Indian Institute of Technology, Kanpur, IndiaAnton Bergant, Litostroj Power, Slovenia Franci Čuš, UM, Faculty of Mech. Engineering, SloveniaNarendra B. Dahotre, University of Tennessee, Knoxville, USAMatija Fajdiga, UL, Faculty of Mech. Engineering, SloveniaImre Felde, Bay Zoltan Inst. for Mater. Sci. and Techn., HungaryJože Flašker, UM, Faculty of Mech. Engineering, SloveniaBernard Franković, Faculty of Engineering Rijeka, CroatiaJanez Grum, UL, Faculty of Mech. Engineering, SloveniaImre Horvath, Delft University of Technology, NetherlandsJulius Kaplunov, Brunel University, West London, UKMilan Kljajin, J.J. Strossmayer University of Osijek, CroatiaJanez Kopač, UL, Faculty of Mech. Engineering, SloveniaFranc Kosel, UL, Faculty of Mech. Engineering, SloveniaThomas Lübben, University of Bremen, GermanyJanez Možina, UL, Faculty of Mech. Engineering, SloveniaMiroslav Plančak, University of Novi Sad, SerbiaBrian Prasad, California Institute of Technology, Pasadena, USABernd Sauer, University of Kaiserlautern, GermanyBrane Širok, UL, Faculty of Mech. Engineering, SloveniaLeopold Škerget, UM, Faculty of Mech. Engineering, SloveniaGeorge E. Totten, Portland State University, USANikos C. Tsourveloudis, Technical University of Crete, GreeceToma Udiljak, University of Zagreb, CroatiaArkady Voloshin, Lehigh University, Bethlehem, USA

President of Publishing CouncilJože DuhovnikUL, Faculty of Mechanical Engineering, Slovenia

PrintTiskarna Present d.o.o., Ižanska cesta 383, Ljubljana, Slovenia

General informationStrojniški vestnik – The Journal of Mechanical Engineering is published in 11 issues per year (July and August is a double issue). Institutional prices include print & online access: institutional subscription price €100,00, general public subscription €25,00, student subscription €10,00, foreign subscription €100,00 per year. The price of a single issue is €5,00. Prices are exclusive of tax. Delivery is included in the price. The recipient is responsible for paying any import duties or taxes. Legal title passes to the customer on dispatch by our distributor. Single issues from current and recent volumes are available at the current single-issue price.To order the journal, please complete the form on our website. For submissions, subscriptions and all other information please visit: http://en.sv-jme.eu/ You can advertise on the inner and outer side of the back cover of the magazine.We would like to thank the reviewers who have taken part in the peer-review process.

Cover: The picture shows air-flow simulation performed while developing energy saving unit for canister cooling which is used on plastic moulding machine. Velocity streamlines represent simulated air-flow in the existing cooling-unit (above) and in the new cooling-unit (below) with optimized geometry design. Advanced geometry design which maximizes air-flow is based on Venturi principle and is further optimized to allow better warm-air disposal. Image courtesy: University of Maribor, Faculty of Mechanical Engineering, Oil Hydraulics Laboratory

ISSN 0039-2480

Aim and ScopeThe international journal publishes original and (mini)review articles covering the concepts of materials science, mechanics, kinematics, thermodynamics, energy and environment, mechatronics and robotics, fluid mechanics, tribology, cybernetics, industrial engineering and structural analysis. The journal follows new trends and progress proven practice in the mechanical engineering and also in the closely related sciences as are electrical, civil and process engineering, medicine, microbiology, ecology, agriculture, transport systems, aviation, and others, thus creating a unique forum for interdisciplinary or multidisciplinary dialogue.The international conferences selected papers are welcome for publishing as a special issue of SV-JME with invited co-editor(s).

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Strojniški vestnik - Journal of Mechanical Engineering 57(2011)2Contents

Contents

Strojniški vestnik - Journal of Mechanical Engineeringvolume 57, (2011), number 2

Ljubljana, February 2011ISSN 0039-2480

Published monthly

Papers

Darko Lovrec, Vito Tič: Energy Saving Cooling-Unit for Plastic Moulding Machine 83Huseyin Gurbuz, Abdullah Kurt, Ibrahim Ciftci, Ulvi Seker: The Influence of Chip Breaker

Geometry on Tool Stresses in Turning 91Vladimir Popovic, Branko Vasic, Milos Petrovic, Sasa Mitic: System Approach to Vehicle

Suspension System Control in CAE Environment 100Aleš Slak, Jože Tavčar, Jože Duhovnik: Application of Genetic Algorithm into Multicriteria

Batch Manufacturing Scheduling 110Uroš Stritih, Vincenc Butala: Energy Savings in Building with a PCM Free Cooling System 125Mário João Simões Ferreira dos Santos - Jaime Batista dos Santos: FPGA-Based Control

System of an Ultrasonic Phased Array 135Franci Čuš, Uroš Župerl: Real-Time Cutting Tool Condition Monitoring in Milling 142Aleksandar Vujovic, Zdravko Krivokapic, Mirko Sokovic: Improvement of Business Processes

Performances through Establishment of the Analogy: Quality Management System – Human Organism 151

Instructions for Authors 163

Strojniški vestnik - Journal of Mechanical Engineering 57(2011)2, 83-90 Paper received: 13.04.2010DOI:10.5545/sv-jme.2010.082 Paper accepted: 06.10.2010

*Corr. Author’s Address: University of Maribor, Faculty of Mechanical Engineering, Smetanova 17, 2000 Maribor, [email protected] 83

Energy Saving Cooling-Unit for Plastic Moulding Machine

Darko Lovrec1,* - Vito Tič1,2

1 University of Maribor, Faculty of Mechanical Engineering, Slovenia 2 Olma d.d., Slovenia

Compressed air is one of the major sources of energy consumption and also one of the most expensive types of energy. Consequently, special attention must be paid to its rational use. This is especially important in cases where compressed air is blowed into an ”empty” space. All cleaning and cooling processes using compressed air are typical examples of such applications. They are the most wasteful consumers of compressed air. Measures to minimize air consumption in such cases are presented in the paper.

The highly efficient cooling unit for plastic moulding machines has been designed and optimized based on CFD simulation, and then produced using RP/RM (Rapid Prototyping / Manufacturing) manufacturing technology. The developed and specially designed nozzle-system was used for cooling purposes during plastic canister manufacturing. The previously-used conventional cooling system had so far been recognized as the most wasteful compressed air consumer in the company. The newly-designed air-efficient cooling unit, with increased capacity for cooling air, allows for up to 50% energy savings regarding compressed air.© 2011 Journal of Mechanical Engineering. All rights reserved.Keywords: compressed air, cooling, blow moulding machine

0 INTRODUCTION

Compressed air is one of the major sources of energy consumption both in industry and its accompanying activities, and can be found in almost every factory. Compressed air powers a variety of equipment, including machine tools, material-handling and separation equipment, and spray-painting equipment. It is most often used in factories for cleaning, polishing, lacquering, air cleaning, drying etc. It is used in drilling, turning and forging processes in assembly plants in order to carry out material handling in combination with control techniques. It is indispensable in tyre-vulcanizing services, and many other applications. The area using compressed air is very broad, and examples are very diverse.

Among other things, compressed air is used for cooling either tools or products in many technical processes - in spite of the fact that using compressed air for the cooling of parts is energetically ineffective. In metal or plastic machining processes, compressed air can be used e.g. for saw blades cooling of or for high-efficient HM or HSS milling cutters. It can also be used with tools, which should not be cooled with

coolant or e.g. when machining plastic products such as the cutting of pipes. During high-speed cutting the saw’s blade should be cooled with air rather than water or a cooling emulsion because they can harm the material. With air cooling, a longer lifetime of the saw blade can be expected as well as better surface quality.

In all the cases mentioned, the cooling effect can be continuously, variably, or individually adjusted according to the required application. Nevertheless, the use of compressed air for cooling purposes is considered to be very uneconomical.

In compressed air systems the cost of energy is extremely high since normally a lot of unused heat is released, indicating that air is a very expensive type of energy [1]. Therefore, great effort is invested in reducing these costs and losses that occur along the entire route of the energy conversion, from electrical to the place where it is used for useful work (Fig. 1).

Due to its many losses compressed air is one of the most expensive types of energy, produced from electricity.

Modern compressed air systems typically convert only 20% of the consumed energy into mechanical work, while 80% of the energy

Strojniški vestnik - Journal of Mechanical Engineering 57(2011)2, 83-90

84 Lovrec, D. - Tič, V.

consumed is dissipated in form of heat losses. Compressed air needs to be properly prepared (filtering, drying, reducing air pressure level), and if the device is poorly maintained, only 5% of the 100% electrical energy consumed is converted into mechanical work.

Fig. 1. Energy flow in a compressed air system

Air-leakages represent a particular problem in compressed air transport along the pipelines. The magnitude of this energy loss varies with the size and number of holes in the pipeline and on the machine - e.g. a small 3 mm hole causes 1000 euro annual loss, and a 6 mm hole will cause approx. 4500 euro of annual loss (at 6 bar). Sufficient facts indicate that special care must be taken when planning a compressor station, preparing air, and providing leakage-free air distribution. The leftover energy must be used in the most considered and rational way: these 5% needs to be dealt with rationally. This is especially important in cases where compressed air is blown into free space. All cleaning and cooling processes using compressed air are typical examples of such applications and they represent the most wasteful consumers of compressed air. This indicates that focus on measures to minimize compressed air consumption in such applications is needed.

1 COMPRESSED AIR ON PLASTIC BLOW-MOULDING MACHINES

Compressed air is also used on plastic blow-moulding machines. Blow-moulding, also

known as blow-forming, is a manufacturing process where hollow plastic parts are formed. In general, there are three main types of blow-moulding: injection blow-moulding, stretch-blow moulding and extrusion blow-moulding [2].

The blow-moulding process begins with the conventional extrusion of a parison or tube, using a die, similar to the one used for making plastic pipes. The parison is commonly extruded downwards between the two halves of an open blow-mould (Fig. 2). When the parison reaches the proper length, the mould closes, catching and holding the neck-end open, and pinching the bottom-end closed. A rod-like blow pin is inserted into the neck-end of the hot parison to simultaneously form the threaded opening and to inflate the parison inside the mould cavity using compressed air, then pressing it against a mould cavity. The pressure is held until the plastic cools. After the form cools, the mould opens to eject the bottle. Blow-moulding is often used to produce plastic vessels and containers, plastic bottles, canister, jugs, barrels, other containers, etc.

Fig. 2. Phases of extrusion blow-moulding processes

After the extrusion process, it is only necessary to remove the residues from the bottom of the plastic vessel, or residue from the throat and, in the case of canisters, the plastic rests under and above the handles.


85Energy Saving Cooling-Unit for Plastic Moulding Machine

The cooling process is performed using a simple cooling unit with multiple nozzles, as shown in Fig. 3, which consumes a large amount of compressed air – the cooling phase lasts approx. 15 s, with compressed air consumption of 48 m3/h. Such energy wasteful cooling systems in the plastic industry should be redesigned [5].

2 DESIGN OF ENERGY-SAVING COOLING-UNIT

Air blow-nozzles (e.g. in blow-guns) are one of the main consumers of compressed air in industry. Depending on the process and its requirements, a large amount of air is blown-out. This high consumption of air is not only highly expensive, but can also cause huge pressure drops in the pneumatic system – malfunctions.

Common blown-guns with classical nozzles work on the principle: “What comes in must come out”. In the case of air-saving nozzles, it is not necessarily always so.

2.1 Energy-Saving Nozzles – Theoretical Background

Energy-saving nozzles work on the “Venturi nozzles” principle with side inlets. The operating principle is based on the Bernoulli and Continuity equation. By reducing the nozzle cross-section, the speed increases, and consequently the pressure is reduced all the way to vacuum, so that the surrounding air is sucked through the side-openings. The additional air sucked from the ambient does not have to be compressed. This means that the blow-gun is blowing out more air than it is actually consuming – see Fig. 4.

Example “Type A” represents air consumption of the ordinary blow-gun with classical nozzle, while example “Type B” shows air consumption of an energy-saving nozzle. Savings in compressed air consumption are approximately 40%, in some cases even more (by lower input pressures).

In order to achieve the maximal efficiency of such a nozzle (the smallest amount of air entering and maximal leaving) the inside shape of the nozzle must be appropriately designed: flow conditions must be optimized to ensure maximal suction effect.

These is carried out after cooling of the mentioned canisters’ parts at a special workplace (machine works in processing tact – cooling and cutting of residues is done simultaneously with blowing next canister) [3].

Fig. 3 shows the appearance of the discussed plastic canister as the end-product, areas of hot plastic above and below the handle, which must be removed, showed in an infra-red spectrum (as seen using a thermal camera) [4]. The picture also shows the existing cooling unit.

Fig. 3. Plastic canister - fields of hot plastic during cooling process; a) existing cooling-unit

and b) temp. scale: 28 to 166°C

Using the thermal camera captured image the already cooled canister-body (water cooling in the tool) and the hot material surpluses (residual of blow-moulding process) can be clearly seen. Cutting area of the canister must be cooled-down to a suitable temperature before cutting: from approximately 170 to 180 °C, to a temperature which ensures the required hardness of the material for faultless cut (without damage to the product), which is about 135 to 138 °C.



Type A: Classical nozzle

Type B: Energy-saving nozzle

Fig. 4. Air consumption of different blow-nozzles at an operating pressure 4.5 bar

The optimal nozzle geometry (in terms of the most effective sucking from the surrounding air) is designed based on the corresponding mathematical model, and numerical simulations of air-flow through the nozzle. Simulations were carried out using the ANSYS Workbench program package [6].

2.2 Meshing

Numerical simulation is based on the finite-volumes method (FVM). The calculation area is a 3D volume, closed from all sides. The area is divided into tetrahedrons. Such a tetrahedron is called a flow-element, and its edges represent the calculating nodes. This type of mesh is a non-structured mesh. The location of the nodes is determined by the Cartesian coordinates x, y, and z. The dependent variables of pressure, velocity, turbulent kinetic energy, dissipation of the turbulent kinetic energy and the volume fraction are calculated for each node of the flow-element [7].

Surface and volume mesh were automatically created using Ansys CFX-Mesh with regard to additional settings. The mesh was refined near each narrowing in order to obtain more realistic simulation results [8] and [9].

Also, five inflation layers were created – the near solid wall’s boundary layers affect

velocity gradients, which are maximal in the normal direction to the wall.

2.3 Simulation Model

Most industrial flows include turbulent structures that cannot be resolved numerically on currently available computers. In order to overcome these limitations, computational fluid dynamics (CFD) methods solve the Reynolds averaged Navier-Stokes equations, using turbulence models to compute the averaged turbulent stresses. These models often limit the accuracy of CFD simulations [10].

The standard k-ε model is used in the prediction of most turbulent flow calculations because of its robustness, economy, and reasonable accuracy over a wide range of flows. However, this model performs poorly when faced with non-equilibrium boundary layers. It tends to predict the onset of separation too late, as well as to underestimate the amount of separation. Separation influences the overall performances of many devices, such as diffusers, turbine blades and aerodynamic bodies. Separation also has a strong influence on other effects, such as wall heat transfer and multi-phase phenomena. Predicting reduced separation usually results in an optimistic prediction of machine performance. In some applications, this can have dangerous consequences, a notable example being the prediction of wing-stall on airplanes [10].

New models have been developed in order to solve this problem. One of the most effective is the shear-stress transport (SST) model. This model works by solving a turbulence/frequency-based model (k-ω) at the wall, and k-ε in the bulk flow. A blending function ensures a smooth transition between the two models.

One of the advantages of the k-ω formulation is the near-wall treatment for low-Reynolds number computations. The model does not involve the complex non-linear damping functions required for the k-ε model and is, therefore, more accurate and more robust. A low-Reynolds k-ε model would typically require a near-wall resolution of y+ < 0.2, while a low-Reynolds number k-ω model would require at least y+ < 2. In industrial flows, even y+ < 2 cannot be guaranteed in most applications and for this



CD kkωω

ρσ ω

ω= ∇ ∇ ×

−max , . 2 1 1 0 102

10 , (6)

F2 2= tanh( )2arg , (7)

with:

arg ky

vy2 2

2 500=

′

max ,

β ω ω. (8)

Further information on the SST model can be found in literature [2].

The energy saving-nozzle was developed on the basis of the process described above. Internal geometry of the nozzle was optimized based on the air-flow simulations, and is shown in Fig. 5. Air was modelled as compressible gas with constant properties at 25 °C. Heat transfer was neglected at this stage of research.

Fig. 5. CFD simulation result of nozzle

2.4 Design of Energy-Saving Cooling-Unit

A similar approach was to design the energy-saving cooling-unit. Compared to the relatively simple geometry of the nozzle, the geometry of the cooling-unit is definitely more complicated and complex.

The basic idea of the cooling-unit design was to connect series of nozzles in line with a single outlet’s joint to a common outlet manifold. A cross-section of the cooling-unit geometry (at the nozzle) with expected flow conditions is shown in Fig. 6.

reason a new near-wall treatment was developed for the k-ω models. It allows for smooth shift from a low-Reynolds number form to a wall function formulation [6].

The k-ω models assume that the turbulence viscosity is linked to the turbulence kinetic energy and turbulent frequency via the relationship:

µ ρωtk

= . (1)

The k-ω based SST model accounts for the transportation of the turbulent shear-stress and gives highly accurate predictions of the onset and the amount of flow-separation under adverse pressure gradients. The Baseline k-ω model combines the advantages of the Wilcox and the k-ε model, but still fails to properly predict the onset and amount of flow-separation from smooth surfaces. The main reason is that both models do not account for the transportation of turbulent shear-stress. This results in an overestimation of the eddy-viscosity. Proper transport behaviour can be obtained by a limiter to the formulation of the eddy-viscosity:

v a ka SFt =

1

1 2max( )ω, (2)

where

vtt=µρ

. (3)

Again F2 has a blending function similar to F1, which restricts the limiter to the wall boundary layer, as the underlying assumptions are incorrect for free shear-flows. S is an invariant measure of the strain rate.

The blending functions are critical to the success of this method. Their formulation is based on the distance to the nearest surface, and on the flow variables:

F1 1= tanh( )4arg , (4)with:

arg ky

vy

kCD ykw

1 22

2

500 4=

′

min maxβ ω ω

ρσω

, , , (5)

where y is the distance to the nearest wall, v is the kinematic viscosity and:



Fig. 6. Cross-section a) of existing cooling-unit, b) new cooling-unit with additional openings for

better warm air disposal

The mesh results of the cooling unit are shown in Fig. 7 and presented in Table 1.

Table 1. Meshing – number of nodes and elements

Nodes ElementsExisting cooling-unit model 520881 2594125

New energy-saving unit model 680601 3576040

Fig. 7. Generated mesh of new energy-saving cooling-unit model

Following Figs. 6 and 7, the air-flow simulations results with convergence level of 1×10-5 are shown in Fig. 8. They show 2D forward velocity streamlines on a cutting-plane starting from vertices with a time limit of 0.01 s. The rainbow colour range is used from gray 0.0 m/s to black 100 m/s. Simulated air-flow during the cooling phase is shown in Fig. 8: flow of fresh air in place from the cooling nozzles, sucking the surrounding air through side inlets and disposing of the warm air after cooling. Simulation results

show a 45% increase in air mass-flow at the outlet manifold compared to the existing cooling-unit.

2.5 Manufacturing Method for the Energy-Saving Cooling-Unit

The shape and internal geometry of the nozzle were defined with development and optimization of the cooling-unit. The problem remaining was the construction of this form. This is usually where things come to a standstill, as complex forms require the use of complex and expensive manufacturing procedures. In case simpler form and construction is used (lower costs) the effect of the nozzle is not optimal. This is particularly true in the case of very complex form of the cooling-unit.

Fig. 8. Simulated air-flow in the existing cooling-unit (above) and with optimized geometry design

in the new cooling-unit (below)

With the use of Rapid Prototyping/ Manufacturing Technology (RP/RM) two good



things can be combined – complex geometry of the nozzles with low manufacturing costs.

For experimental verification of effectiveness, the energy saving cooling-unit was produced by the rapid prototyping method using SLS technology (Selective Laser Sintering). As material, polyamide PA2200 in 30 micrometer granulation is used. The high-power laser (CO2 laser) sticks together fine particles of plastic powder into layers that finally form a 3D object [11]. The final product made from this material has sufficient strength, is thermally stable, and has low weight.

3 EXPERIMENTAL TESTING OF COOLING-UNIT EFFICIENCY

It is reasonable to use a thermal camera to experimentally determine the efficiency of a newly-developed energy-saving cooling-unit. With the help of a thermal imaging system the process of cooling the canister waste can be recorded and observed in detail. In order to determine the effectiveness of the cooling, the maximum temperature of the plastic, which still provides a flawless material cut, must be determined in conjunction with a reduction of compressed air consumption from the pneumatic network’s supply.

The existing system, without the optimization of nozzle geometry and without the possibility of sucking surrounding air, uses 48 m3/h of compressed air, while the temperature of the cooled material ranges between 134 and 135 °C. At higher temperatures, which may arise due to the lack of cooling efficiency, e.g. due to lower input quantities of compressed air, the plastic is insufficiently cooled and may lead to bad material cut – useless product.

The key factor regarding product quality is, therefore, the sufficiently low temperature of the plastic before cutting surpluses with minimum consumption of compressed air – whether cooling air is fully provided from a pneumatic network or the amount provided from a network is smaller, and the additional quantity is sucked from cooling-unit’s surrounding.

Temperature conditions at the given measured flow-rates of compressed air at the inlet of the cooling-unit are shown in Fig. 9 (in black

white to provide better clearness). In all cases, the temperature fields were recorded during the final-stages of cooling process, immediately before the waste cut.

Fig. 9. Comparison of temperature fields of a) existing cooling-unit at flow of 48 m3/h, b)

new cooling-unit at flow of 48 m3/h, c) existing cooling-unit at flow of 25 m3/h, d) new cooling-

unit at flow of 25 m3/h; temperature scale: 100 to 140 °C

When comparing the thermal images of the waste materials temperature fields it is fairly clear, that cooling with the new cooling-unit is much more effective than with the old unit (Figs. 9a and b). The new cooling-unit consumes (almost) only half the compressed air for equally-effective cooling as the existing one (Figs. 9a and d). The remaining amount of air involved in the cooling was sucked “free” from the surrounding.

4 DISCUSSION

The principle used in an energy-saving nozzle, as an example of a simple element, was transferred to a more complex product – an energy-saving cooling-unit with a much more complex geometry.

The efficiency of this cooling-unit is clearly visible based on the thermo-graphic measurements and comparison of different inlet flows of compressed air from the pneumatic network.

a) b)

c) d)



Despite current performance, there are many possibilities for further development of this system with regard to its greater efficiency, e.g. concentrated cooling at points with larger amount of material, cut-line aimed cooling instead of cooling the whole waste, production of a lightweight unit (honeycomb) with possible significant material savings and faster RP/RM manufacturing process and, therefore, lower product price, thus optimizing the geometry of cooling-unit for specific shapes, …, are just some of the possible further development activities for such a cooling-unit. This is reasonable only in cases where compressed air is available on the machine and is already in use.

The presented cooling of waste material in the process of plastic-blowing is just one of the many possible examples of using such cooling, which can be designed much more efficiently when taking additional measures.

5 SYMBOLS

k turbulence kinetic energy per unit massS invariant measure of the strain ratey distance to the nearest wally+ dimensionless distance from the wallβ coefficient of thermal expansionμt turbulent viscosityν kinematic viscosityρ densityσ turbulence model constantω angular velocity

6 REFERENCES

[1] Trautmann. A., Meyer, J., Herpertz, S. (2002). Rationelle Energienutzung in der Kunststoff verarbeitenden Industrie. Friedr. Vieweg&Sohn Verlagsgesellschaft, Braunschweig/Wiesbaden.

[2] deLorenzi, H.G., Nied, H.F. (1987). Blow molding and thermoforming of plastics: Finite element modelling. Computers & Structures, vol. 26, no. 1-2, p. 197-206.

[3] Bendada, A., Erchiqui, F., Kipping, A. (2005). Understanding heat transfer mechanisms during the cooling phase of blow molding using infrared thermography. NDT & E International, vol. 38, no. 6, p. 433-441.

[4] Prystay, M., Wang, H., Garcia-Rejon, A. (1996). Application of thermographic temperature measurements in injection molding and blow molding of plastics. Proc. SPIE, vol. 2766: Thermosense XVIII: An International Conference on Thermal Sensing and Imaging Diagnostic Applications, Orlando.

[5] Glen, R., Anderson, R.G. (2006). Redesigning process cooling system in the plastics industry. Proceedings of the 28th

Industrial Energy Technology Conference, New Orleans.

[6] ANSYS CFX-Solver Theory Guide (2009). Ansys Inc., USA.

[7] Biluš, I., Škerget, L., Predin, A., Hriberšek, M. (2005). Experimental and numerical analyses of the cavitational flows around a hydrofoil. Strojniški vestnik - Journal of Mechanical Engineering, vol. 51, no. 2, p. 103-118.

[8] Berg, E., Volmajer, M. (2003). Validation of a CFD model for coupled simulation of nozzle flow, primary fuel jet break-up and spray formation. Proceedings of ICES03, Salzburg, p. 1-10.

[9] Warsi, Z.U.A. (2005). Fluid Dynamics: Theoretical and Computational Approaches, 3rd Edition. CRC Press.

[10] ANSYS (2006). Innovative Turbulence Modelling: SST Model in ANSYS® CFX.Ansys Inc., USA.

[11] Drstvenšek, I., Lovrec, D., Tič, V., Burjek, A., Jelovčan, L. (2008). Implementation of rapid manufacturing techniques for fluid power products. Ventil, vol. 14, no. 5, p. 476-482.


*Corr. Author’s Address: Hacettepe University, Faculty of Engineering, 06800 Beytepe, Ankara, Turkey,[email protected] 91

The Influence of Chip Breaker Geometry on Tool Stresses in Turning

Huseyin Gurbuz1,* - Abdullah Kurt2 - Ibrahim Ciftci3, Ulvi Seker2

1 Hacettepe University, Faculty of Engineering, Turkey 2 Gazi University, Technical Education Faculty, Turkey

3 Karabuk University, Technical Education Faculty, Turkey

In this study, the influence of different chip breaker geometries on cutting forces and tool stresses developed during turning was investigated experimentally. For this purpose, turning tests in accordance with ISO 3685 were carried out on AISI 1050 steel using uncoated and coated cemented carbide cutting tools with different chip breaker geometries. The tests were carried out at different cutting parameters. The cutting forces were measured using a Kistler 9257B type dynamometer. The effect of cutting force variation on tool stresses was analysed using finite element analysis software (ANSYS). The analyses results showed that the coated tools were subjected to higher stresses than the uncoated ones. However, the stresses on the uncoated tools were found to be higher than those on the coated tools at the heavy cutting conditions. In addition, the chip breaker geometry was also found to result in variation in the stresses acting on the tools.©2011 Journal of Mechanical Engineering. All rights reserved. Keywords: metal cutting, chip breaker form, cutting forces, tool stresses, principal stresses, AISI 1050

0 INTRODUCTION

Parts manufactured by casting, forming and various shaping processes often require further processing or finishing operations to import specific characteristics, such as dimensional accuracy and surface finish, before the product is ready for use. These processes are generally classified as material-removal or cutting processes. Cutting processes remove material from the surface of the workpiece by producing chips [1]. Metal cutting is dynamic technology, involving several disciplines of science. It is continually changing in line with strategies and material developments through the manufacturing industry worldwide. On the other hand, it is also changing as a consequence of developments within the cutting tool industry. The relation between “machine tool – cutting tool – workpiece materials” should be well established. In addition, the variables called “cutting parameters (V, a, f)” should be well assessed [2].

Controlling of both the chip breaking and chip curling is the control of the chip form. Since the first use of carbide tools, many techniques have been developed to control the chip formation. The most widespread method is to employ chip breaker

and chip curler. In order to determine the optimum cross-section which the cutting tool can withstand and the ideal angles (ideal tool geometry) which ease the cutting operation, many studies have been carried out. Although the cutting edges of the cutting tools used in machining metals and their alloys are quite sharp, they are forced significantly under the stresses developed during machining. Significantly high stress is required in order to break the chip. With the aid of this high stress, the chip breaker easily generates a bending torque [2] to [6].

Karahasan determined the characteristics of the optimum chip breaker form, which leads to acceptable chip geometry by examining the types of chip breakers and technological developments [7]. Mesquita and Barata Marques developed a method which predicts the cutting forces beforehand in their study on the influence of chip breaker geometry on cutting forces. In this developed model, they took into consideration the influences of chipping and penetration for the parallel groove type chip breaker. This technique is based on the formation of chip breaker geometry and calculation of effective side relief angle. Chipping effect, dynamic area effect and cutting forces were determined by experimental studies.


92 Gurbuz, H. - Kurt, A. - Ciftci, I. - Seker, U.

The model which they proposed was applied to the machining of martensitic stainless steels by coated carbide tools. Finally, they compared the experimentally measured and theoretically predicted values [3]. Fang compared the chip breaking performance of an asymmetric grove type (AGT) to that of symmetric type (SGT). In this study, two mathematical models were developed using multiple linear regression model to predict chip breaking ability of the new type chip breakers. The experimental results showed that replacement of AGT by SGT is practical when the depth of cut, feed rate and chip breaking performances were taken into consideration. The theoretical predictions were obtained depending on the experimental results at the given cutting conditions [8].

Kim and Kweun modelled the formation of chip flow using various cutting tools with different geometries. This study was centred on the chip breaker design and machining of medium carbon steels using cutting tool with chip breaker [9]. Kramar and Kopač investigated the application of high pressure cooling (HPC) assistance in the rough turning of two different hard-to-machine materials, namely hard-chromed and surface hardened C45E and Inconel 718 with coated carbide tools. The capabilities of different hard turning procedures were compared by means of chip breakability, cooling efficiency, temperatures in cutting zone, tool wear and cutting forces [10]. Mahashar and Murugan performed an experimental work which deals with the influence of two design parameters, width of chip breaker and angle of chip breaker of a clamped on chip breaker on effective chip breaking [11]. Karabulut and Gullu designed a chip breaker and experimental cutting of Inconel 718 was conducted with the designed chip breaker. Their experimental results showed that the designed chip breaker can break long chips at any cutting condition and acceptable surface finish can be achieved [12]. Arrazola et al. compared two AISI 4140 steels with different machinability ratings and three types of tools: (i) uncoated with 0° rake angle, (ii) coated with -6° rake angle and (iii) coated with chip breaker. A control volume approach was used to estimate the energy partition from thermal images and energy outflow was compared to direct measurement of

the cutting power. This provided a new physical tool for examining machinability, tool wear and subsurface damage [13]. Kim et al. evaluated the performance of commercial chip breakers using a neural network that was trained through a back propagation algorithm. Important form elements (depth of cut, land, breadth, and radius) that directly influenced the chip formation were chosen among commercial chip breakers, and were used as input values of the neural network. As a result, they developed the performance evaluation method and applied it to commercial tools, which resulted in excellent performance [14].

Formation of chip breaker grooves on the rake faces of indexable insert type cutting tools is one of the effective methods for breaking chip. The influence of chip breaker geometry on the chip breaking performance was tackled by many researchers in the past [15] to [21]. This study concentrates on the influence of different chip breaker geometries on cutting forces and tool stresses developed during turning.

1 EXPERIMENTAL PROCEDURE

The cutting tools used were cemented carbide and were suitable for the experimental conditions defined in ISO 3685. They were in the form of SNMG120408R while the tool holder was in the form of PSBNR252512. This tool holder provided a 75° side cutting edge angle. The cutting tools were produced by Mitsubishi Carbide with MA, SA, MS, GH and standard (STD) types chip breaker forms. All these tools were coated. In addition, uncoated MS and STD types were also used. The tools had UC6010 and UT120T Mitsubishi Carbide designations equivalent to ISO P15 and P30. Fig. 1 gives the pictures of the cutting tools. The uncoated cutting tools in the experimental studies, was shown by letter “U”.

The tests were carried out on JOHNFORD T35 CNC turning centre. The workpiece material was AISI 1050 (DIN 1.1210) carbon steel widely used in manufacturing industry. The cutting forces developed during turning were measured using a Kistler 9257B type dynamometer. The dynamometer was connected to a computer and a total of 210 turning tests (30 tests for each cutting tool) were conducted without a coolant. The


93The Influence of Chip Breaker Geometry on Tool Stresses in Turning

surface-to-surface contact element CONTA174 for the insert and 3-D target segment TARGE170 for the tool holder). The behaviour of the contact surface between the cutting tool base and cutting tool base plate was bonded in all directions. When forming the contact pair between two edges of the cutting tool in contact with the tool holder, the behaviour of the contact surfaces was applied as “standard”. The friction coefficient between the contact surfaces was selected as “0.1” and the starting penetration was selected as “0” since there was no penetration between the contact surfaces. The target was selected as the cutting tool while the contact was selected as the tool holder. In the analysis, “P” method was used to fix the cutting tool to the tool holder in accordance with ISO 1832 (the tool holder was in PSBNR form). In this method, a pin is used to fix the cutting tool. In “P” method, the squeezing force was found to be around 1040 N from the previous studies [25] to [27]. This force was applied as surface pressure to the squeezing area and then transferred to the elements.

In parallel to the literature [26], the cutting forces were applied to the nodes in the tool–chip contact areas as follows: the primary cutting force was applied as triangular surface load throughout the tool–chip contact length. The feed and the passive forces were applied to the nodes in the contact areas in the feed direction of the cutting tool and the workpiece as the nodal force. In order to reduce the calculation time in the analysis, some assumptions were performed as follows: the weight of the tool holder and the insert were neglected. The inserts used in the analysis were new and unused (sharp). The vibrations and temperatures occurred in the metal cutting were also neglected in the analysis. The static analysis solution method was used. As a boundary condition for constraint, the degree of freedom of the nodes (nodal displacements) in the area to mount the tool holder to the dynamometer, on the tool holder mounting length, was selected as zero in all directions (nodal displacements = 0).

Maximum principal stress (S1) and minimum principal stress (S3) were used to investigate the stresses on the cutting tool according to the cutting parameter variations.

cutting parameters used in the experiments are shown in Table 1.

Fig.1. The cutting tools used for the tests and their chip breaker forms

Table 1. Test parameters

Cutting speed V [m/min] 150, 200, 250, 300, 350Feed rate, f [mm/rev] 0.15, 0.25, 0.35Depth of cut, a [mm] 1.6, 2.5

The tool holder and cemented carbide tools were modelled using CATIA V5R15 software for analysis purposes and recorded as CATIA model. The models were then opened in ANSYS with model extension. The material models for the insert and tool holder used in the analyses are shown in Table 2.

Table 2. Material properties of the cutting tools

Cutting tools

Modulus of elasticityE [GPa]

Poisson’s ratio υ Ref.

P15 530 0.23 [22]P30 558.6 0.22 [23]

Tool holder 210.7 0.28 [24]

SOLID92, three-dimensional (3-D) 10-node tetrahedral structural solid with a quadratic displacement behaviour well suited for modelling irregular meshes (such as those produced from various CAD/CAM systems) was used as the element type for the cutting tools in the FEM model. The mesh density was selected very densely (smartsize = 3) in the tool-chip contact areas. However, it was selected sparsely (smartsize = 5) in other parts of the cutting tool. The contact pairs were also applied between the cutting tool and the seating surface of the tool holder in parallel to the literature [25] (3-D eight-node



2 EXPERIMENTAL RESULTS AND DISCUSSIONS

When the graphs in Fig. 2 are examined, it is seen that main cutting force (FC) increases with increasing depth of cut and feed rate and decreases with increasing cutting speed for all the chip breaker types.

This situation is in agreement with the literature [22] and [28]. Decreasing cutting forces can be explained by increasing energy spent with increasing cutting speed and almost all of this energy is transformed into temperature. This temperature, in turn, eases the chip formation during machining. According to Kienzle’s “FC = A × ks” equation, cutting forces increase

depending on increasing chip cross-section (A) which is the product of feed rate and depth of cut [2]. When both uncoated and coated tools having the same type of chip breaker are compared, no significant difference in the cutting forces was observed at low cutting speeds. However, when cutting speed was increased to 300 and 350 m/min, the uncoated tool with the STD type chip breaker resulted in higher FC forces than the coated one with the same type of chip breaker (Fig. 2). A similar finding was also observed for MS type breaker. The main cutting forces (FC) obtained with the uncoated MS chip breaker type were higher than those obtained with the coated MS chip breaker type especially at 350 m/min. This situation can be attributed to faster wear of

a) a = 1.6 mm and f = 0.15 mm/rev

Fig. 2. Variation of main cutting forces (FC) depending on chip breaker form

b) a = 2.5 mm and f = 0.15 mm/rev

c) a = 1.6 mm and f = 0.25 mm/rev d) a = 2.5 mm and f = 0.25 mm/rev

e) a = 1.6 mm and f = 0.35 mm/rev f) a = 2.5 mm and f = 0.35 mm/rev



a) a = 1.6 mm and V = 150 m/min b) a = 2.5 mm and V = 150 m/min

c) a = 1.6 mm and V = 200 m/min d) a = 2.5 mm and V = 200 m/min

e) a = 1.6 mm and V = 250 m/min f) a = 2.5 mm and V = 250 m/min

g) a = 1.6 mm and V = 300 m/min h) a = 2.5 mm and V = 300 m/min

i) a = 1.6 mm and V = 350 m/min j) a = 2.5 mm and V = 350 m/min

Fig. 3. Variation of maximum principal stress (S1) depending on chip breaker form

S 1[G

Pa]

S 1[G

Pa]

S 1[G

Pa]

S 1[G

Pa]

S 1[G

Pa]

S 1[G

Pa]

S 1[G

Pa]

S 1[G

Pa]

S 1[G

Pa]

S 1[G

Pa]



the uncoated tools than the coated ones at high cutting speeds. For all the chip breaker types, increasing cutting speed generally decreases the cutting forces. However, a slight increase is observed when the cutting speed is raised to 350 m/min. This increase can be explained by the higher cutting speed which is above the range suggested by the manufacturer for this cutting tool. Generally, the highest main cutting forces were obtained for the tools with the most complex chip breaker type while the lowest main cutting forces were obtained for the tools having the least complex chip breaker type.

It is seen from the graphs in Fig. 3 that the principal stresses obtained are in the following order from the highest to the lowest: coated SA/MS type chip breaker, uncoated MS type chip breaker, coated GH/STD type chip breaker, uncoated STD type chip breaker and coated MA type chip breaker at 1.6 mm depth of cut. On the other hand, at 2.5 mm depth of cut, the principle stresses (S1) from the highest to the lowest are obtained in the following order: coated SA – MA – MS, uncoated MS, coated GH – STD and uncoated STD type chip breakers. At 1.6 and 2.5 mm depth of cut values, the reason for this order can be explained depending on the cutting force (FC) values obtained by the chip breaker forms. As the forces increase, S1 stresses are considered to increase. The highest stresses were observed for the most complex chip breaker forms while the lowest stresses were observed for the least complex chip breaker forms. When the maximum principal stress (S1) graphs are examined, S1 stress is seen to be very high when the depth of cut is increased from 1.6 mm to 2.5 mm for MA type chip breaker at all the feed rates and cutting speeds. This can be explained by the depth of cut and cutting speed values which are outside ranges suggested by the cutting tool manufacturer for MA type chip breaker. Generally, increasing feed rate increases the maximum principal stresses S1 for all the cutting tools while increasing cutting speed and depth of cut decreases S1 stresses. It is considered that increasing feed rate and depth of cut increased the tool-chip contact area and chip cross-section and increasing cutting speed decreased the cutting forces and these, in turn, reduced S1 stresses. When the maximum principal stress S1 graphs are examined, it is seen that the

coated (MS, STD) chip breaker forms result in higher stresses than the uncoated (MS, STD) chip breaker forms. However, the uncoated (MS, STD) chip breaker forms result in lower stresses than the coated (MS, STD) chip breaker forms at 350 m/min cutting speed and 0.25 to 0.35 mm/rev feed rates. The reason for this can be explained depending on the cutting force (FC) values obtained with these chip breaker forms at these cutting conditions. As the uncoated chip breaker forms result in higher forces than the coated chip breaker forms, increasing forces is considered to increase S1 stresses.

When all the graphs in Fig. 4 are examined, it is seen that S3 stresses increase with increasing feed rate for all the chip breaker forms and decrease with increasing cutting speed and depth of cut. It is considered that increasing feed rate and depth of cut increased the tool-chip contact area and chip cross-section and increasing cutting speed decreased the cutting forces and these, in turn, reduced S3 stresses. When the least principal stress (S3) graphs are examined, the highest stresses are seen for the coated SA type chip breaker while the minimum principal stresses (S3) are seen for the uncoated STD type chip breaker generally at 1.6 mm depth of cut. These sorts of stress results can be explained by the number of node at the tool-chip area for the chip breaker forms. According to this, increasing the node number increases the stresses while decreasing the node number decreases the stresses. At 1.6 mm depth of cut, the highest S3 stresses are caused by the coated SA type chip breaker while the coated MA type breaker results in the highest stresses when the depth of cut is increased to 2.5 mm. It is considered that the stresses increased due to the chip breaker geometry for this cutting tool form and that such high stresses were due to the used depth of cut which was outside the range suggested for MA type chip breaker by the manufacturer. When the coated (MS, STD) and uncoated (MS, STD) chip breaker forms are compared, it is seen that the coated chip breaker forms generally result in higher S3 stresses than the uncoated ones. On the other hand, the uncoated chip breaker forms result in higher stresses than the coated ones only at 350 m/min cutting speed and 0.25 to 0.35 mm/rev feed rates. The reason for this can be explained depending on the cutting force (FC)



a) a = 1.6 mm and V = 150 m/min b) a = 2.5 mm and V = 150 m/min

c) a = 1.6 mm and V = 200 m/min d) a = 2.5 mm and V = 200 m/min

e) a = 1.6 mm and V = 250 m/min f) a = 2.5 mm and V = 250 m/min

g) a = 1.6 mm and V = 300 m/min h) a = 2.5 mm and V = 300 m/min

i) a = 1.6 mm and V = 350 m/min j) a = 2.5 mm and V = 350 m/min

Fig. 4. Variation of minimum principal stress (S3) depending on chip breaker form

S 3[G

Pa]

S 3[G

Pa]

S 3[G

Pa]

S 3[G

Pa]

S 3[G

Pa]

S 3[G

Pa]

S 3[G

Pa]

S 3[G

Pa]

S 3[G

Pa]

S 3[G

Pa]



values obtained with these chip breaker forms and it is considered that increasing forces increase the stresses.

3 CONCLUSIONS

Increasing cutting speed was generally found to decrease the main cutting force (FC) for all the chip breaker forms up to 300 m/min cutting speed beyond which it increased. At all the cutting conditions, increases in feed rate and depth of cut increased the main cutting force (FC) for all the chip breaker forms. The highest FC cutting forces were generally obtained for SA type chip breaker and the complex chip breaker geometry was determined to result in these higher cutting forces. Generally, increasing feed rate was found to result in increases in the maximum principal stresses (S1) and minimum principal stresses (S3) while S1, S3 stresses decreased depending on the cutting speed and depth of cut for all the cutting tool forms. The analysis results showed that the highest values of maximum principal stresses (S1) and minimum principal stresses (S3) were generally obtained for the most complex coated SA and MA type chip breaker forms. On the other hand, the lowest values for these stresses were obtained for the uncoated STD type chip breaker form. When (S1, S3) graphs are examined, it is seen that the stresses produced by MA type chip breaker were raised significantly at all the feed rates and cutting speeds when the depth of cut was increased to 2.5 mm from 1.6 mm. This can be explained by the depth of cut and cutting speed values which are outside the ranges suggested by the cutting tool manufacturer for MA type chip breaker.

4 ACKNOWLEDGEMENTS

The authors would like to thank Gazi University (Project Code: 07/2002–13) for providing financial support for the project.

5 REFERENCES

[1] Kalpakjiyan, S., Schmid, S.R. (2003). Manufacturing Processes for Engineering Materials (4th Ed.). Prentice Hall, p. 404-405.

[2] Seker, U. (1997). Lecture notes in Tool design. Gazi University, Technical Education Faculty, Ankara, p. 5-11, 33-44, 47-72. (in Turkish)

[3] Mesquita, R.M.D., Barata Marques, M.J.M. (1992). Effect of chip-breaker geometries on cutting forces. J. Mater. Pro. Tech., vol. 31, p. 317-325.

[4] Kaldor, S., Ber, A., Lenz, E. (1979). On the mechanism of chip breaking. Trans. ASME, J. Eng. Ind., vol. 101, p. 241.

[5] Boothroyd, G. (1975). Fundamentals of metal machining and machine tools. Mc Graw-Hill, New York, p. 61-88.

[6] Shaw, M.C. (1984). Metal Cutting Principles. Oxford Science Publications, p. 545-555.

[7] Karahasan, Z.O. (1995). The Influences of tool geometry and chip breaker form of cutting on tool performance. MSc. Thesis, Yıldız Technical University, Science and Technology, Istanbul, p.153-161. (in Turkish)

[8] Fang, N. (1998). Influence of the geometrical parameters of the chip groove on chip breaking performance using new-style chip formers. J. Mater. Pro. Tech., vol. 74, p. 268-275.

[9] Kim, J.D., Kweun, O.B. (1997). A chip-breaking system for mild steel in turning. Int. J. Mach. Tools and Manuf., vol. 37, p. 607-617.

[10] Kramar, D., Kopač, J. (2009). High Pressure Cooling in the Machining of Hard-to-Machine Materials. Strojniški vestnik - Journal of Mechanical Engineering, vol. 55, no. 11, p. 685-694.

[11] Mahashar, A.J., Murugan, M. (2009). Influence of chip breaker location and angle on chip form in turning low carbon steel. International journal of machining and machinability of materials A., vol. 5, no. 4, p. 452-475.

[12] Karabulut, S., Gullu, A. (2009). Dynamic chip breaker design for nickel-base, inconel 718, alloy with coated carbide tools using negative angle tool holder, diffusion and defect data. Solid state data. Part B, Solid state phenomena A, vol. 147-149, p. 758-763.



[13] Arrazola, P.J., Arriola, I., Davies, M.A. (2009). Analysis of the influence of tool type, coatings, and machinability on the thermal fields in orthogonal machining of AISI 4140 steels. CIRP annals A., vol. 58, no. 1, p. 85-88.

[14] Kim, H.G., Sim, J.H., Kweon, H.J. (2009). Performance evaluation of chip breaker utilizing neural network. Journal of materials processing technology A, vol. 209, no. 2, p. 647-656.

[15] Henriksen, E.K. (1954). Balanced design will fit the chip breaker to the job. Am. Mach., vol. 88, p. 118-124.

[16] Cook, N.H., Jehaveri, P. (1963). The mechanism of chip curl and its importance in metal cutting. Trans. ASME 85 (B), p. 374-380.

[17] Spaans, C., Geel, P.F.H.J. (1966). Breaking mechanisms in cutting with a chip breaker, Ann. CIRP, vol. 18, p. 87-92.

[18] Nakayama, K. (1984). Chip control in metal cutting. Bull. Jpn. Soc. Precis. Eng., vol. 18, p. 97-103.

[19] Worthington, B., Redford, A.H. (1973). Chip curl and the action of groove type chip former. Int. J. Mach. Tool Des. Res., vol. 13, p. 257-270.

[20] Worthington, B. (1976). The operation and performance of a groove type chip former device. Int. J. Prod. Res., vol. 14, p. 529-558.

[21] Worthington, B., Rahman, M.H. (1979). Predicting breaking with groove type

breaker. Int. J. Mach. Tool Des. Res., vol. 19, p. 121-132.

[22] Wood, R.J.K., Wheeler, D.W., Lejeau, D.C., Mellor, B.G. (1999). Sand erosion performance of CVD boron carbide coated tungsten carbide. Wear, vol. 233-235, p. 134-150.

[23] Shatla, M., Kerk, C., Altan, T. (2001). Process modelling in machining. Part II: validation and applications of the determined flow stress data. Int. J. Mach. Tools and Manuf., vol. 41, p. 1659-1680.

[24] Lin, Z.C., Lo, S.P. (1998). A study of determination of the machined workpiece and tool under different low cutting velocities with an elastic tool. Int. J. Mech. Sci., vol. 40, p. 663-681.

[25] Wikgren, T. (2001). Analysis of contact between insert and tip seat. MSc. Thesis, Lulea Universty of Technolgy, Institutionen för Maskinteknik Avdelnigen för Datorstödd maskinkonstruktion, p. 13-15.

[26] Kurt, A. (2006). Experimentally investigation and mathematical modelling of the cutting forces and mechanical stresses on metal cutting. PhD. Thesis, Gazi University Institute of Science and Technology, Ankara, p. 100-106.

[27] Tjernström, E. (1996). Clamping forces with RC-clamping mechanism. AB Sandvik Coromant, Sandviken, p. 105. (in Turkish)

[28] Choudhury, I.A., El-Baridie, M.A. (1998). Machining nickel base super alloys: Inconel 718. Proc. the Institution of Mech. Eng., vol. 212, p. 195-206.


*Corr. Author’s Address: University of Belgrade, Faculty of Mechanical Engineering, Kraljice Marije 16, 11120 Belgrade, Serbia, [email protected]

System Approach to Vehicle Suspension System Control in CAE Environment

Vladimir Popovic1,* - Branko Vasic1 - Milos Petrovic2 - Sasa Mitic1

1 University of Belgrade, Faculty of Mechanical Engineering, Serbia 2 Institute for Research and Design in Commerce & Industry, Serbia

In recent years, motor vehicles industry has shown a tendency of replacing electromechanical components by mechatronic systems with intelligent and autonomous properties. The integration of hardware components and implementation of advance control function characterize this replacement. In this paper, we have applied the system approach and system engineering methods in the initial phase of vehicle active suspension development. An emphasis has been placed upon the interrelations between computer-aided simulation and other elements of the development process. The benefits of application of active suspension simulation are numerous: reduction of time to market, the new and improved functions of mechatronic components/devices, as well as the increased system reliability. In suspension model development, we used CAD/CAE tools, as well as the multipurpose simulation programs. For simulation, we used the one-quarter vehicle model. The modelling was carried out through the state-space equation, after which we designed the controller for our system. During this, we considered only the digital systems of automatic regulation.©2011 Journal of Mechanical Engineering. All rights reserved.Keywords: active suspension system, system approach, simulation, control system, PID controller

0 INTRODUCTION

Considerable effort aiming at reducing the cost, the ever increasing expectations of users when it comes to reliability, the greater complexity of modern technical systems, and other requests, bring us to the conclusion that the approach based on system engineering principles, remains the only way to stay competitive on the market. Intensifying the integration of electronic components into areas which used to be strictly mechanical and the degree of change within technologies, all result in increased demands for dealing with the problems on the basis of what is offered by system engineering. In recent years, motor vehicles industry has started developing an unusual trend - the mechatronic systems with intelligent and autonomous abilities. Development of integrated mechatronic systems should, in its own way, play the key role in motor vehicles industry [1]. Mechatronics is being more and more accepted as the design methodology for all motor vehicle systems. Verification of proper functionality through simulation can improve reliability and reduce component design time. Replacement of simple electro-mechanical

components by “intelligent” mechatronic systems is characterised by two aspects: integration of hardware components and implementation of advanced control functions (functional and algorithm integration). Hence, mechanical components are operated through basic sensor feedback of low level and intelligent processing of advanced level information.

The goal in active suspension control research is to improve the ride performance, generally quantified by sprung mass acceleration, while maintaining an acceptable level of suspension stroke and tyre deflection as packaging and handling measures. Ride comfort, road holding ability and suspension deflection are the three main performance criteria in any vehicle suspension design [2]. Finally, all control concepts aim at introducing additional forces to the suspension system to reduce roll, pitch, and stroke movements, as well as body and wheel vibrations. Therefore, the basic control approach is similar for all actuation systems. Then, the physical structure of the considered system determines the further controller design and also directly limits the achievable system’s performance [3].


101System Approach to Vehicle Suspension System Control in CAE Environment

1 DESIGN DEVELOPMENT PROCESS

The design procedure, which is to be carried out in the case of mechatronic systems, is very demanding - it is necessary that system engineering, within the field of mechanics, electronics and computer science, forms a completely integrated system. Hence, intelligent testing technologies, supported by CAE (Computer Aided Engineering), which provide numerical simulation models, are to be employed during component development and their qualification. An emphasis should be placed upon the interaction between computer aided system simulation and experimental testing techniques through intelligent information processing. CAE is a technology that enables computer analysis of the design, created within CAD (Computer Aided Design) technology. By the application of CAE technology it is now possible to have a much better linking of design, testing and design improvement, which are the development phases that have, so far, been almost separate. Fig. 1 shows a diagram of design development process [4]. After theoretical modelling and experimental system identification, the future design steps are control system analysis and model based controller design. The demands for efficient optimisation and testing include:• Software development environment with

accurate, firm connections among different CAE tools, such as CAD, FEM and MBS programs, and CACE (Computer Aided Control Engineering) tools. An example of such an environment is shown in Fig. 2 [5]. On the basis of this diagram, we can determine the role of Matlab (and Simulink) in the framework of the complete process of modelling and simulation of the new technical system.

• Experimental, high performance platform which, in combination with software development environment, completely automates the implementation process from off-line simulation to engaging complex controllers with real time operation.

Interactive testing of mechatronic components, using advanced hardware simulators in feedback, plays an important role. The idea is to build mechatronic components into a virtual

environment in which vehicle movement, external load and the surrounding mechanical systems, are simulated in real time. A complex model of vertical vehicle dynamics has been developed using CAE tools and multi-purpose simulation programs. On the basis of reduced models, a multivariable, powerful control system should be designed for active suspension control. Fig. 3 [4], [5] shows the corresponding ¼ vehicle model with a passive spring that bears static load, which is to be used for suspension system simulation.

Fig. 1. Design development process

Fig. 2. Software development environment with connections among different CAE tools

2 ACTIVE SUSPENSION SYSTEM MODELLING

Various approaches have been proposed to improve the performance of active suspension designs, such as linear optimal control, fuzzy logic and neural network control, adaptive control, H∞ control, nonlinear control, LQG control, skyhook control algorithm, etc [2] to [8]. Also, many approaches are presented to deal with the multi-objective requirement of vehicle suspensions. In particular, H∞ control of active suspensions is intensively discussed in the context of robustness and disturbance attenuation [2]. An interesting solution is a fuzzy sliding mode controller to


102 Popovic, V.- Vasic, B. - Petrovic, M. - Mitic, S.

of the model. Also, the step function that we used for simulation has certain limitations since if the tyre of the vehicle is excited by the step function, the tyre will jump and the contact with the driving surface will be lost. Furthermore, when compressed, damping of the suspension damper is many times smaller than damping when the damper is extended. These limitations will be further discussed in the conclusion. The data that we used during the simulation refer to the bus. The simulation was performed using Matlab interactive environment. The designations in Fig. 3 have the following meanings [4] and [5]:• body mass (m1) = 2250 kg;• suspension mass (m2) = 290 kg;• spring constant of suspension system

(k1) = 72000 N/m;• spring constant of wheel and tyre

(k2) = 450000 N/m;• damping constant of suspension system

(b1) = 315 Ns/m;• damping constant of wheel and tyre

(b2) = 13500 Ns/m;• control force (Fa) = force from the controller

we are going to design.When the vehicle is experiencing any

road disturbance, the body should not have large oscillations, and the oscillations should dissipate quickly. This, at the same time, is our principal task. Since the distance x1-Z is very difficult to measure, and the deformation of the tyre x2-Z is negligible, we will use the distance x1-x2 instead of x1-Z as the output in our problem. The road disturbance Z will be simulated by a step input. This step could represent the vehicle coming out of a pothole. We wish to design a feedback controller so that the output x1-x2 has an overshoot less than 5% and a settling time shorter than 5 sec. Dynamic properties of the system can, in time range, be most suitably defined by the values of parametres that determine the system response (overshoot and settling time) [17].

2.1 Model of the System in State-Space

The assessment of the quality of automatic regulation of a system behaviour essentially boils down to estimating the error between a predetermined value and the real value of the controlled variable. The knowing of this error at

control an active suspension system and evaluate its control performance [9] to [12]. The fuzzy sliding mode controller employed the error of the sprung mass position and the error change to establish a sliding surface, and then introduced the sliding surface and the change of the sliding surface as input variables of a traditional fuzzy controller in controlling the suspension system [13]. One of the possible solutions is vibration control of a vehicle active suspension system using a new robust neural network control system [14]. It consists of a robust feedback controller and feedforward neural network predictive controller.

Fig. 3. 1/4 vehicle model

In the design of the suspension system, the following is used: ¼ vehicle model [2] to [8], [11] and [13], ½ vehicle model [15] and full vehicle model [14] and [16]. We used ¼ vehicle model (Fig. 3) to simplify the problem to a one-dimensional spring-damper system. The vehicle represents a complex oscillatory system with a great number of freedom degrees. Vehicle oscillations are caused mainly by road disturbance. In reviewing vehicle oscillations we adopted the following starting points:• the vehicle is in the rectilinear motion at a

constant speed;• the wheels are always in contact with the road

and that is a one-spot contact;• road disturbance is the same on the left and

the right wheel and the car is symmetrical relative to the longitudinal axis;

• mass distribution coefficient is approximately 1.

Although the real characteristics of the suspension system (k1, b1) are non-linear, we adopted constant values for them in this paper which, to a certain extent, enable the linearization



any point would give complete information on the features of the observed system. Due to the diversity of the laws of the system input change, which might occur in the normal work regime, such an approach, based on estimating the current values of error, is inappropriate speaking from the aspect of practice. Therefore, it is preferable to make an estimation of the relevant system characteristics on the basis of the features it manifests while being disturbed by the typical input signals [17]. On the basis of Fig. 3 and Newton’s Law, the following dynamic equations, which represent the mathematical model of the dynamic system [4] and [5] are obtained:

m x b x x k x x Fa1 1 1 1 2 1 1 2 = +- - - -( ) ( ) (1)

m x b x x k x xb Z x k Z x Fa

2 2 1 1 2 1 1 2

2 2 2 2

= + +

+ +

( ) ( )

( ) ( ) .

- -

- - -(2)

When addressing new ways for system analysis and synthesis that avoid the problem of solving algebraic and differential equations, the system analysis and synthesis in state-space mainly come to mind. Representation of the system in the form of state-space equation is much easily derived from differential equations than by using Laplace transformations. To be a valid state-space representation, the derivative of all states must be in terms of inputs and the states themselves. Now, let us choose the states that we shall be using. Firstly, let us divide the Eqs. (1) and (2) by m1 and m2, respectively and introduce the substitute Y1 = x1-x2. Note that Z appears in the Eq. for 2x :

x bm

Y km

Y Fm

a1

1

11

1

11

1

= +- - , (3)

x bm

Y km

Y bm

Z x

km

Z x Fm

a

21

21

1

21

2

22

2

22

2

= + + +

+

( )

( ) .

-

- -(4)

The first state-space equation will be x1. Since no derivatives of the input appear in the equation for 1x , we choose 1x for the second state. Then, we choose the third state as the difference between x1 and x2. After doing some algebra, we will determine what the fourth state

should be. Therefore, we subtract Eq. (4) from Eq. (3) to get an expression for 1Y :

x x Y bm

bm

Y km

km

Y

bm

Z x

1 2 11

1

1

21

1

1

1

21

2

22

- - - -

- -

= = + +( ) ( )

( ) -- -km

Z x Fm ma

2

22

1 2

1 1( ) ( ).+ +(5)

Since we cannot use second derivatives in the state-space representation, we integrate this Eq. to get Y1 :

Y bm

bm

Y bm

Z x

km

km

Y km

Z x

11

1

1

21

2

22

1

1

1

21

2

22

= + +

+ +∫

- - -

- - -

( ) ( )

( ( ) ( ) ++

+ +Fm m

dta ( )) .1 1

1 2

(6)

No derivatives of the input appear in this equation, and Y1 is expressed in terms of states and inputs only, except for the integral. Let us call the integral Y2. Assuming that x2 = x1 - Y1, from the Eq. (6) we get the state-space equation for Y1:

Y bm

bm

Y bm

Z x Y Y11

1

1

21

2

21 1 2= + + +- - -( ) ( ) . (7)

Then we shall substitute the derivative of Y1 into the Eq. (3) of the derivative of x1:

x b bm m

x bm

bm

bm

bm

km

Y

b bm m

Z

11 2

1 21

1

1

1

1

1

2

2

2

1

11

1 2

1 2

= + + + +

+ +

- -( ( ) )

FFm

bm

Ya

1

1

12- .

(8)

Fig. 4. Open-loop response for the unit step actuated force



The state variables are x1, x1 , Y1 and Y2. The matrix from the above Eq. is:

xxYY

b bm m

bm

bm

bm

bm1

1

1

2

1 2

1 2

1

1

1

1

1

2

2

0 1 0 0

0

=

− + +(22

1

1

1

1

2

2

1

1

1

2

2

2

2

2

1

1

1

2

2

2

0 1

0

)

( )

( )

− −

− + +

− + +

km

bm

bm

bm

bm

bm

km

km

km

km

00

0 01

1

1

1

2

1

1 2

+

+

*

xxYY

mb b

m

11 2

2

2

1 2

2

2

0

1 1

mbm

m mkm

FZ

a

−

+ −

,

(9)

Y

xxYY

FZ

a= [ ]

+ [ ]

0 0 1 0 0 0

1

1

1

2

. (10)

We can put the above state-space Eqs. (9) and (10) into Matlab by defining the four matrices of the standard state-space equation: X AX BZ= + ; Y = CX + DZ. Thus, a new m-file, formed on the basis of state-space equation has been created. By adding ‘step(A,B,C,D,1)’ command into the m-file and its activating, in Matlab command window an open-loop response for the unit step actuated force is obtained. Generally, new functions to the Matlab vocabulary are added by expressing them in terms of existing functions. The existing commands and functions that compose the new function reside in a text file called an m-file [18]. M-files can be either scripts or functions. Scripts are simply files containing a sequence of Matlab statements. Functions make use of their own local variables and accept input arguments. The name of an m-file begins with an alphabetic character and has a filename extension of “.m”.

Fig. 4 shows that the system is underdumped. People sitting on the bus will feel a small amount of oscillation and the steady-state error is about 0.01 mm. However, the bus needs unacceptably long time to reach the steady state the settling time is rather long.The solution to this problem lies in including a feedback controller into the block diagram of the system. By the term controller we also imply

the controller and the actuator. By adding the command ‘step(A,0.1*B,C,D,2)’ into the m-file the open-loop response to 0.1 m step disturbance is obtained.

Fig. 5. Open-loop response to 0.1 m step disturbance

Fig. 5 shows that, when the bus passes a 10 cm high bump on the road, the bus body will oscillate for an unacceptably long time (≈ 50sec), and with a much larger amplitude than the initial impact. The big overshoot and the slow settling time will cause damage to the suspension system. As already stated above, the solution to this problem is to add a feedback controller into the system to improve performance. The block diagram of the thus obtained closed-loop system is presented in Fig. 6.

Fig. 6. Closed-loop system block diagram

3 SYNTHESIS OF ACTIVE SUSPENSION DIGITAL SYSTEM

Apart from the PID algorithm, which was used for controlling an active vehicle suspension system, many modern solutions for control systems and actuators themselves are found in the literature:• H∞ control with actuator time delay [2];



• Impedance control for the vehicle suspension system by electro hydraulic actuator – the electro hydraulic actuator comprises a servo-valve and a hydraulic cylinder [6];

• Hybrid control technique applied to a vehicle active suspension system using skyhook and adaptive neuro active force control [8]. The overall control system essentially comprises four feedback control loops; the innermost proportional-integral control loop for the force tracking of the pneumatic actuator, the intermediate skyhook and active force control control loops for the compensation of the disturbances and the outermost PID control loop for the computation of the optimum target/ commanded force. Pneumatic actuators have the advantage of low cost, a high power to weight ratio, ease of maintenance and a readily available and cheap power source;

• Different kinds of rotational actuators, electric levelling actuators, electromechanical actuators and electric damper actuators [7];

• The optimization technique used for the optimization of the controller parameters and the spring rates is based on a genetic algorithm [7]. The main advantage of this method and all other non-gradient based optimization methods is that it does not need the derivatives of the objective function, which are difficult to calculate from a numeric simulation model;

• Knowledge-based fuzzy logic controllers, variable structure controllers, backstepping controllers [16];

• Possible solutions are dampers taking advantage of the electro-rheological or magneto-rheological property of liquids. Both principles are based on the alteration of the damping medium’s viscosity depending on an applied electric or magnetic field. These systems are mainly applied with semi-active vehicle suspensions [3] and [19];

• Somewhat older solutions are hydraulic, hydro-pneumatic, electro-mechanical and pneumatic systems. Most of these systems also comprise passive elements such as conventional springs and dampers or hydro-pneumatic damping and springing components involved in the actuation system [3];

• In the integrated intelligence type implementation, actuator intelligence and actuation are combined into a single material. A piezoceramic actuator is a typical example of this type [20].

Actuators may have undesirable static and dynamic characteristics introducing many problems for the PID control loop. For instance, limit cycles caused by actuator backlash cannot be removed by adjusting PID parameters. Therefore, a nonlinearity compensation algorithm using an inverse model and a simple position controller are implemented in order to maintain the actuator characteristics to be as linear as possible within the attainable actuation range [20]. The PID controller has been most widely employed because of its simple structure and the effective use in industry [21] and [22]. Despite many advances for the PID controller, this structure has constant gain parameters and is not good for decreasing velocity control error [14]. For the purpose of our paper, the simplest conventional PID controllers are obtained through an easy discretization of analogous equivalents. By the application of z-transformation, the function of incremental PID controller discrete transfer in the following form is obtained:

D z U zE z

K T Tz

TT

zpI D( ) ( )

( )/ ( )= = +−

+ −

−

−11

111

(11)or

D z K Kz

K zp PI

D( ) ( )= +−

+ −−−

111

1 ,

where KP = K, Ki = KT / Ti and KD = KTD / T are known as P-, I- D-action factors, respectively (K is the factor of gain; TI and TD are time constants of integral and differential action).

The structure of the system with a digital PID controller is given in Fig. 7 [17]. Note that without the block with broken lines in Fig. 7a, the regulation contour contains an incremental PID controller. With this block, however, this contour becomes equivalent to the contour in Fig. 7b, which contains a positional PID regulation law. The positional type of digital law seems natural; however, the incremental kind is more readily used. Incremental algorithm must be used if it is immediately connected to the executive organ of the stepping motor type, and it is also convenient



for it works easily even in the case when the executive organ output is in border line position. Also, it is important to emphasize that there are numerous methods of on-hardware optimization and improvement of the performance of open-loop stepping motor system dynamics [23]. The principal defect of incremental algorithm is “not being able to see” the position of executive organ output. Hence, when proportional executive or integrating (but included by feedback) organ is used, the positional form of PID algorithm must be used, or the incremental form must be converted into the positional, which essentially boils down to implementation of the block with broken lines in Fig. 7a. By adopting a smart actuator technology, standard PID control techniques can be used successfully in the presence of undesirable actuator characteristics. The smart actuator could be applied to an on-line PID controller retuning using a standard pole-placement technique to counteract degraded actuator performance [20].

Fig. 7. Structure of the system with a digital PID controller; a) of incremental type, b) of positional

type

Our task is to design a digital controller, in state-space, for suspension system control. First we convert a continual into discrete model, and then use pole-placement method, as one of the possible solutions, in order to design a controller. The state-space model is given in Eqs. (9) and (10). The first step in converting the continual into its discrete equivalent is the choice of suitable

sampling time T. This is a highly important step, since road disturbance very quickly influences the output. Because the controller can “see” only the consequence of the disturbance after a complete sampling time, we shall have to choose a sampling time T, short enough, so that the (x1 - x2) output will not exceed the initial requirement of 5% in one selection time. To choose a selection time, we should carefully consider the initial part of the system response graph. Fig. 5 tells us that the output soon assumes negative values, and then starts to oscillate. We shall simulate only the beginning of this response by adjusting the time vector in 0 to 0.005 range. The response to 0.1 m step disturbance is simulated by multiplying B matrix by 0.1. Into the already formed m-file we added the command ‘step(A,.1*B,C,D,2,0:0.0001: .005)’ and got the open loop response to 0.1 m step disturbance. This graph shows that the spring with the deflection rate k1 compresses rather quickly, and that our system exceeds the initial requirement of 5 mm response to 0.1 m step disturbance after only a little more that 0.001 sec. Therefore, we adjusted T to 0.0005 sec to give the controller a chance to react. After we have chosen the sampling time, we converted the system into the discrete form. We can use Matlab to convert the above presented state-space model of the system, using A, B, C and D matrices, into a discrete state-space model (using Ad, Bd, Cd and Dd) by ‘c2dm’ command. This command normally has six arguments: four space matrices, sampling time T and the type of circuit hold. In this example we used the zero-order hold -”zoh”. Let us add the following commands into our m-file:

T = 0.0005;[Ad Bd Cd Dd] = c2dm(A,B,C,D,T,'zoh').

In this way, we get the response that represents a new discrete state-space model of the system. Also, in active control of vehicle suspension systems, the time delay of the system is another important issue that needs careful treatment to avoid poor performance or even possible instability of the closed-loop system. Unavoidable time delays may appear in the controlled channel, particularly in the digital controller as it carries out the calculations associated with complex sophisticated control law, and in sensors and actuators hardware such as



hydraulic actuators where the delays are taken by the actuators to build up the required control force [2]. Although the delay time may be short, it can nevertheless limit the control performance or even cause the instability of the system when the delay appears in the feedback loop.

Fig. 8. Closed-loop response to 0.1 m step disturbance

3.1 Adding an Integrator

The next step is adding an integrator into the system, so that the system response in stationary state is zero. We will add this integrator in series with the plant. This will result in adding of the other system states. We shall add an integrator by representing it in state-space and by using the ‘series’ command. This command takes A, B, C and D matrices of the two systems, links them in a serial connection as arguments and brings us back a new set of A, B, C and D matrices. The integrator in discrete state-space may be represented in any time T in the following way:

x k x k Tu k

y k x k T u k

( ) ( ) ( )

( ) ( ) ( ) .

+ = +

= +

1

2

and(12)

To do this, let us add the following command into the m-file:

Ai = 1; Bi = 1; Ci = T; Di = T/2;[Ada Bda Cda Dda] =

= series(Ad,Bd,Cd,Dd,Ai,Bi,Ci,Di).

Matlab will respond to us with a new set of matrices, which are the result of integrator action, with matrix dimension 5 instead of the earlier dimension 4.

Ada = − − −

1 0000 0 0 1 0000 00 1 0000 0 0005 0 00 0 0034 1 0000 0 0125 0

. .. .. . . .00001

0 0 0232 0 0 9762 0 00050 0 7652 0 0002 0 9051 0 9998

. . .

. . . .−

,

Bda =−−

0 00 00 0 00340 0 02320 0 7652

...

,

Cda = 1.0e-003 * (0.5000 0 0 0.2500 0) ,

Dda = 0 0 .

Unfortunately, the output of this equation is a new integral value. The matrix output Cda must be changed, in which case we get:

Cda=[Cd 0] Cda = 0 0 1 0 0 .

The controller structure is similar to that of the state-space controller with continual systems. We shall now use the ‘place’ command, so as to calculate the gain matrix K, which will provide the desired poles of the closed-loop regulation system. First, we should decide where to position the closed-loop poles. Since we need to determine the position of all the five closed-loop poles, we must be very selective when it comes to this problem. To be more precise, we can define the poles so that they cancel all the system zeros, and at the same time give us the desired response. Firstly, we should track all the zeros by converting digital state-space equations into the transfer function, and then by determining the square root of the numerator. We shall use the ‘ss2tf’ command which takes state-space matrices and the chosen input as arguments, and gives us the transfer function of the numerator and denominator as the output. Let us add the following code into the m-file:

[num,den]=ss2tf (Ad, Bd, Cd, Dd, 1);zeros=roots(num)

The following response is obtained:

zeros = 0.9987 + 0.0065i 0.9987 - 0.0065i -0.9929 .



We shall select these three zeros as three out of five desired poles of the closed-loop system. One of the remaining two will be selected at 0.9992, for it is the pole location after approximately 10000 sampling times (or 5 sec). The last pole will be selected with z = 0.2, for this is quick enough to be neglected. Let us add the following code into our m-file: p1=.97+.13i; p2=.97-.13i; p3=-.87; p1=zeros(1); p2=zeros(2); p3=zeros(3); p4=.9992; p5=.5;

K=place(Ada, Bda*[1;0], [p1 p2 p3 p4 p5])

Matlab gives us the following response:place: ndigits = 15

K = 1.0e+008 ** (0.0082 0.3458 0.0054 9.8881 0.0096) .

We shall use ‘dstep’ command to simulate the closed-loop response. Since multiplying the state-space vector by K in our controller, gives one signal only, it is necessary to add a series of zeros to K vector by multiplying it by [1 0]T. We shall perform the simulation by the negative value of the step disturbance -0.1 m, in order to get the positive value of the deflection for practical reasons. Let us add the following code:

yout=dstep(Ada-Bda*[1 0]'*K,-.1*Bda, Cda, -.1*Dda, 2, 10001);

t=0:.0005:5; stairs(t,yout) .

Fig. 8 shows the closed-loop response to 0.1 m step disturbance. The overshoot is less than 5 mm, and the settling time is not more than 5 sec.

4 CONCLUSION

Over the past few years, active suspension system has seen increasing application. The simulation of this system in the initial development phase is of a multiple importance: i) time is gained by not having towait for exploitation results;ii) it is cost-effective an expensive model inthe lab does not need to be designed and complex laboratory examinations need not be performed; iii) suspension systems of almost all categories and types of motor vehicles can be simulated, which would, be hard to achieve in laboratory conditions. Before the practical implementation of

the active suspension system, it is recommended to simulate the obtained analytical solutions on the computer in order to examine the range of change of regulative action and the response of the regulated variable to a typical input signal, and thus confirm the possibility of the system physical realization, bearing in mind the actuator limitations, as well as check whether other possible limitations within the object of control as a whole have been reached. The above statements by no means suggest that laboratory testing is no longer necessary, but that it can be carried out in a shorter period of time and with reduced cost [24]. The key challenge associated with active suspension and their actuators is the size, weight and energy consumption required to achieve acceptable performance. For this reason, the physical properties of the actuator should be included in the optimization problem. In practice, actuator faults are quite often the largest source of control system degradation. Suspension system modelling was performed on ¼ vehicle model, using the Matlab interactive environment and by the state-space equation. The step input is the unit step function, that is, a certain value of road disturbance. It has been concluded that after encountering any kind of obstacle, the settling time and overshoot of the vehicle, are too long, and that a controller must be introduced into the suspension system. We designed a digital controller by the pole-placement method, which makes only one of the possible solutions. The presented dynamic model is only a very rough representation of the true dynamic system, which is applicable only in the early concept design phases of the research and development process. It is expected that, in the near future, this design and the above-mentioned modifications and the necessary improvements, would be used in designing such systems in the motor vehicles industry of our country.

5 ACKNOWLEDGEMENT

This paper is a part of a project of The Ministry of Science and Technological Development of Serbia (project number TR035045 - “Scientific-Technological Support to Enhancing the Safety of Special Road and Rail Vehicles”).



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[12] Guclu, R. (2004). The fuzzy-logic control of active suspensions without suspension-gap

degeneration. Strojniški vestnik - Journal of Mechanical Engineering, vol. 50, no. 10, p. 462-468.

[13] Lin, J., Lian, R.J., Huang, C.N., Sie, W.T. (2009). Enhanced fuzzy sliding mode controller for active suspension systems. Mechatronics, vol. 19, p. 1178-1190.

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*Corr. Author’s Address: Iskra ISD-Strugarstvo d.o.o., Savska loka 4, SI-4000 Kranj, Slovenia, [email protected]

Application of Genetic Algorithm into Multicriteria Batch Manufacturing Scheduling

Aleš Slak1,* - Jože Tavčar2 - Jože Duhovnik3

1Iskra ISD-Strugarstvo d.o.o., Slovenia 2 Iskra Mehanizmi, Slovenia

3University of Ljubljana, Faculty of Mechanical Engineering, Slovenia

Technical innovations in the area of manufacturing logistics are introduced partially and thus fail to realize their full potential. In order to optimise the efficiency of turning manufacturing processes, the production planning and scheduling, cutting tools and material flow process, manufacturing capacities have been analysed. All data from production operations, quantities and the, duration of operations are now kept in the ERP system. It provided the necessary condition for the establishment of a robust planning model, which includes stock control of material and cutting tools. An update was required for the whole lifecycle of products and means of work. The article presents information and an algorithm for a dynamic scheduling model, based on a genetic algorithm. The orders on the machines are scheduled on the basis of a genetic algorithm, according to the target function criteria. The algorithm provides a satisfactory, almost ideal solution, which is good enough for implementation in practice. With the GA the machine utilization increased, throughput time was reduced and costs and delivery delays improved. The presented model of GA also allows further optimisation of manufacturing plans and the machines layout. ©2011 Journal of Mechanical Engineering. All rights reserved. Keywords: genetic algorithm, multicriteria scheduling, batch production, target function

0 INTRODUCTION

In the traditional approach, planning and scheduling are two separate and successive operations. The production planning phase mostly includes the physical aspect of planning, where a product range, production quantities, machines, tools, material and accessories are selected. The emphasis in the second, scheduling phase, is on the time aspect. The plan is the basis for determining the sequence of operations across available machines in a way that provides maximum machine utilisation and timely manufacturing. Differences between planning and scheduling criteria often lead to conflict situations or even opposition between respective goals.

Master production schedule (MPS) and capacity requirement planning (CRP) in the environment of manufacturing resources planning (MRPII) are the most frequently applied traditional methods in practice. This system is characterised by a hierarchical approach from top to bottom. Goals and restrictions at the lower level are determined by the results at the higher level. Such a planning method rarely takes account of

the scheduling problem. Separate treatment of planning and scheduling often results in plans having to be manually modified. This leads to significant rearrangement costs and supply delays. Nowadays, such planning does not provide efficient business operations as customers and the market require flexibility and quick response.

The solution for such problems is the integration of the planning and scheduling processes. The article presents a solution on a case study of planning and scheduling in the batch production. Presented is a planning model and its advantages, compared to the existing method of work. A genetic algorithm was used to optimise scheduling. The updated process planning improved the efficiency of production and reliability of supplies.

1 LITERATURE REVIEW

The change of planning was triggered by the requirement for flexibility and quick response to market and consumer needs. J. Errington [1] presented the advantages of Advanced Planning and Scheduling (APS), compared to traditional


111Application of Genetic Algorithm into Multicriteria Batch Manufacturing Scheduling

methods. Here, individual steps of the traditional approach are merged in a way that allows automatic transition. Tasič et al. [2] presented advanced scheduling methods, such as priority rules, useful also for sophisticated systems.

Computer Aided Process Planning (CAPP) has been recognised to play a key role in Computer Integrated Manufacturing (CIM) [3]. Traditionally, nearly all computer aided process planning (CAPP) supposes that manufacturing is of secondary importance and that the workshop has unlimited resources. This triggers unrealistic plans that are unworkable in the manufacturing process.

Planning and scheduling functions often have opposing goals, which makes their separate treatment less efficient. The other deficiency of separate treatment is not taking account of the dynamic manufacturing environment. The time delay between process planning and the actual beginning of the plan can cause the plan to become unworkable. Fixed plans lead to bottleneck situations. Research has shown that as many as 20 to 30% of all plans have to be modified in order to be adjusted to the dynamic manufacturing environment.

Integration between the planning and scheduling process is, therefore, vital as this is the only way to create more realistic plans and schedules. Only this will result in real CIM [4]. The integration creates a single space for solutions and provides the basis for efficient integrated solutions that are in the interests of both, planning and scheduling [5]. This contributes to an improved efficiency of manufacturing resources, shorter throughput times, scheduling times and shorter delays. The integration of planning and scheduling is much more complex and more difficult to achieve in practice. Kumar et al. [6], suggested introducing on-line scheduling in a CAPP system where real time status of the shop floor is crucial and dynamic feedback is required for scheduling.

Larsen [7] and Alting [3] arranged different approaches to integrated process planning into three types; The concept of non-linear process planning (NLPP) or flexible process planning, closed loop process planning (CLPP) and distributed process planning (DPP).

The CLPP approach involves generating plans by means of a dynamic feedback from the shop floor. It is better than NLPP since plans are generated with a view to realistic conditions in the manufacturing environment. With this method, the realistic conditions are vital and the feedback is important for the scheduling process.

DPP performs process planning simultaneously with the scheduling of manufacturing. Process planning is divided into two phases. The first phase is the initial phase where product requirements are analysed according to its shape, tools, machines etc. When all the requirements are satisfied, the second phase or final planning begins. During this phase, the required manufacturing resources and the available resources are matched. The flexibility of this approach increases if the required resources are given as alternatives. Examples of such an approach are Wang and Shen [8] and the IPPM system by Zhang [9], followed by IPPS by Huang et al. [4].

Matching individual planning models has shown that DPP is the best approach. However, it still requires high performance hardware and software. NLPP involves alternative plans, which allows better scheduling flexibility. Such a method can be introduced in enterprises without the need for their reorganisation, compared to DPP and CLPP that would require reorganisation of the planning and scheduling departments. This is due to CLPP and DPP requiring the merging of different separate processes within an enterprise.

Several researchers merged individual approaches in order to remove the deficiencies and come up with optimum plans. Gao et al. [10] presented an integrated process planning and scheduling model, based on taking the advantages of NLPP (alternative plans) and DPP (hierarchical approach) models. Their model is based on the principles of concurrent engineering, where computer-aided planning and scheduling are performed simultaneously.

The concept of non-linear process planning is typical of integrated process planning and scheduling (IPPS). Usher and Fernandes [11] presented the PARIS system, which is designed to support dynamic process planning. It consists of two phases; static and dynamic. Each phase is characterized by objectives that should be met.


112 Slak, A. - Tavčar, J. - Duhovnik, J.

improved hybrid genetic algorithm (HGA). To improve the results, they also used heuristic rules, i.e. the shortest processing time (SPT) and the longest processing time (LPT). Lee and Kim [22] suggested simulation with a genetic algorithm, linked with a simulation module that calculates the success rate of planning, based on combinations between process plans. Total production time and delivery date were used as target functions. Gao et al. [10] suggest the use of a modified genetic algorithm where each function (process planning and scheduling) was given a different chromosome representation. Minimum throughput time of all products was used as the target function. For the integrated model a mathematical model, solved with genetic algorithms was introduced [23]. Lestan et al. [24] developed a simple genetic algorithm for a job-shop problem. Makespan as a target function was used and the solution was found without the help of heuristic rules.

The article describes a new approach with GA in the batch production. Separate functions, production planning and scheduling are put together in one system. A planning model which comprehensively covers production planning has been developed. Constructing the model has shown that planning should also include material and tool purchases as well as product sales. This model has served as a basis of determining a satisfactory plan with genetic algorithms. Several target functions and criteria are present in the optimisation procedure. This has improved productivity, utilisation of machines and delivery times. Presented are also experimental results and a comparison with other authors is given.

2 PROBLEM DESCRIPTION: PLANNING AND SCHEDULING IN BATCH

PRODUCTION

Planning and scheduling of products in the production of turned parts are complex problems. An n number of products should be scheduled across an m number of machines in the way that they will be manufactured and delivered to the customer on time. Each product has at least one alternative plan that includes several operations and is also the end product. Each operation is performed on a single machine. Currently, plans are made manually and everything depends on

Kumar et al. [12] proposed the integration of process planning and scheduling activities through two controlling modules – the process plan generator and scheduler. Jain et al. [13] and Razfar et al. [14] propose a similar way, by dividing the problem into two modules.

Nowadays, modern manufacturing systems should be adapted to the changes on the market. They all have the same objective – the lowest possible manufacturing costs. On the other hand, they wish to meet quality and timely delivery requirements of customers. Starbek et al. [15] developed a method which enables the delivery time of a new product on the basis of actual throughput times of previous products. The suggested procedure also allows changes of the calculated delivery time with regard to confidence and risk level. Kopač et al. [16] presented modern techniques together with methods for the reduction of throughput times and improved quality.

The importance of artificial intelligence has grown due to an increased complexity of planning problems. Methods, based on a genetic algorithm, for example, are very efficient for difficult problems. They do not always yield the best absolute solution but they find an approximation, good enough for practice. Balič et al. [17] used genetic algorithms in order to arrange machines and apparatus in a flexible processing system. They used variable transferable costs as their target function. Yan and Zhang [18] developed a uniform planning and scheduling optimisation model for the management of a three-tier manufacturing system. In order to solve the problem, they opted for a heuristic approach, based on genetic algorithms. They developed an algorithm where they determined the optimum size of a batch. Park and Choi [19] suggested the use of genetic algorithms (GA). The target function was the shortest production time of all products, while taking account of alternative machines and alternative sequence of operations. Using genetic algorithms Kimms [20] solved a mixed integer program formulation which he had developed for multi-level, multi-machine scheduling. Zhang and Yan [21] defined the planning and scheduling problem as a nonlinear mixed integer program model. For the purpose of simultaneous optimisation of production planning and scheduling, they presented an



a single person who relies on their experiences. Some products are subjected to planning according to a MRP system. It is a well-known fact that such a system does not always lead to workable plans, which makes it is almost useless in a dynamic environment. For this reason planners extend the product’s throughput time and thus deliberately extend delivery times. In turn, this creates longer queues, less flow through production, less effective utilisation of machines and higher production costs. Current planning does not provide any feedback on the situation of manufacturing resources and response to any change on the shop floor (machine failure, urgent order) is poor. The number of items for a single operation is not known and the whole potential of the information system is not exploited. This results in high consumption of time to coordinate plans and scheduling causes conflict situations.

Since process planning and scheduling are closely related functions, their integration in such an environment is vital for the creation of the best plan.

3 INTEGRATION OF TWO MAIN FUNCTIONS: PRODUCTION PLANNING

AND SCHEDULING

The basis for our work was the existing ERP (Enterprise Resource Planning) information system, which represents the backbone of our dynamic planning system. Fig. 1 shows the overall procedure for the integration of the production planning and scheduling functions. It is basically divided into two modules – the first module or production planning module, where all alternative plans for each product are determined, and the second module or scheduling module with the genetic algorithms approach. In the first module, the static or pre-planning phase is performed, with the emphasis on specifying various plans according to available resources and specifying production quantities and material needs. The second module involves the final planning phase, where target functions and realistic conditions in the manufacturing environment are considered. This means that the available resources are matched with the required ones. If the current capacity does not meet the requirements, an extra plan should be included in the process. In practice,

this means that a product is being manufactured simultaneously on two machines.

First, from the CAPP system all orders are collected, followed by making a rough plan by means of gathering the information on the available capacity of regular orders that are always manufactured on a single machine. For other orders, alternative plans are prepared according to other available resources. This is followed by specifying material needs and capacity for all production quantities.

Fig. 1. Integrated model with production planning and scheduling function

When alternative plans, manufacturing procedure (sequence of operations), required quantities of material and tools are available, a scheduling module for which these are the input data begins. In this module the final planning phase is performed and its key role is to schedule all product operations across machines in a way that meets the scheduling criteria. Detailed planning of the manufacturing plan is subject to timely delivery of materials and tools by suppliers. Specifying the optimum plan, the target functions are the minimum throughput time of all products and minimum total costs. At the same



time, another aim is to achieve the most effective utilisation of machines, reduction of set-up times for the first operation and to minimise order delays.

Once a satisfactory schedule has been completed, it is considered as a working frame. If urgent orders arrive after the schedule has been completed, certain tasks are given higher priority. If an order has been cancelled or there is an equipment failure, a new schedule should be created. Due to the above-mentioned reasons it again functions as a frame. If there are no modifications, the optimum schedule should take place on the shop floor.

The following assumptions have been specified for the solution of such a problem:• Only one product can be produced on one

machine at the same time;• Each product can be machined independently

from other products;• Each product has one to three alternative

plans (different CNC machines);• The sequence of operations is predefined for

each product in the alternative plan.

3.1 Advantages and Disadvantages of the Proposed Method

The integration of production planning and scheduling has led to the shift from manual scheduling to automated, computer-aided planning. Defining a detailed time when the manufacturing process of an order will start and when it will end, including delivery time, is the biggest advantage. Each operation on a product has a specified start time and end time. For example, it is specified when and how many products should be ready for galvanic coating. A planner is now able to check in any given moment which products are being produced on which machines and how many of them are on each operation. Such a planning method gives a planner a more supervisory role as they can follow via the information system what goes on on the shop floor and is informed whether an order is behind or ahead of schedule.

Manual methods include a lot of verbal communication with the shop floor, physical checks of machines on the progress of the work, which is now no longer necessary. The automated

planning and scheduling system also allows control over finished products, material and tools. Planning requires consistency in the monitoring of products on each operation throughout the manufacturing process. This information allows an accurate prediction of how many products can be finished in a short period of time. This is important in the case of increased demand for a product by a customer. The most important thing for managing a dynamic manufacturing environment is a quick and efficient response to changes, such as machine failure, maintenance work, and urgent orders. This means that if the machine breaks down, then new borders are put into the planning system. The products that have already been put on the machine get the status “in progress” and they cannot be withdrawn, but the products with the “plan” status can be rescheduled. This status is written in the ERP system. The same procedure applies to urgent orders. A new plan can be made any time, the response time for a new plan is 15 minutes, on average. Information about the finished operations from the production is an automatic update in the ERP system every 30 minutes.

Managing changes of materials and tools delivery dates by suppliers could be a drawback of production planning. If materials and tools are out of stock or they are not supplied on the planned date, the plan is unworkable and should be delayed by at least one day, which can trigger delays in the supply of end products.

4 USER INTERFACE BETWEEN ERP SYSTEM AND SCHEDULE OPTIMISATION

SOFTWARE

A manufacturing information system is the backbone of the planning and managing of production, which is why the existing information system within the planning model has been complemented rather than replaced. All data are stored in the ERP information system. In order to calculate the near optimum plan and schedule, data are exported and optimisation of the plan with genetic algorithms is performed. The collected data are then returned to the information system. The whole system of data management is shown in Fig. 2.



Data are exported from the information system with the Oracle Discoverer 4.1 application. The proposed algorithm is performed once a day or at a request into a purpose built software application (relational base MS Access, Visual Basic). Later, a transition to Oracle base and C++ is planned. A genetic algorithm was used and proved very fast and with good conversion results. This local base is the groundwork for planning on the basis of the data from the basic information system. The optimisation procedure results in a detailed plan and schedule of products manufacturing. It serves as input information and enables the planner to generate manufacturing product tasks within the ERP system.

Fig. 2. Data flowchart

The Oracle Discoverer application allows access to the data base, located in the information system. Each ERP session has a table with data that are also visible in the session itself. Necessary information is specified in the Oracle Discoverer application. The application also allows data filtering, additional equations-based restrictions, similar data pooling etc. The selected data are exported into the local relational base that functions as the basis for planning and scheduling. The relational data base is created in MS Access which serves as the basis for the plan. Each field is written in the same way as in the table in the ERP (Baan) system. The same applies to tables names.

A purpose application has been created for the optimisation process. On the basis of the ERP information system it schedules the activities on the shop floor in real time. Genetic algorithms

have proved to be an efficient method for product scheduling.

5 GENETIC ALGORITHM-BASED APPROACH FOR PRODUCTION

SCHEDULING

Genetic algorithms (GA) are a method of evolutionary computation. Genetic algorithms are a random search technique, not requiring detailed knowledge about a problem that is being optimised.

Fig. 3 shows the application of the genetic algorithm procedure. At the beginning, all alternative plans and other relevant information for the calculation of a satisfactory plan are gathered. Other data include delivery dates, stock information, materials and tools supply. In the next step, a population is randomly generated. It consists of a large number of different possible plans. On the basis of target functions and restrictions, each chromosome of the initial population is evaluated. The evaluation process is then carried out, with better and better solutions being sought on the basis of selection, reproduction, crossover and mutation until the required condition is reached. The condition in our case was a maximum number of generations.

Fig. 3. Course of the application of the genetic algorithm

Genetic algorithms represent solutions of problems in structures, called chromosomes. The collection of chromosomes is called a population



and specific chromosomes in the population are individuals. A population at any given time is called a generation. The goal of genetic algorithms is to create new generations. Although the population can change from generation to generation, the size of the population and the structure of chromosomes remain the same. They operate with characters, representing parameters. Proper specification of parameters or chromosome syntax is an important step. It has an effect on subsequent work with genetic algorithms and on the share of workable solutions in the search area in terms of a large number of plans and restrictions. The literature has so far presented the chromosome in a number of ways [25]. Below are presented the chromosome, the evolutionary function with restrictions and genetic operators. The chromosome has been adapted to the needs of the production of turned parts.

5.1 Chromosome Representation

A job-based representation of a chromosome has been chosen to represent the chromosome of the given problem. The manufacturing type in the company is by orders. One product is represented by one gene in the chromosome. The gene is represented by two pieces of information, with the first one being the number of the alternative plan according to which the product will be manufactured while the other one specifies whether tools presetting will take place (number 1) or not (number 0). The gene can be extended to other characteristics. The chromosome is shown in Fig. 4. The chromosome is made up of a group of genes. In our case this is the schedule of all the products to be manufactured. This means that the chromosome represents a permutation of tasks. Representation of the chromosome by products belongs to direct approaches, where the solution is built in the chromosome. Genetic algorithms are used for the creation of the chromosomes whose objective is to find a better schedule. First, operations of the first product in the chromosome are scheduled across machines. They are followed by the second product’s operations and so on to the last one in the chromosome.

When the target function of the chromosome is calculated, it has to be decoded

back to the solution of the problem, which means the schedule of products. During the encoding it is of the utmost importance to keep the subsequent solution of each chromosome decoded within the possible space of a given problem. This feature is called feasibility. When the generated chromosome cannot be decoded into a solution, correcting techniques are applied. They change an illegal chromosome into a legal one. This feature is called illegality.

Fig. 4. Chromosome

The relational database contains information on products that is used for the purpose of order scheduling. The list of genes or the chromosome represents one possible schedule on the shop floor. Using GA, a satisfactory schedule, as close to the optimum as possible, should be found. The GA principle is similar to natural evolution. Only the strongest individuals survive, creating a new and more advanced population of descendants by means of gene mutations and their crossover.

5.2 Evaluation Functions

The shortest makespan and minimum manufacturing costs were set as target functions and manufacturing restrictions and customers’ requirements were considered. Delivery time is the most important customer requirement. Other restrictions and criteria are described later on. Special emphasis is on the products where one of the operations is performed at an outsourcer. Together with the making itself, the manufacturing time also involves machine set-up and finishing times. Total costs consist of manufacturing costs, labour costs and depreciation costs.

Mathematical model symbols: N Number of orders or work orders,A Number of items on the bill of material,M Number of machines on the shop floor,Pi Number of alternative plans for product i,Gil Number of operations for product’ i plan l,



oilk k-operation for product’s i plan l,Qo,i Ordered quantity of product i,Qp,i Quantity of product’s i workpieces,

overlapping with the next operation,tpo,ilkj Time of operation’s oilk overlap on machine

j,tt,ilkj Technological time for the production of

one product i for operation oilk on machine j,

tw,ilkj Processing time for operation oilk on machine j,

top,ilkj Time of the performance of operation oilk on machine j

tpz,ilkj Set-up and finishing times of machine j for operation oilk

tes,ilkj The earliest beginning of operation oilk on machine j

tef,ilkj The earliest end of operation oilk on machine j (df,ilkj)

tp,i Product’s i throughput timetp,j Throughput time for all operations,

performed on machine j, i.e. total time when a machine is engaged in the performance of all planned operations

ts,ilkj Set-up time of machine j for operation oilktmo,ilkj Time of interoperational hold-up for

operation oilk on machine j wi Product i prioritydd,i Scheduled delivery date of product i to the

customertp,max Maximum time on machine among all

machinescssc,i Manufacturing costs for one piece of

product i (all operations, materials, tools)cd Labour costsca,j Depreciation cost for machine jcs,ilkj Cost of machine j for operation oilkcpz,ilkj Cost of operation’s oilk set-up and finishing

time on machine jcw,i Total cost for processing all operations for

product icp,i Total costs for product iC Total costs for all productsmnuj Non-utilisation of machine jMU Maximum utilisation of all machinesD Large positive integerZi Delivery delay of product i (in the number

of days)cwf Combined weighted functionλ Weight of delay

δ Costs of 1 day delay of the delivery date

αii j

ii j

´

;´

;=

1

0

product appears beforeproduct on machine product DOES NOT appear before product on machine

ii j´

βili

=10; alternative plan for product IS selected ;; alternative plan for product IS NOT selectedi

γ ilk =10; ;operation is performed simultaneouslyoperation iss performed sequentially

Target functions:a) Minimum throughput time of all tasks and

operations on a single machine:

Min t Max t t tp p p p M,max , , ,, ,...,= 1 2 ,j M=1,..., ,

t tp j op ilkj ilk

G

l

P

i

N ili

, ,= ⋅( )

===∑∑∑ β

101.

(1)

Time of the performance of operation: t t t t t Qop ilkj pz ilkj w ilkj pz ilkj t ilkj o i, , , , , ,= + = + ⋅ .

Overlap time:t t Qpo ilkj t ilkj p i, , ,= ⋅ .

b) Timely delivery with minimum delays:

Min Z Max Z Z ZNmax , ,...,= 1 2 ,i N=1,..., ,

Zd d

d did i f ilG

f ilG d i

il

il

=>

−

0 ; if

otherwise, ,

, , ; ,

d tf ilG ef ilG jil il, ,= .

(2)

c) Minimum total manufacturing costs for all products:

C Min cp ii

N

==∑ ,

1,

c c c cp i pz ilkj s ilkj ilj

M

k

G

l

P

w i

ili

, , , ,= +( ) ⋅( ) +===∑∑∑ β

110.

(3)

Processing costs: c c Qw i ssc i o i, , ,= ⋅ ; i = 1, ..., N .



Machine costs:c t cs ilkj op ilkj a j, , ,= ⋅ .

Set-up and finishing time costs:c t cpz ilkj pz ilkj d, ,= ⋅ .

d) Combined weighted function:

cwf c c

c Z Z

p i z ii

N

z i i i

= +( )= ⋅ ⋅

=∑ , ,

,

,

( ^ ) .1

λ δ(4)

e) Maximum utilisation of all machines:

MUmnu

mnut

t

jj

M

j

w ilkji

N

j

=

= −

=

=

∑

∑

1

1

1

1

min,

,

max,

=; ,..., .j M1

(5)

All restrictions have to be considered for an optimum manufacturing schedule of turned parts. They are listed above. Below, they are presented in a mathematical form.

Restrictions:a) Only one product can be processed on each

machine at the same time:

If thend dt t D t

d i d i

p i l k j p ilkj ii j w i l k j

, , ´

, ´ ´ ´ , ´ , ´ ´ ´( )<

− + ⋅ − ≥1 α .(6)

b) Sequence of operations is predefined for each product in the alternative plan:

t t t ti N les ilkj w ilkj pz il k j es il k j, , , ( ) ´ , ( ) ´

,..., ; ,

+ + ≤

= =+ +1 1

1 0 ...., ; ,... ;, ´ ,..., .

P k Gj j M

il==

11

(7)

c) Only one alternative plan can be selected for one product.

Assumption:

w w

i N

il i l

ill

Pi

>

= ∀ ∈[ ]

+

=∑

( )

,

1

01 1β .

(8)

d) Operation is performed simultaneously or sequentially.

Product's throughput times change because operation can be performed simultaneously or sequentially.

Throughput time for product i where operations are performed sequentially:

t t t t tp i ef ilG j es il j op ilkj mo ilkjk

G

il

il

, , , , ,= − = +( )=∑1

1. (9)

Throughput time for product i where operations are performed simultaneously:

t t t t t

t

p i pz ilkj po ilkj mo ilkjk

G

op ilG j

mo

il

il, , , , ,

,

,= + +( ) +=

−

∑1

1

iilkj es il k j ef ilkjt t= −+, ( ) ´ , .1

(10)

e) Throughput times for each operation and product costs have to equal or exceed 0:

t cp ilkj p i, ,≥ ≥0 0 ; , (11)

i N l P k G j Mi il= = = =1 0 1 1,..., ; ,..., ; ,... ; ,..., . (12)

Target functions equations are written from Eqs. (1) to (5). Taking account of all the objectives can be termed a multi-objective problem. The most important objectives to achieve are minimum throughput time Eq. (1), timely delivery Eq. (2) and minimum total costs Eq. (3). Combined weighted function Eq. (4) is composed of total costs and delays. It is necessary to achieve the highest possible efficiency of machines Eq. (5), which reduces idle time on a machine.

Restrictions that appear in the algorithm are written from Eqs. (6) to (10). Eq. (6) determines that a machine can produce only one product at a time. The sequence of operations for two different products on the same machine depends on the delivery date. It applies to all operations for all products. Eq. (7) determines the sequence of operations to be fixed for each product. It further increases the complexity of the problem. Only one alternative plan can be used for each product, which is shown in Eq. (8). The first alternative technological process is used as the initial plan. Choosing only one possible plan automatically means that only one machine is designated for the performance of a single operation because the machine is hidden in the plan. Eqs. (9) and (10)



determine a product’s throughput time according to the way of operations performance. The algorithm also takes account of the fact that some operations take place consecutively and others simultaneously. Eq. (11) determines throughput time of each operation and all product costs should equal or exceed 0.

5.3 Genetic Operators

5.3.1 Selection

Tournament-type selection was used. For this selection type, a certain number of chromosomes is selected, which then take part in the tournament. Two individuals are matched and the chromosome with the best value is selected. If the number of chromosomes to be matched increases in an algorithm, the pressure on the selection increases. Inferior chromosomes are not selected and thus do not take part in the creation of the next generation while at the same time good individuals also do not dominate the reproduction process.

5.3.2 Crossover

Two-point crossover was chosen as the crossover operator. Selected from the population are two parents who form two offspring. Two random points are selected in the chromosome, as shown in Fig. 5. The material between both points is exchanged between the parents. This always creates feasible solutions. The crossover operator is subjected to the probability that determines the use of the crossover chromosome.

5.3.3 Mutation

A mutation is about introducing new genetic material in the existing chromosome. It introduces variety in the population and broadens the search area. A mutation as well as crossover is subjected to some probability. For the existing problem, the uniform mutation was selected. This mutation is about each gene in a chromosome being attributed a random number, which is then matched with the probability of mutation. If the random number is smaller, the gene is subjected to a mutation. This is shown in Fig. 6. Genetic operators were selected on the basis of

a systematic comparison between the results of optimising and the necessary calculating time.

Fig. 5. Crossover operator

Fig. 6. Mutation

6 EXPERIMENTAL RESULTS

During the development of the genetic algorithm, several set-ups and approaches were systematically tested. Tests showed that with roughly 1000 evaluations it is possible to find a satisfactory solution for a plan. The size of the population was limited to 60. A larger population extends the calculation time while there are no significant savings with the population sizes of 80 and 120 (Fig. 7).

The optimum selection was determined after comparing selections without the use of crossover and mutation operators. The roulette and tournament selections proved to be the best.

Two-point crossover with one-point mutation quickly converges towards a good solution but it also calms down quickly and is unable to look further for better solutions. A random mutation is looking for solutions to a larger extent and the convergence of the population towards better solutions is slower but the final solution is very good. For these reasons, two-point crossover with random uniform mutation is used



in the final version. The crossover probability is set at 0.9 and the mutation probability at 0.01.

For the purpose of determining the optimum solution for the production of a product or a product range, several alternative plans were used for each product. These plans are entered in the information system and their numbers of operations and designated machines for each operation are already specified. Thirty products were scheduled onto eighteen CNC machines

in the case study. There are thirty machines and ninety-five operations all together. There can be up to four operations on a particular product and up to three alternative plans. Planned time in hours and machines for particular operations are shown in Table 1. In the case of alternative plans additional machines are specified in the same way as with products J2, J4 and J5. In parenthesis, the machine is marked with M and next to it is the number of the machine.

Fig. 7. Influence by size of population on convergence

Table 1. Processing time of all operations for a job with alternative plans

JobsProcessing time

Operation 1 Operation 2 Operation 3 Operation 4Plan 1 Plan 2 Plan 3J1 30.3 (M13) / / 0.8 (M19) 2.8 (M27)J2 35.1 (M10) 33.6 (M8) 33.6 (M15) 1.21 (M19) 1.04 (M22) 17.42 (M26)J3 92.1 (M13) / / 1.3 (M19) 11.1 (M27)J4 10.8 (M10) 8.9 (M12) / 0.3 (M19) 0.3 (M27)J5 9.6 (M10) 8.7 (M12) / 0.3 (M19) 0.4 (M27)

Table 2. Results from the initial plan

Total costs [€]

Throughput time [h] Makespan [h] Sum of delays

[day]

Utilization of all machines

[%]

Combined weighted function [EUR]

Initial plan 146269.6 4386.27 1257.58 83 23 452567.5



For each target function an almost optimum solution was found with genetic algorithms. Solutions are good enough satisfactory for production scheduling. Better solutions do exist but a minor difference does not make up for additional calculation time. The objective is to find the near optimum or satisfactory manufacturing plan out of the alternative plans in order to achieve good result of the fitness function.

The results of different target functions are summarized in Table 3. The size of the population was set to sixty and the number of generations to fifty. Calculation time on a middle class personal computer was 280 seconds for each target function from Table 3. The almost optimum solutions for particular target functions are highlighted. Other values in the same line are given for a comparison between different target functions. Each target function achieves the best result in the column

Table 3. Comparison between the values of target functions

Target functionTotal costs [€]

Throughput time [h]

Makespan [h]

Sum of delays [day]

Utilization of all

machines [%]

Combined weighted function

[€]Total costs [€] 116849.2 3565.60 421.55 29 51 161489.5Throughput time [h] 125131.9 3533.68 461.08 36 49 193800.8Makespan [h] 126840.1 3753.66 421.55 33 52 180927.5Sum of delays [day] 138955.9 4001.91 443.98 6 53 146468.9Utilization of all machines [%] 153241.1 4139.53 443.98 48 58 259471.8Combined weighted function [€] 124658.7 3816.34 452.98 7 51 134035.1

Fig. 8. Gantt chart for the operations from the scheduling problem



– it matches expectations. Special attention was paid to delivering products on due dates and to control costs at the same time. We have set up a Combined weighted function model that considers both criteria: due date and costs. The comparison between the first and the second target function reveals that the results are very similar. Due to bigger makespan, the Throughput time function yields more delays and higher additional costs. In that case products are not well scheduled on the machines.

According to the minimum delays criteria the fourth target function is the best choice. In this case, products extend the throughput time and increase total costs although combined costs are at an acceptable level. The delays are only 6 days. The last of the target functions is a combined weighted function. It represents a compromise between total costs and delays. The value of this target function from Table 3 is not optimal, but it is a good solution acceptable to be used in practice.

Attention was also paid to the busiest machines on the shop floor and utilization of the machines. The maximum utilization target function, where the result is 58%, the order of the products on the machines causes the longest delay. This is probably due to the order of the products on the machines. All products produced together result in very good efficiency, but some products will be delayed because of this.

Other researchers report that 1200 calculations with the genetic algorithm represent a good enough solution that could be put into practice. In our case there were 3000 calculations.

Fig. 8 shows the Gantt chart for all operations from the scheduling problem. On the vertical line are machines and time in hours is on the horizontal line. Each rectangle represents one operation. From the chart it can be seen that on machine 19 operations take a shorter time. This is the deoiling machine and it can operate with many parts concurrently. Machines M20 to M22 are galvanizing machines. Those machines operate also with big product batches. Makespan for the presented schedule is 452.98 hours, which can be seen in Fig. 8. Some machines do not operate at all. All alternative plans for each product were considered during the optimisation process and those machines were not chosen in any satisfactory scheduling plan. Machines

from M1 to M18 are CNC machines. For these machines we also considered the working time at the beginning of the scheduling. If a machine is not available at the beginning the grey rectangle in Fig. 8 shows finished time of the production. And after that the product can be scheduled on this machine. We believe that the major problem in this example was scheduling on CNC machines because the operations are the longest. Machines from M19 to M30 represent the second operation, third operation, etc. These operations mostly take a short time and operate with big product batches.

The proposed genetic algorithm was tested on a similar problem from Gao et al. [23]. There are six jobs and five different machines, each job has several alternative plans. The results are in Table 4. Makespan is the result of the algorithm. Probability pr, pc and pm are settings of probability for reproduction, crossover and mutation, respectively.

Computation time cannot be directly compared as it was not recognized as bottle-neck due to different programming environments. When the target functions (makespan) the results are close together. The proposed GA results in longer makespans, but it is good enough for practical application. The results from all cases of the proposed GA are almost the same. The increase of the population size in this simple problem does not yield significantly better results. The closest makespan is achieved in the second case. With a population size ten times smaller, almost the same result is obtained. We believe that the result in the second case is satisfactory for this simple problem.

After all these comparisons a specific solution for the production of turned parts’ has been created: combined weighted function offers the satisfactory solution. From the point of view of the sales department, minimum delays are top priority, while on the other hand, for the production department and management, minimum costs are top priority. This target function allows both.

7 CONCLUSION

This article has shown how to apply a genetic algorithm for batch scheduling on an example of the production of turned parts,. Each criterion or target function appears to be very



convincing, which could result in a dead end. For this reason, it is vital to check combinations of different target functions as in the case of combined weighted function. The proposed criterion for the production of turned parts includes total costs and delays. For the lowest common costs, additional delays will have to be accepted.

Table 4. Comparison of the results

Gaos’ approach Proposed GA

Code C++ VB

Computer2Ghz

Core ™2 Duo CPU

1.66 Ghz Core™2 CPU

1st case

2rd case

3rd case

Pop size 200 200 20 60Max gens 50 50 50 50Iterations 20 20 20 20pr 0.1 0.1 0.1 0.2pc 0.8 0.8 0.8 0.9pm 0.1 0.1 0.1 0.01Makespan 92 96 95 96

Finally, it was possible to compare the results from the selected target function and the initial plan, shown in Table 2. It was applied on the machine according to when the orders reached our company and for each job first an alternative plan was chosen. The costs of the combined weighted function drop by 69.1%. 15.6% amount to the total cost, while the rest is owing to the machines being chosen correctly. The makespan moves from 1257.58 to 452.98 hours. The amount of saving is the result of our strong emphasis on delays. For this reason a special weight has been developed.

In addition to planning and scheduling of manufacturing activities, the presented model also allows optimisation of manufacturing plans and machines. In this case the solution area is much larger due to the fact that all available machines are varied within each operation of the plan. It is possible to also take account of the machines that are not yet present in the existing plans or exist only in investment plans.

8 ACKNOWLEDEGMENT

The research is supported by the Ministry of Higher Education, Science and Technology under the project P-MR-08/88. Operation part financed by the European Union, European Social Fund.

9 REFERENCES

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[2] Tasič, T., Buchmeister, B., Ačko, B. (2007). The development of advanced methods for scheduling production process. Strojniški vestnik – Journal of Mechanical Engineering, vol. 53, no. 12, p. 844-857.

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[5] Tan, W., Khoshnevis, B. (2000). Integration of process planning and scheduling – a review. Journal of Intelligent Manufacturing, vol. 11, no. 1, p. 51-63.

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[8] Wang, L., Shen, W. (2003). DPP: An agent-based approach for distributed process planning. Journal of Intelligent Manufacturing, vol. 14, no. 5, p. 429-439.

[9] Zhang, H-C. (1993). IPPM - A prototype to integrate process planning and job shop scheduling functions. CIRP Annals – Manufacturing Technology, vol. 42, no. 1, p. 513-518.



[10] Gao, L., Shao, X., Li, X., Zhang C. (2009). Integration of process planning and scheduling – A modified genetic algorithm-based approach. Computers & Operation Research, vol. 36, no. 6, p. 2082-2096.

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[16] Kopač, J., Soković, M., Rosu, M., Doicin, C. (2005). Quality and cost in production management process. Strojniški vestnik – Journal of Mechanical Engineering, vol. 54, no. 3, p. 207-218.

[17] Balič, J., Ficko, M., Brezočnik, M. (2005). A model for forming a flexible manufacturing system using genetic algorithms. Strojniški vestnik – Journal of Mechanical Engineering, vol. 51, no. 1, p. 28-40.

[18] Yan, H-S., Zhang, X-D. (2007). A case study on integrated production planning and scheduling in a three-stage manufacturing

system. IEEE Transactions on automation science and engineering, vol. 4, no. 1, p. 86-92.

[19] Park, B.J., Choi, H.R. (2006). A genetic algorithm for integration of process planning and scheduling in a job shop. AI2006: Advances in Artificial Intelligence, p. 647-657.

[20] Kimms, A. (1999). A genetic algorithm for multi-level, multi-machine lot sizing and scheduling. Computers & Operations Research, vol. 26, no. 8, p. 829-848.

[21] Zhang, X.-D., Yan, H.-S. (2005). Integrated optimization of production planning and scheduling for a kind of a job. The International Journal of Advanced Manufacturing Technology, vol. 26, no. 7-8, p. 876-886.

[22] Lee, H., Kim, S.-S. (2001). Integration of process planning and scheduling using simulation based genetic algorithms. The International Journal of Advanced Manufacturing Technology, vol. 18, no. 8, p. 586-590.

[23] Li, X., Gao, L., Shao, X., Zhang, C., Wang, C. (2010). Mathematical modeling and evolutionary algorithm-based approach for integrated process planning and scheduling. Computers and Operations Research, vol. 37, no. 4, p. 656-667.

[24] Lestan, Z., Brezočnik, M., Buchmeister, B., Brezočnik, S., Balič, J. (2009). Solving the job-shop scheduling problem with simple genetic algorithm. International Journal of Simulation Modelling, vol. 8, no. 4, p. 197-205.

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*Corr. Author’s Address: University of Ljubljana, Faculty of Mechanical Engineering, Aškerčeva 6, 1000 Ljubljana, [email protected] 125

Energy Savings in Building with a PCM Free Cooling System

Uroš Stritih* - Vincenc ButalaUniversity of Ljubljana, Faculty of Mechanical Engineering, Slovenia

Alternative solutions for cooling and ventilation of buildings have appeared in practice as a counterweight to energy wasting conventional systems. Free cooling principle with phase change material (PCM) has been used as an alternative method of cooling and ventilating buildings which combines night ventilation and increased thermal mass. A simulation model defining the transient behaviour of the phase change unit was used. The heat transfer problem of the model, which was treated as two-dimensional, was solved numerically by an enthalpy-based finite differences method. The computer program was written in Fortran program language and can calculate temperature fields in certain time in paraffin area, as well as air temperatures. Energy conservation was calculated from meteorological data for a reference year in a specific country. The difference between inlet and outlet temperature were calculated, from which energy conservation for a cooling season were obtained. Calculated energy savings are from 14 to 87% depending on selected parameters.©2011 Journal of Mechanical Engineering. All rights reserved. Keywords: cooling, cold storage, phase change material, experiment, numerical analysis

0 INTRODUCTION

Alternative systems for cooling and ventilation of buildings have their power dependent on the environmental conditions. Therefore they usually need an efficient thermal storage system. Such a system should meet many demands. Among them the most important are: to store as much energy as possible in the defined volume, to have large area for heat transfer and to conduct heat as quickly as possible. One of the important parameters is also the temperature at which energy is stored. This temperature influences the efficiency of the system as well as on the indoor environment condition of buildings. One of the solutions is to use phase change materials – PCM.

Phase change materials have high melting heat which can store or release heat at melting and solidification. This heat is released or absorbed at nearly constant temperature. The content of the stored heat is from 5 to 14 times grater compared to the classical thermal storage systems (like water, stones etc.).

Thermal storages with PCM take up a major part of the research in the building area. Researchers have made fundamental investigations for more than 20 years. Chen et al. [1] have experimentally investigated cold storage in an encapsulated thermal storage tank.

Lamberg [2] made analytical model for two-phase solidification problem in a finned PCM. She calculated location of the solidification points and temperature profile. Simard and Lacroix [3] have analyzed the behavior of the plate thermal storage with PCM. They calculated optimal panel depth and the distance between them. Seeniraj and Narasimhan [4] analyzed performance enhancement of a solar dynamic LHTS module. They have numerically calculated that fins increases heat transfer in PCM. Cabeza et al. [5] have studied heat transfer enhancement in water as PCM with insertion of three possible materials: stainless steel, cupper and graphite. They have found that heat flux increases the most at insertion of the graphite. Stritih [6] has experimentally discovered that heat transfer at melting is not a problem. At solidification heat transfer can be enhanced with fins.

Applicative researchers on this field deals with insertion of PCM into wall and ceiling panels, heating and air-conditioning systems and the use of PCM in thermal storage systems. Most of the researchers have shown that integration of PCM into walls increase their characteristics. Lee et al. [7] have presented the results of macro scale tests that compare the thermal storage performance of ordinary concrete blocks with those that have been impregnated with two phase change materials. Zalba et al. [8] have discovered that the capacity


126 Stritih, U. - Butala, V.

of PCM influences on the reduction of the internal temperature oscillations compared to the external ones. Darkwa and Collagham [9] have shown that PCM panels with the narrow phase change area are the most efficient for applications in buildings. Liu and Awbi [10] have compared natural convection beside an ordinary wall and wall with PCM. They have discovered increased thermal capacity and decreased thermal conductivity. Zhou et al. [11] have analyzed the room with PCM panels. They have found that the efficiency is better if the PCM panel is located on the internal side of the wall. Ahmad et al. [12] experimentally shown that wallboards with PCM have the same characteristics even after 400 cycles. Numerical simulations [13] have shown that optimal melting temperature for PCM panels in passive solar buildings is 21 ºC. Kuznik et al. [14] made optimization of PCM wallboard for building use. They have found that optimal thickness depends on the time interval of the operation.

Research of the integration of PCM into double glass window [15] has shown that in winter thermal comfort is increased and in summer heat loads are decreased. Results show that the transmisivity of the glass remains good. Huang et al. [16] have found the integration of PCM increases the efficiency of the photovoltaic (PV) system. Wang et al. [17] have analyzed the influence of PCM on the coefficient of performance (COP) of the cooling system.

a) b)

Fig. 1. Principal function of PCM “free cooling system”; a) night time, b) day time

The objective of this paper is to present energy conservation opportunities at PCM free cooling. This is an alternative method of cooling and ventilating buildings, which combines increased thermal mass and night ventilation. The thermal inertia of a building is actively adjusted by integrating PCM into the ceiling board of a building. Cold night air is used to cool the building interior and the PCM storage. Outside

air for night cooling can be introduced into the space locally by window, wall fans or by a central air supply system. During the daytime hot indoor air is circulated in the unit (Fig. 1) [18]. The aim of the use of PCM-products is to reduce the energy needed for cooling buildings with heat overproduction.

1 EXPERIMENT

An experimental set-up for the analysis of heat transfer within the free cooling system was installed. A measurement testing line was designed for measuring the daily cold storage efficiency. The cold storage filled with paraffin was located in an air duct that let in cold air during the night. Cold air led to paraffin crystallization and therefore cold accumulation. During the daytime hot air was led through the air duct, which was cooled down due to the accumulated cold in the cold storage. The air duct was insulated in order to reduce cold exchange with the surroundings.

The testing line (Fig. 2) consisted of a personal computer (1) in which the measured data were put through an A/D (Analogy/Digital) converter (2) and I/O (Input/Output) Card (3). Characteristic values for the inlet (5) and outlet (8) temperatures were measured with type K thermocouples (4) and air velocities were measured with an anemometer (10). Air entered into the insulated air duct (6) through an inlet aperture and was then fed through the cold storage (7) to the outlet aperture (8) by the fan (9), where it was measured by anemometer (10).

Fig. 2. Testing line scheme for measuring daily cold storage efficiency

The cold storage was composed of a metal box, whose shape enabled it to be built


127Energy Savings in Building with a PCM Free Cooling System

Table 1 shows the common properties of cold storage.

Table 1. Cold storage properties

Dimensions 500×220×90 mmTotal mass 16.2 kgMass of the shell 12.6 kgMass of PCM 3.6 kgPhase change temperature 20-22 °COperation temperature 15-30 °CCold storage capacity 172 kJ/kgCold stored in PCM 732.6 kJCold stored in shell 11.3 kJTotal cold stored 743.9 kJ

Inlet, outlet air temperatures, and airflow have been measured. These measurements were conducted in April, when the outside air temperatures were below 15 °C. At night-time the outside air was used for cooling PCM, which was placed in a thermal buffer. Measuring usually started at 7:30 AM and ended at 2 PM. The cooling time for PCM at night-time with the outside air was approximately the same for every single measurement. It was essential to have the same amount of coldness inside the buffer, so that all the results would be comparable. The process of measuring took 7 days and during those days the outside temperature remained stable. We made several measurements for combinations of inlet temperature 26, 36 and 40 ºC and air flows 1.5 and 2.4 m/s.

into a ceiling board. In order to increase the thermal power of the cold storage two different industrials fins were used. These fins were made of aluminium and were connected to the metal box from the interior and exterior sides on both upper and lower sides of the box (Fig. 3). The contact between the box and fins was filled with thermal conductible paste.

Fig. 3. a) Shape and b) cross section of the cold storage

Fins were used here for two different reasons. On the exterior side of the cold storage, fins were connected to the box in order to increase the surface area, which leads to a higher convection cold flux. On the interior side of the box, the fins were connected in order to increase the conduction cold flux. The inside of the box was filled with paraffin with a melting point of 22 °C. The heat storage capacity of the paraffin used here was 172 kJ/kg within a temperature range of 11 to 26 °C and had a specific heat of 1.8 / 2.4 (solid/liquid) kJ/kg K. Used paraffin is chemically inert with respect to most materials and should therefore not contribute to corrosion.

Fig. 4. Outlet air temperatures at different inlet air temperatures and airflows



Fig. 4 shows that when we used airflow of 1.5 m/s, the outlet air temperature was lower than at airflow of 2.4 m/s. This means that the length of the buffer used was not appropriate for airflows larger than 1.5 m/s and inlet air temperatures below 30 ºC. The reason is that after 200 minutes the outlet air temperature equals the inlet air temperature.

However, in the case of sequentially used buffers the effect would be greater and the air could be cooled for longer. At regime Ti = 36 ºC and v = 1.5 m/s the outlet air temperature approaches the inlet air temperature but does not reach it. This is caused by thermal losses to the surroundings. In our case, the loss was to room temperature, which was 23 ºC on average. This constituted an additional heat flux that was cooling the air to infinity. The same happened at regime Ti = 40 ºC, where the temperature disparity

was much higher and therefore the heat flux was also greater (Fig. 5).

We calculated the amount of cold released under different regimes. The results represent the amount of cold released at a time interval between 0 and 5 minutes or in other words between 0 and 300 s. Other components of cold were calculated using Excel software.

Fig. 6 shows the amount cold, which the buffer released under individual regimes.

2 MATHEMATICAL MODEL AND COMPARISON WITH EXPERIMENTS

The mathematical model enables us to calculate the stored cold in PCM and the temperatures of cooled air. The physical picture is the following: a cold storage chamber with dimensions W, H and L is filled with PCM. Cold is

Fig. 6. Amount of cold stored in cold storage at different inlet air temperatures and airflows

Fig. 5. Heat flux of the cold storage at different inlet air temperatures and airflows



transferred and stored in PCM as described below. The air is cooled by convection heat transfer (Fig. 7). Energy storage in PCM includes a moving boundary problem: there is a solid phase on one side and a liquid phase on the other.

Fig. 7. Control volume of the PCM

The partial differential equation for heat conduction:

∂∂Tt

adiv gradT a T= =( ) ∆ (1)

must be written for a solid (s) as:

∂∂Tt

a div gradTss s= ( ) (2)

and for the liquid (l) phase as:

∂∂Tt

a div gradTll l= ( ) . (3)

Since heat is transferred across the borders of the system the increase in energy can be described by introducing specific enthalpy:

ddt

h dV gradT dAV A

ρ λ. . . .∫ ∫= . (4)

The following Eq. can be written for this finite element:

ρ λ λ

λ λ

V dhdt

A dTdx

A dTdx

A dTdy

A dTdx

i jt

t

i

i

i

i

j

j

, . .

. .

+ + −

+

= − +

+ −

1 1 1

1

jj

j−1 (5)

and using a standardized differential method we get:

h h tl

T T T T Ti jt

i jt

i j i j i j i j i, , , , , , ,.

.( )(+

− + − += + + + + −12 1 1 1 1 4λ

ρ∆∆ jj ) . (6)

This Eq. is used for all control volumes except for the borders, where the boundary conditions are defined as:

dTdx

dTdxx x L= =

= =0

0 and

(7)dTdy

dTdy

T Ty y

out i in iH H=− =+

= = −2 2

α ( ), , .

Where α is the turbulent convection coefficient calculated from the following equation:

Nu = 0 018 0 80, Re , . (8)

Since the thermal conductivity of PCM is very low, the heat conduction can be increased with fins. Increased thermal conductivity can be calculated using the following Eq.:

λ λλ

λ λ λ

= +

−+

pcmfin

fin pcm

pcm

pcm

f13

.(9)

The computer program was written in the FORTRAN program language and can calculate temperature fields over a certain time within the paraffin area as well as air temperatures.

The program numerically solves transient two-dimensional heat transfer equations, which contain an equation for convection on the air-PCM side and for conduction inside the PCM.

Fig. 8 presents measured inlet air temperatures and outlet temperatures received by measurements and numerically for the case Ti = 36 ºC, v = 1.5 m/s. The same conditions were used in the program and in the experiment.

Numerical values of temperatures are in quite good agreement with experimental values, especially in the middle and at the end of the measurements. At the beginning the differences are larger. The reason could be that the inlet temperatures during simulations have been set at a constant value (36 ºC) but during experiments these values change and may be increasing at the beginning of the experiment.



The key challenge was the time step and number of finite differences (mesh). These two parameters are the key to the stability of the system. If they are set wrongly, then the system is not stable and the calculated temperatures are wrong.

For our case, the temperature interval dt was 0.5 s and Dx = Dy was 0.01.

Fig. 9 presents temperatures at the middle cross section of the cold storage: air temperatures and three paraffin temperatures. At the beginning, all temperatures are the same, since the PCM is cooled to the air temperature. After some time, differences that are a consequence of heat transfer from air to the PCM can be observed. The deeper into the PCM we go, the greater is the temperature difference. At the end when the temperature reaches a maximum, all the PCM temperatures are

the same again, which means that the cold storage is empty.

3 RESULTS OF SIMULATIONS

The program was written in such a way that it reads external files with values of inlet air temperatures for every hour in a whole year (Test Reference Year) and gives outlet temperatures as a result. Energy conservation is calculated from the difference between the inlet and outlet air temperature. Calculations have been made for four representative European cities with different climate conditions: Ljubljana, Rome, London and Stockholm. The program works through the following steps:1. Constant parameters during calculations:

Fig. 8. Comparison of experimental and numerical values of air temperatures

Fig. 9. Temperatures of air, surface and PCM in middle cross section



• geometry of storage (length, width, height, depth),

• thermal characteristics (thermal conductivity, density, specific heat),

• geometry of the air channel,• thermal characteristics of PCM (melting

heat, solidification and melting temperature, density and specific heat in both phases),

• air flow,• thermal characteristics of air (specific heat,

thermal conductivity, viscosity),• time steps and• number of control volumes.2. The program reads values of inlet air

temperatures for the selected city every hour for the whole year (from the external file).

3. Using the above-presented Eqs. (5) to (7), the program calculates the values of the temperature for each control volume of the paraffin and air at each time step.

4. The values of the selected temperatures are written on the screen and saved into an external file for future analysis.

Table 2. Cold storage

Cold StorageHeight H 0.2 mLength L 0.5 mWidth W 2 × 0.03 mWidth of air channel 2 × 0.015 mPhase-change materialLatent heat 100 / 200 kJ/kgSol. / liq. temperature 20 / 22 °CSpecific heat solid / liquid 1800 / 2400 kJDensity solid / liquid 840 / 760 kg/m3

Table 2 presents the key parameters for cold thermal storage with PCM for the simulations.

Simulations were carried out under four different conditions: two different latent heats of PCM (100 kJ/kg, 200 kJ/kg) and two different air flows (10 m3/h, 40 m3/h) were used.

The calculations of energy conservation using cold storage were made in the following way; for the selected parameters (city, latent heat and air flow), we have calculated:a) The monthly energy required for cooling

outside air to room temperature (Eo);b) The monthly energy required for cooling air

from cold storage to room temperature (Epcm).

The difference between a) and b) represents energy savings. The percentage was calculated by Eq. (10) and results are presented in Fig. 10.

η =−

×E E

Eo pcm

o

100 . (10)

In Fig. 10, monthly energy savings using PCM cold storage for 4 different European cities are presented.

In the case of Ljubljana, the needs for cooling are not high in April and October. Therefore even with a PCM with low latent heat (100 kJ/kg) the total air volume can be cooled down. Closer to the hottest part of the year, the need for cooling is higher and the efficiency is lower. In July when the need for cooling is the highest, the share of saved energy is in the range of 20 to 60%. The higher the volume flow and the lower latent heat, the more cold has to be added by conventional cooling.

The example for Rome shows us that the efficiency of cold storage decreases in a hotter environment. In July and August the temperatures do not fall below 20 ºC, so cold cannot be stored as latent heat in thermal storage. The consequence of this is that air cannot be cooled during the day and the efficiency is lower by 10%. In other months the efficiency increases since the need for cooling is smaller and the nighttime temperatures are lower.

Examples from London and Stockholm shows us that in colder climates, where the need for cooling is lower, more than 50% efficiency during all cooling periods using cold storage can be reached.

We have also made integral calculations of cold storage in different cities. The data are presented in Table 3. They are more representative than those in Fig. 10 since they include both percentages and absolute values.

Table 3 shows that the quantity of cooling energy at 40 m2/h is four times lower than at 10 m2/h. This means that the quantity of air and the cooling energy is proportional.

The value for the cooling energy necessary for Ljubljana at the lower volume flow is 12.9 kWh. The energy savings are 8.5 kWh at a melting heat 100 kJ/kg and 10.1 kWh at a melting heat 200 kJ/kg. At higher volume flow, the cooling energy needed on a yearly basis is 51.9 kWh. Energy



a) Energy conservation with PCM cold storage for Ljubljana b) Energy conservation with PCM cold storage for Rome

c) Energy conservation with PCM cold storage for London d) Energy conservation with PCM cold storage for Stockholm

Fig. 10. The share of saved energy for different European cities

savings are 14.2 kWh and 26.3 kWh respectively for the two different melting heats. The share of saved energy at a volume flow of 10 m2/h is over 60% and at a volume flow of 40 m2/h this is reduced for 27 and 51 %.

In the case of Stockholm, the share of saved energy is similar to that of Ljubljana. This means that both cities have a similar temperature fluctuation. The cold energy needed is 60% lower in Stockholm, however, due to different climate conditions.

Rome and London represents two extreme conditions. In the case of Rome, the need for cooling energy is twenty times higher than in London. This is connected to cooling efficiency: in Rome less energy is saved (up to 30%), whereas in London the majority of cold (more than 60%) can be obtained with PCM.

4 CONCLUSIONS

The proposed model assumes that the only factors in the energy calculation are dependant on the heat flow related to ventilation. This ignores

issues of solar gains, internal heat gains, reverse transmission, and energy storage in the room.

Cold storage with PCM could be a very useful component of the ventilation system in buildings since it uses cold from the environment. However, from the analysis it is clear that such a system can only be an addition to the conventional cooling systems and cannot be used alone.

Energy savings vary from one location to another and the savings are very dependent on the weather conditions: daytime and nighttime air temperatures, the melting heat of PCM in cold storage, and the quantity of circulating air.

In places with hot weather conditions the temperatures at night will be too high to solidify the PCM under the given parameters. This means that air during daytime cannot be cooled . On the other hand, in places with cold weather conditions there is plenty of nighttime cold but no strong need for cooling.

The influence of volume flow on energy savings is also important. At low volume flows energy savings are higher than in the case of high volume flows. Nevetheless, low volume flows in general do not satisfy the needs for



Table 3. Cold storage properties for different European cities

LjubljanaLatent heat [kJ/kg]/ Air flow [m3/h] 100 / 10 100 / 40 200 / 10 200 / 40Energy for cooling [kWh] 12.9 51.9 12.9 51.9Saved energy [kWh] 8.5 14.2 10.1 26.3Saved energy [%] 66 27 78 51

RomeLatent heat [kJ/kg]/ Air flow [m3/h] 100 / 10 100 / 40 200 / 10 200 / 40Energy for cooling [kWh] 35.9 143.7 35.9 143.7Saved energy [kWh] 7.8 14.0 9.2 123.6Saved energy [%] 22 10 26 14

LondonLatent heat [kJ/kg]/ Air flow [m3/h] 100 / 10 100 / 40 200 / 10 200 / 40Energy for cooling [kWh] 1.5 5.9 1.5 5.9Saved energy [kWh] 1.3 2.6 1.5 3.7Saved energy [%] 87 44 100 63

StockholmLatent heat [kJ/kg]/ Air flow [m3/h] 100 / 10 100 / 40 200 / 10 200 / 40Energy for cooling [kWh] 5.3 21.3 5.3 21.3Saved energy [kWh] 3.7 6.4 4.2 9.4Saved energy [%] 70 30 79 44

building ventilation. From the analysis it can be concluded that the most efficient system would be cold storage with the highest possible latent heat and the lowest possible volume flow through the system.

Knowing the payback time period of such a system is also significant. Therefore, a cost analysis will need to be carried out in the future.

5 NOMENCLATURE

List of symbols:A Area m2

cp Specific heat capacity J/kgKh Enthalpy kJ/kgL Latent heat J/kgl Characteristic value mm Mass kgQ Heat JT Temperature Kt Time sV Volume m3

v Velocity m/sF Heat flux Wλ Thermal conductivity W/mKρ Density kg/m3

ΔT Temperature difference KΔt Time interval s

List of abbreviations:f Finalmax Maximumi Inletn Number of time intervalsj Certain timeo Outletl LiquidPCM Phase change materialm Melteds Solid

6 REFERENCES

[1] Chen, S.L., Chen, C.L., Tin, C.C., Lee, T.S., Ke, M.C. (2000). An experimental investigation of cold storage in an encapsulated thermal storage tank. Experimental Thermal and Fluid Science, vol. 23, p. 133-144.

[2] Lamberg, P. (2004). Approximate analytical model for two-phase solidification problem



in a finned phase-change material storage. Applied Energy, vol. 77, no. 2, p. 131-152.

[3] Simard, A.P., Lacroix, M. (2002). Study of the thermal behavior of a latent heat cold storage unit operating under frosting conditions. Energy Conversion and Management, vol. 44, p. 1605-1624.

[4] Seeniraj, R.V., Lakshmi Narasimhan, N. (2008). Performance enhancement of a solar dynamic LHTS module having both fins and multiple PCMs. Solar Energy, vol. 82, p. 535-542.

[5] Cabeza, L.F., Mehling, H., Hiebler, S., Ziegler, F. (2002). Heat transfer enhancement in water when used as PCM in thermal energy storage. Applied Thermal Engineering, vol. 22, p. 1141-1151.

[6] Stritih, U. (2004). An experimental study of enhanced heat transfer in rectangular PCM thermal storage. International Journal of Heat and Mass Transfer, vol. 47, p. 2841-2847.

[7] Lee, T., Hawes, D.W., Banu, D., Feldman, D. (1999). Control aspects of latent heat storage and recovery in concrete. Solar Energy Materials & Solar Cells, vol. 62, p. 217-237.

[8] Zalba, B., Marin, J.M., Cabeza, L.F., Mehling H. (2003). Review on a thermal energy storage with phase change materials, heat transfer analysis and applications. Applied Thermal Engineering, vol. 23, no. 3, p. 251-283.

[9] Darkwa, K., O`Callaghan, P.W. (2005). Simulation of phase change drywalls in a passive solar building. Applied Thermal Engineering, vol. 26, p. 853-858.

[10] Liu, H., Awbi, H.B. (2008). Performance of phase change material boards under natural

convection. Building and Environment, vol. 44, p. 1788-1793.

[11] Zhou, G., Zhang, Y., Kunping, L., Xiao, V. 2008. Thermal analysis of a direct-gain room with shape-stabilized PCM plates. Renewable Energy, vol. 33, p. 1228-1236.

[12] Ahmad, M., Bontemps, A., Sallee, H., Quenard, D. (2006). Experimental investigation and computer simulation of thermal behavior of wallboards containing a phase change material. Energy and Buildings, vol. 38, no. 4, p. 357-366.

[13] Zhou, G., Zhang, Y., Wang, X., Lin, K., Xiao, W. (2007). An assessment of mixed type PCM-gypsum and shape-stabilized PCM plates in a building for passive solar heating. Solar Energy, vol. 81, p. 1351-1360.

[14] Kuznik, F., Virgone, J., Noel, J. (2007). Optimization of a phase change material wallboard for building use. Applied Thermal Engineering, vol. 28, p. 1291-1298.

[15] Weinläder, H., Beck, A., Fricke, J. (2004). PCM-facade-panel for day lighting and room heating. Solar Energy, vol. 78, p. 177-186.

[16] Huang, M.J., Eames, P.C., Norton, B. (2003). Thermal regulation of building-integrated photovoltaic using phase change materials. International Journal of Heat and Mass Transfer, vol. 47, p. 2715-2733.

[17] Wang, F., Maidment, G., Missenden, J., Tozer, R. (2007). The novel use of phase change materials in refrigeration plant, Part 3: PCM for control and energy savings. Applied Thermal Engineering, vol. 27, p. 2911.

[18] Butala, V., Stritih, U. (2009). Experimental investigation of PCM cold storage. Energy and Buildings, vol. 41, no. 3, p. 354-359.


*Corr. Author’s Address: University of Coimbra’s Mechanical Engineering Research Center, Department of Electrical Engineering and Computers, University of Coimbra, 3030-290 Coimbra, Portugal, [email protected] 135

FPGA-Based Control System of an Ultrasonic Phased ArraySantos, M.J.S.F. - Santos, J.B.

Mário João Simões Ferreira dos Santos* - Jaime Batista dos SantosUniversity of Coimbra’s Mechanical Engineering Research Center, Department of Electrical Engineering

and Computers, University of Coimbra, Portugal

One of the main features of ultrasonic phased arrays is the ability to create an ultrasonic focused beam by applying time-delayed signals to each one of the elements of the array. If the focused beam can sweep a certain area it is possible to obtain a B-scan image. This process is called beamforming.

In this work, a prototype control system for a phased array, based in a FPGA (Field Programmable Gate Array), was conceived. The array has 30 elements and is to be used in B-scan low resolution imaging (50 × 50 pixels). Time-delayed was implemented by means of a 50 MHz oscillator, which allows a minimum delay of 20 ns. By using nine bits counters it is possible to obtain a maximum delay of 10.24 µs between control signals. The scanning sector area is 60 mm in depth and a ±30º aperture.©2011 Journal of Mechanical Engineering. All rights reserved. Keywords: ultrasonic imaging, phased array, B-scan, FPGA

0 INTRODUCTION

The ultrasonic waves are mechanical vibrations that can propagate through many different types of materials. Typically, in ultrasonic non-destructive testing (UNDT), an ultrasonic signal is transmitted into the material to be tested, after which this signal is collected and processed to obtain information that may be related to parameters or properties of that material. A classic application is the detection of defects or inclusions inside a given material: the ultrasounds when travelling between materials with different properties (interface material/defect), give rise to a reflected wave, which can be used for identification and classification of defects or inclusions. After a defect has been located in a given material it is often important to have information about their shape and size and then make decisions about its integrity. In the medical field, 2D US images are an important means of diagnosis essential to evaluate many clinical situations.

One of the most often used techniques is the ultrasonic B-scan imaging. This technique combines the information obtained by a one-dimensional A-scan method, related to the ultrasonic beam propagation inside the material, with a mechanical movement that produced an image parallel to the direction of propagation. The time-of-flight (travel time) of the sound energy

is displayed along the vertical axis and the linear position of the transducer is displayed along the horizontal axis. The mechanical movement made by the transducer is usually replaced by an electronic scanning of the beam, by means of a phased array [1] and [2]. In the most basic sense, a phased array system utilizes the wave physics principle of phasing. Varying the time between a series of outgoing ultrasonic pulses in such a way that the individual wave fronts generated by each element in the array combine with each other to add or cancel energy in predictable ways that effectively steer and shape the sound beam.

In the last decades there has been considerable advancement in the ultrasound systems function and their beamformers [3]. The first techniques used to implement the delays among the different elements of an array were based in analog systems like lumped elements, electromagnetic delay lines or charge transfer devices [4]. In modern ultrasound scanners a wide variety of beamforming approaches are applied using digital processing. Some of them use classical full phased array imaging and sweep out a sector image by beam steering across the image space, where all elements are active during both transmission and receiving [1]. Other developed techniques use a different number of active channels and different approaches: parallel receiving phase rotation [5], single-bit delta-sigma conversion [6], direct sampled in-phase/quadrature


136 Santos, M.J.S.F. - Santos, J.B.

beamformer [7] or phased subarray imaging [8]. Also, FPGA based systems have been developed where power dissipation [9] and compact size [10] are important project parameters.

In this work, a way to implement the delays to be provided to each of the elements of a linear array with 30 elements, designed to obtain low resolution ultrasonic B-scan images is presented. In the proposed implementation a low cost and high scale of integration solution, based on an FPGA xc3s1000 from Xilinx, was used.

1 ULTRASONIC B-SCAN IMAGING WITH PHASED ARRAYS

In most conventional ultrasonic inspections only one probe is used and the information gathered is related to propagation axis. If this monoblock probe is divided into several elements (array) with width much less than its length, we can assume that each of the elements leads to a linear source of cylindrical waves. The wave fronts of the array elements will interfere, generating an overall wave front [11]. The new wave fronts may be delayed and synchronized with each other in terms of phase and amplitude in order to create an ultrasonic beam with the possibility of focusing and steering. The individual control of each of the elements of the array is made to give rise to a focused beam, with the possibility of changing some parameters such as angle, the focal length or size. This process is controlled by dynamic software. The scanning of the focused beam allows detection of defects with several orientations, as well as outside of the propagation axis. These situations are illustrated in Fig. 1a: a single probe showing some limitations in the detection of

defects presenting the illustrated orientations or located outside the propagation axis.

To produce a beam with constructive interference, the various elements of the array must be excited with signals with small time differences between them. Fig. 2 shows the acquisition and control system for the transmitted (a) and received signals (b), in order to produce a focus on the desired point. The collected signals are shifted in time, as shown in Fig. 3 before being added. The result is an A-scan signal that emphasizes the response of the desired focal point and attenuates the echoes from other points of the testing material.

During the transmission, the acquisition unit sends a trigger signal to the control system, which converts this signal into a high voltage pulse with a time delay and width programmed according to the focal law. Each element receives only one pulse, giving rise to a beam with a specific angle and focal length. When the beam reaches the defect a reflection occur and the signal is collected by each one of the elements of the array, and time-shifted according to the parameters of the system. Finally, the signals are combined so that only one ultrasonic pulse is delivered to the acquisition system. Fig. 4 shows the beam focusing for an angled incidence.

There are several techniques of beam scanning to obtain an image [8]. Typically, in phased arrays with a high number of elements, a sequential excitation of a group of active transducers with the same delay is done along the array. In other cases, it may be a scan based on changes in focal length, with all elements to be used simultaneously and focalization on the receiving stage. Other techniques use a

Fig. 1. Detection of misoriented defects by: a) monocrystal probe; b) multielement probe


137FPGA-Based Control System of an Ultrasonic Phased Array

assumed that each of its elements will behave as a point source and the radiation diagram is omnidirectional in the plane of interest. For a homogeneous medium with constant wave speed, it is then necessary to determine the distances from each one element of the array to the focussing points. The resulting delays are obtained from the corresponding ratio between the distances and wave speed in the medium.

Fig. 5 depicts the geometry used to deduce the expression for calculating the delays to provide to a phased array with an even number of elements.

Fig. 5. Geometry of a linear phased array used to deriving the focusing formula

For a particular focalization point with polar coordinates (r, θ), it is possible to obtain the distances (distn) to each array element by the expression:

dist r r N n dn = + +−

−( cos ) ( sin ( ) )θ θ2 212

, (1)

where:r is the distance to the centre of the array [m],N is the number of elements of the array,n is the index of the array element,d is the distance between two consecutive elements [m].

Using an auxiliary variable defined as kn

k N n dn =−

−( )12

, (2)

focalization of all elements simultaneously with constant distance from the centre of the array and variable inclination.

Fig. 2. Acquisition and control system of the phased array: a) transmission; b) reception

Fig. 3. Delays in the reception

Fig. 4. Beam focusing for angled incidence

2 FOCUSING DELAY CALCULATION

To make the focusing of a phased array transmitted beam at a particular point, it is



the Eq. (1) can be simplified leading to:

dist r k r kn n n= + +2 22 sinθ . (3)

The delays can now be calculated by:

delaydist dist

cnn=

−max , (4)

where c is the wave speed in the medium [m/s], distmax is the maximum distance value [m].

With the present system, we intend to control an array with 30 elements having 1 mm separation between elements, capable to produce a scanning area from 10 to 60 mm in depth with an aperture of ±30º. Fig. 6 shows schematically these dimensions. The low resolution image has 2500 pixels (50×50), corresponding to 1 pixel/mm in the direction perpendicular to the array (depth) and a minimum of 1.35 pixel/mm in the angle direction.

Fig. 6. Scanning area

The situation where there are major differences between the delays is for the closer points to the array. For the minimum focalization distance (10 mm), the maximum delay between the central element and one of the extremes of the array can be easily calculated, and for the present case is 5.3 µs.

The delays are implemented based on synchronous counters which operate at a frequency of 50 MHz, allowing a minimum counting period (delay) of 20 ns. As it will be used 9 bit counters, maximum delay values of 29 × 20 ns = 512 × 20 ns = 10.24 µs are possible, which is about two times above the desired specifications.

3 DELAY IMPLEMENTATION

The implementation of the delays was done using a Xilinx FPGA xc3s1000 from Spartan-3 family. The Digilent programmable logic board presented in Fig. 7 and VHDL programming language were used for that goal. This board has high-speed USB2 port, together with a collection of I/O devices (VGA output, push-buttons, switches and LCD display), data ports and expansion connectors that allow a wide range of designs to be completed without needing any additional components.

The simplified block diagram of hardware implementation is depicted in Fig. 8. Each block represents a programming module and is controlled by the whole global program responsible for data loading.

Fig. 7. Programmable logic board

The Calc_sin module provides the sin values of the angle corresponding to the focalization point, relatively to the centre of the array. As VHDL language does not support the sin function, a solution often used in programmable devices that consists of the creation of a lookup table (LUT) with the 50 values was implemented, corresponding to the 50 points of the angular scanning (±30º).

To determine different distances from the focalization points to the elements of the array, the Eq. (3) will be used with the parameters n, d and N previously defined. Again the strategy of calculating the square root needs to be used as this is not supported by the language. In this



case the use of LUT is not adequate because it would consume many resources (50×50 values), so the alternative was the development of Eq. (3) in terms of Taylor series. This module is called Calc_dist. After calculating the distances, the module Calc_distmax determines the maximum of all distances.

Fig. 8. Simplified block diagram of the array control system

From this point, the system will have as many parallel channels as the number of elements of the array, which is 30 for the present system. From the set of distances previously calculated and using its maximum value, the delays will be calculated using the module Calc_delay_n, which in practice implements the Eq. (4). After the calculation of the delays, these values must be converted to multiples of the time unit of the system, which are 20 ns. As already mentioned, the system uses 9 bit counters, which allows a maximum number of 512 counts. The initial value to load each one counters, L_contn, is then given by:

L contdelay

nn_ = −512

20, (5)

where: delayn is delay of nth element [ns].

The greater the value of delayn, which indicates a higher proximity of the element to the focalization point, the lower the L_contn value to provide to the counter.

The final module Contn consists of a counter. After all L_contn signals have been loaded, a control signal sent to all counters (not represented) will start the count of each one from the original value L_contn. The control signals of the array Sn will change its state when each a

counter reaches the maximum count, leading to the desired shift to provide to the corresponding element.

For a new focalization point, the whole process is repeated, with the introduction of the angle and radius parameters.

4 RESULTS

At this stage the system was only tested for a focalization in a single point. Fig. 9 shows the timing diagram for the outputs Sn corresponding to a focalization point with an angle of +9° and a distance of 43.5 mm from the centre of the array. As can be seen, the shifting between the signals agrees with the predictions: the transducers further away from the focalization point are excited first (extremes of the array) and the closest are excited later (centre of the array). Note that in this case, the excitation of the elements 12 to 16 is simultaneous, which is due to the fact that the spatial difference between them is very small, causing differences in the propagation times less than 20 ns. Delays were measured and found in accordance with the theoretical values given by Eq. (4).

It was also possible to verify that only 40% of all resources (106 gates) of the FPGA have been used. Thus, this circuit can be used in situations where controlling of larger number of elements or analyzing of greater scanning areas are needed. These results can be seen as an improvement when compared with the ones provided by other authors [9] and [10].

5 CONCLUSIONS

In this work, a way to implement a phased array control to be used in low resolution ultrasonic B-scan imaging has been developed. Using a FPGA, the signals were successfully obtained to excite the array elements. The experimental results are in agreement with the analytical calculations.

One of the major advantages of the presented implementation is the great degree of miniaturization when compared with the use of conventional electronic circuits. The used FPGA occupies an area less than 3 cm2. It is also very versatile as the control system can be housed in



one standard FPGA that can easily be programmed and upgraded. Another advantage is related to the huge cost savings, because a simple laptop can be used to visualize the information.

The developed hardware allows controlling the array transmission. Future work will deal with developments concerning the reception of the collected signals and their processing, towards the formation of B-scan image of the interior of structures to be inspected.

6 REFERENCES

[1] Macovski, A. (1979). Ultrasonic Imaging Using Arrays. Proceedings of the IEEE, vol. 67, no. 4, p. 484-495.

[2] Von Ramman, O., Smith, S. (1983). Beam steering with linear arrays. Trans. on Biomedical Engineering, vol. BME-30, no. 8, p. 438-452.

[3] Thomenius, K.E. (1996). Evolution of ultrasound beamformers. IEEE Ultrasonic Symposium, San Antonio, p. 1615-1622.

[4] Manes, G.F., Atzeni, C., Susini, C., Somer, J.C. (1979). A new delay technique with application to ultrasound phased-array imaging systems. Ultrasonics, vol. 17, no. 5, p. 225-229.

[5] O´Donnell, M. (1990). Efficient parallel receive beam forming for phased array imaging using phase rotation (medical US application). IEEE Ultrasonic Symposium, Honolulu, p. 1495-1498.

[6] Kozak, M., Karama, M. (2001). Digital phased array beamforming using single-bit delta-sigma conversion with non-uniform over-sampling. IEEE Tran. Ultrason. Ferroelect. Freq. Control, vol. 48, p. 922-931.

Fig. 8. Timing diagram of the excitation signals for a focalization point with 9º inclination and 43.5 mm of distance, for the centre of the array



[7] Ranganathan, K., Santy, M., Blalock, T., Hossack, J., Walker, W. (2005). Direct sampled I/Q beamforming for compact and very low-cost ultrasound imaging. IEEE Tran. Ultrason. Ferroelect. Freq. Control, vol. 51, no. 9, p. 1082-1094.

[8] Johnson, J., Karaman, M., Khuri-Yakub, B. (2005). Coherent-array imaging using phased subarrays. Part I: Basic principles. IEEE Tran. Ultrason. Ferroelect. Freq. Control, vol. 52, no. 1, p. 37-50.

[9] Feldkamper, H, Schwann, R., Gierenz, T., Noll, T. (2000). Low power delay calculation

for digital beamforming in handheld ultrasound systems. IEEE Ultrasonic Symposium, San Juan, p. 1763-1766.

[10] Tomov, B., Jensen, J. (2001). A new architecture for single-chip multi-channel beamformer based on a standard FPGA. IEEE Ultrasonic Symposium, Atlanta, p. 1529-1533.

[11] R/D Tech, (2004). Introduction to Phased Array Ultrasonic Technology Applications. R/D Tech Corp, Waltham.


*Corr. Author’s Address: University of Maribor, Faculty of Mechanical Engineering, Smetanova 17, 2000 Maribor, Slovenia, [email protected]

Real-Time Cutting Tool Condition Monitoring in Milling

Franci Čuš - Uroš Župerl* University of Maribor, Faculty of Mechanical Engineering, Slovenia

Reliable tool wear monitoring system is one of the important aspects for achieving a self-adjusting manufacturing system. The original contribution of the research is the developed monitoring system that can detect tool breakage in real time by using a combination of neural decision system and ANFIS tool wear estimator. The principal presumption was that force signals contain the most useful information for determining the tool condition. Therefore, the ANFIS method is used to extract the features of tool states from cutting force signals. ANFIS method seeks to provide a linguistic model for the estimation of tool wear from the knowledge embedded in the artificial neural network. The ANFIS method uses the relationship between flank wear and the resultant cutting force to estimate tool wear. A series of experiments were conducted to determine the relationship between flank wear and cutting force as well as cutting parameters. Speed, feed, depth of cutting, time and cutting forces were used as input parameters and flank wear width and tool state were output parameters. The forces were measured using a piezoelectric dynamometer and data acquisition system. Simultaneously flank wear at the cutting edge was monitored by using a tool maker’s microscope. The experimental force and wear data were utilized to train the developed simulation environment based on ANFIS modelling. The artificial neural network, was also used to discriminate different malfunction states from measured signals. By developed tool monitoring system (TCM) the machining process can be on-line monitored and stopped for tool change based on a pre-set tool-wear limit. The fundamental limitation of research was to develop a singlesensor monitoring system, reliable as commercially available system, but 80% cheaper than multisensor approach.© 2011 Journal of Mechanical Engineering. All rights reserved. Keywords: end-milling, tool condition monitoring (TCM), wear estimation, ANFIS

0 INTRODUCTION

The demand to reduce production costs has driven manufacturers to automate most operations previously controlled by skilled operators. Therefore, the Unmanned Flexible Manufacturing Systems (UFMS) have been developed. In such automated and unmanned machining system, a computerized system must have capabilities for monitoring and controlling the machining process to perform the role of a human operator. A tool condition monitoring system (TCM) is a fundamental requirement for the control of the machining process.

The main goal of developing TCM systems is to increase productivity and hence competitiveness by maximizing tool life, minimising down time, reducing scrappage and preventing damage. The traditional ability of the operator to determine the condition of the tool based on their experiences and senses is now the expected role of the monitoring system. The role of the operator is typically supervisory. Usually

the operator is also responsible for loading and unloading parts for several machines in a manufacturing cell, meaning that their reaction time to a problem with any one machine will not be sufficient for the speed at which machining operations take place on modern machine tools.

Each TCM system consists of: sensors, signal conditioners/amplifiers and a monitor [1]. The monitor uses a strategy to analyse the signals from the sensors and to provide reliable detection of tool and process failures. It can be equipped with some signal visualisation system and is connected to the machine control.

Many studies have been conducted on monitoring of malfunctions and abnormal cutting states of machine tools [2]. With regard to the monitoring of cutting tool states, two main factors are tool wear and failure. Tool failure has become more important recently since hard tools are frequently used in the cutting process. Current research in TCM is oriented towards the development of online TCM techniques.


143Real-Time Cutting Tool Condition Monitoring in Milling

There are two techniques for tool wear sensing: direct and indirect. The direct technique includes measuring the actual wear, using radioactive analyses of the chip. Generally direct measurements are avoided because of difficulty of online measurements. For indirect methods of TCM, the following steps are followed: use of single or multiple sensors [3] to capture process information; use of signal processing methods to extract features from the sensor information; use of decision-making strategy to utilise extracted featured for prediction of tool failure. Indirect technique includes the measuring of cutting forces, torque, vibration, acoustic emission (stress wave energy), sound, temperature variation of the cutting tool, power or current consumption of spindle or feed motors and roughness of the machined surface [4]. Recent trend in TCM is a multisensory approach which is termed as sensor fusion /sensor integration/sensor synthesis. The idea is to gather information from several sensors to make a comprehensive estimate of tool wear. The application of TCM in the industry has mostly relied on robust and reliable sensor signals such as force, power and AE. They are relatively easy to install in existing or new machines, and do not influence machine integrity and stiffness.

Recent studies show that force signals contain the most useful information for determining the tool condition [5]. However, in many cases the use of force sensors is not practical for retrofit applications and spindle power signal is often used as an alternative.

Several different approaches have been proposed to automate the tool monitoring function. These include classical statistical approaches as well as fuzzy systems and neural networks. For instance, Iqbal [6] developed an approach based on the least-squares regression for estimating tool wear in machining while Haber [7] has measured the flank wear of the cutting tool using computer vision. The capacity of artificial neural networks to capture nonlinear relationships in a relatively efficient manner has motivated Chien and Tsai [8] to apply these networks for developing tool wear prediction models. But in such models, the nonlinear relationship between sensor readings and tool wear embedded in a neural network remains hidden and inaccessible to the user [9]. In this research we attempt to solve this situation by

using the Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the flank wear of the tool in end-milling process. This model offers an ability to estimate tool wear as its neural network based counterpart but provides an additional level of transparency that neural networks fails to provide. Then a neural network is used as a decision making system to predict the condition of the tool. In this study, the cutting forces are used as an indicator of the tool flank wear variation.

1 MONITORING SYSTEM COST AND SENSORS JUSTIFICATION

The ability of a TCM system relies on two basic elements: first, the number and type of sensors used and second, the associated signal processing and simplification methods utilised to extract the necessary important information from machining signals [3]. The first element involves expensive hardware which influences the cost of the system, whereas the second element affects the efficiency and the speed of the system. The main issue here is to design a condition monitoring system with high efficiency, short development time, and with a reduced number of sensors. This basically includes a selection of sensors and associated signal processing methods which provide the minimum classification error of process faults.

Most commonly used, in TCM systems, are sensors measuring cutting force components or quantities related to cutting force (power, torque, distance/displacement and strain). They are relatively easy to install in existing or new machines, and do not influence machine integrity and stiffness. Piezoelectric force sensors are well adjusted to harsh machine tool environment.

Monitoring systems developed in laboratories, are often multisensor systems embodying complex AI-based strategies to integrate information, extract features and make more reliable decisions on the state of the tool. In commercially available systems, the one sensor–one tool approach dominates. Multisensor here means providing the best sensor for each application. Only one manufacturer “wear estimator” uses more than one signal for monitoring the wear of one tool (exclusively for turning).


144 Čuš, F. - Župerl, U.

tool state. If the tool condition is good, the peak measurement of each tooth’s force should be roughly the same during one revolution of the cutter. If a tooth is broken, it generates a smaller peak force because it carries a smaller chip load. As a result, the tooth that follows a broken tooth generates a higher peak force as it extracts the chip that the broken tool could not. One main force principle can be used to detect tool condition: Maximum peak force in each revolution should differ between good and broken tools [10]. Maximum peak force of a broken tool must be larger than that of a good tool.

Fig. 1. Cutting force signal of a good and damaged cutter (cutter diameter = 16 mm)

Fig. 1 illustrates the diagram of undamaged and broken tools.

Applying these principles, an in-process tool breakage monitoring system was developed for end milling operations. The cutting forces and machining parameters were selected as input factors.

3 METHODOLOGY AND SYSTEM COMPONENTS

The proposed approach consists of two main steps: First, an ANFIS model of tool wear is developed from a set of data obtained during actual machining tests performed on a Heller milling machine using a Kistler force sensor. The trained ANFIS model of tool wear is then subsequently merged with a neural network for estimating tool wear condition (fresh, worn). Fig. 2 shows the basic architecture of the proposed system.

The goal of developing the TCM is not only to produce a reliable monitoring system, but also to keep the system as cheap as possible. In order to do this, the utilisation of sensors in the system should be kept relatively low (i.e. there is no need to maximise the number of sensory characteristic features used from an implemented sensor). The sensor utilisation factor (SUF) [3] is a function of: the number of sensory features used from the sensor (NSF), the total number of sensory features in the system (TNSF) and the number of signals which can be physically produced by the sensor (NS). It is found out that SUF factor is very useful in reducing the cost of the TCM by removing sensors which do not produce sufficient useful signals. The cost analysis is calculated using the variable cost of the system (costs of sensors). The fixed costs such as the PC, data acquisition card, and the software cost should be added to the nominal variable cost to acquire the total cost of the system. If a cheaper TCM with good performance is needed it is essential to make a compromise between cost and systems performance.

From our calculation it is obvious that the force dynamometer is the most utilised sensor. It has been found that the proposed sensor system has the lowest cost of 4,612 € but has an error of 20.21%. On the other hand the multisensor approach has an error of 9.01% but it has a high nominal cost of 23,524 €. Comparing both systems it can be seen that an improvement of 11.20% has caused an increase in the system cost of 18,912 €. Therefore, it is essential to compromise between cost and performance of the systems if a cheaper system with good performance is needed. The new system is 18,912 € cheaper than the cheapest multisensor monitoring approach.

2 PROBLEM DEFINITION

End-milling is interrupted cutting process, which means that each cutting tooth generates a cyclic cutting force ranging from negative to maximum force, and back to negative. This force is graphed as a series of peaks (Fig. 1).

Cutting parameters and tool conditions affect the magnitude of resultant force. Therefore, the resultant force FR, generated from X and Y directions, is used in this experiment for detecting



This is a typical TCM system where the sensor is used to collect the signals during milling through a data acquisition module. The signal processing module analyses the machining signals for extracting features sensitive to tool wear. The features together with the machining parameters constitute the data set to be used as input to the decision system and estimator. The main purpose of the decision system and estimator is to map the input features to the current state of tool .i.e. the amount of tool wear.

A multi-layer perceptron neural network with backpropagation algorithm is used in TCM as a decision system due to its ability of learning, noise suppression and parallel processing. A random pattern classifier module divides the data into training and testing set. The training set is used for learning the purpose while the testing set is used for testing the decision system performance.

3.1 ANFIS Based Tool Wear Predictor

The relationship between the machining parameters/sensor signals and flank wear is first captured via a network and is subsequently reflected in linguistic form with the help of a fuzzy logic based algorithm. The estimation design process consists of a linguistic rule construction, partition of fuzzy subsets and the definition of the membership function shapes. It uses training examples as input and constructs the fuzzy if-then rules and the membership functions (MF) of the

fuzzy sets involved in these rules as output. This process is called a training phase.

In this model, we adopted two different types of membership functions for analysis in ANFIS training and compared their differences regarding the accuracy rate of the flank wear prediction. After training the estimator, its performance was tested under various cutting conditions. The performance of this method turned out to be satisfactory for evaluating flank wear, within a 5% mean percentage error. Generally, a worn tool is not a catastrophic event and when detected, it is usually possible to continue machining to the end of the current operation.

3.1.1 ANFIS Arhitecture and Learning Method

Using a given input/output data set, the ANFIS method constructs a fuzzy inference system (FIS) whose membership function parameters are adjusted using the backpropagation algorithm. This allows fuzzy systems to learn from the data they are modeling. FIS Structure is a network-type structure, which maps inputs through input membership functions and associated parameters, and then through output membership functions and associated parameters to outputs.

Fig. 3 shows the fuzzy rule architecture of ANFIS when the triangular membership function is adopted. The architectures shown in Fig. 3 consist of 31 fuzzy rules.

ANFIS applies Hybrid Learning method for updating parameters. For premise parameters

Fig. 2. Architecture of tool condition monitoring system



that define membership functions, ANFIS employs gradient descent to fine-tune them. For consequent parameters that define the coefficients of each output equations, ANFIS uses the least-squares method to identify them.

This approach is thus called Hybrid Learning.

Fig. 3. Components of TCM (in-process ANFIS predictor and ANN decision system)

3.1.2 ANFIS Modeling Algorithm

Modeling process starts by selecting a data set (input-output data pairs) and dividing it into training and testing data sets. The training data set is used to find the initial premise parameters for the membership functions by equally spacing each of the membership functions. A threshold value for the error between the actual and desired output is determined. The consequent parameters are found using the least-squares method. Then an error for each data pair is found. If this error is larger than the threshold value, update the premise parameters using the gradient decent method

as the following (Qnext=Qnov+ηd, where Q is a parameter that minimizes the error, η the learning rate, and d is a direction vector). The process is terminated when the error becomes less than the threshold value. Then the testing data set is used to compare the model with actual system.

During training in ANFIS, 150 sets of experimental data were used to conduct 500 cycles of learning.

The findings are analyzed and discussed in the fourth chapter.

3.2 Neural Decision System Development

A neural decision-making system was developed in Matlab software. The neural network used to predict the cutting tool condition is shown in Fig. 3. It has tool-breakage detection capability and is based on pattern recognition. The neural network stores a number of reference force patterns that are characteristic of tool breakage. When a tool tooth breaks, cutting force suddenly rises for a while, and then drops to zero. The system continuously monitors the signal for the break pattern. If a pattern is identified, a break is declared within 10 ms of the breakage.

Four steps are required to develop a neural decision system. In step one, network architecture and prediction factors were selected. The network has two hidden layers and uses a set of 5 normalized inputs for tool condition prediction: (1) cutting speed, (2) feed rate, (3) depths of cut, (4) forces, (5) tool wear. Output layer consist of only two neurons: (1) normal and (2) broken/worn.

In step 2 the learning rate, momentum factor and the number of hidden layers/hidden neurons were defined. The number of hidden neurons was set at 12, the learning rate was set at 1, and the momentum item was 0.4. The number of training/testing cycles was 1700.

In step 3 the data set was divided into training and testing set. 200 data points were used in this research. Good tools collected half of these and broken tools collected the rest. All the data were scaled.

In step 4 the training and testing faze is accomplished. During the training stage, the neural network adjusted its internal weight values to give correct output results according to the input



features. Finally, in the last step the trained neural network was used to predict tool conditions.

4 EXPERIMENTAL DESIGN

Monitoring experiments were performed on a HELLER machine tool (type BEA1) with FAGOR CNC controller. It involved an end-milling process of steel parts using two end-mill cutters [11]: normal and on tooth broken. The cutting tool used in the machining test was a solid end-milling cutter (R216.24-16050 IAK32P) with four cutting edges. The tool diameter was 16 mm. Its helix angle was 10°. The corner radius of the cutter was 4 mm. The insert had an outer coated layer of TiN featuring low friction and welding resistance.

The workpiece material used in the machining test was Ck 45 and Ck 45 (XM) with improved machining properties. Workpieces were cut off from a warm-rolled bar. The dimension of the workpiece was 200 × 70 × 70 mm. The workpiece was mounted in a 3 component piezoelectric dynamometer (Kistler 9255) to monitor the cutting forces in the X and Y directions. Force dynamometer was mounted on the machining table and connected to a 3-channel charge amplifier. The signals were monitored using a fast data acquisition card (National Instruments PC-MIO-16E-4) and software written with The National Instruments CVI programming package.

The force measurements were sampled at 15000 points/second, then digitally low-

pass filtered at a cut-off frequency of 400 Hz to eliminate the high-frequency components resulting from the machine tool dynamics.

The measured force signals were digitized using an A/D converting board at 1 kHz sampling rate for each channel. The experimental set-up is shown in Fig. 2.

The flank wear was observed during the experiments. The cutting tool flank wear was discontinuously measured with a tool microscope of 0.01 mm accuracy. The experiments were carried out for all combinations of the chosen cutting parameters and tool wear.

In the experiments the cutting parameters were set as [12]: four level of feed rate (f1 = 0.05, f2 = 0.25, f3 = 0.35, f4 = 0.45 mm/tooth), four level of spindle speed (n1 = 200, n2 = 360, n3 = 340 and n4 = 480 min-1) and three levels of radial/axial depth of cut (RD1 = 1d, RD2 = 0.5d, RD3 = 0.25d; AD1 = 2, AD2 =4, AD3 = 8 mm; d = 16 mm cutting parameter). Parameters such as tool diameter, rake angle, etc. are kept constant.

The sampling frequency was 400 Hz and total numbers of 83 data points were used for signal processing at the spindle speed of 360 min-1, and 45 data points at 580 min-1. The number of data points is the total sampled during one revolution of a spindle with a 0.0025 s sampling interval.

5 RESULTS AND DISCUSSION

In-process sensing technique in connection with decision-making system is essential for

Table 1. Partial results of TCM testing (ANFIS wear prediction and ANN tool condition estimation)

Tool conditions

Input factors ANN outputsANN

Prediction

ANFIS Prediction WB [mm]

F [N]

N [min-1]

F [mm/rev]

AD [mm]

RD [mm] ANN1 ANN2

Normal 427.2 440 0.17 1.2 8 0.9 0.1 Normal 0.11Broken 777.9 440 0.17 1.2 8 0.02 0.98 Broken 0.24Normal 433.9 440 0.13 1.4 8 0.3 0.7 Broken 0.17Broken 729.6 440 0.13 1.4 8 0 1 Broken 0.26Normal 650.5 440 0.20 1.4 8 0.89 0.11 Normal 0.13Broken 925.7 440 0.20 1.4 8 0 1 Broken 0.27Normal 614.4 480 0.20 1.4 8 0.88 0.12 Normal 0.15Broken 751.9 480 0.20 1.4 8 0.03 0.97 Broken 0.23Normal 904.3 360 0.22 1.6 8 0.89 0.11 Normal 0.14Broken 991.9 360 0.22 1.6 8 0 1 Broken 0.31



the successful operation of TCM. The neural network was capable of detecting tool conditions accurately in real time. The accuracy of training data was 98.1%, and the accuracy of testing data was 94.9%. The results of neural network testing are shown in Table 1. The output node value of a back-propagation neural network was mapped as 0.01 for the normal cutting state, and 0.99 for the tool breakage.

When the neural network outputs are over 0.9 (tool breakage), it sends the signal “Tool broken” to the PC. When both the neural network outputs are below 0.9, it sends the signal “Tool condition Normal”.

Fig. 4. Thrust force of a) normal and b) broken tool in real time monitoring, c) indicative tool

breakage force pattern with limits

Figs. 4a and b represent the cutting force signals for the normal and broken cutter. Cutting conditions used in the experiments are: spindle speeds of 360 min-1, and feed rates of 187 mm/min. Neural network takes 0.1575 s to accumulate 83 data points in one buffer using the

sampling time of 0.0025 s under the cutter rotation of 360 min-1. Total processing time is 0.143 s for processing data points in a buffer, identifying the states in neural networks and sending/presenting a result to a computer.

The developed decision system incorporates simple fixed limits for tool breakage detection. Limits are: L1 (collision), L2 (tool fracture), L3 (worn tool) and L4 (missing tool limit).

In the future it will be appropriate to replace fixed limits with self-adjusting limits. The detection system demonstrated a very short response-time to tool conditions. Since tool conditions could be monitored in real-time, the worn tool could be replaced immediately to prevent damage to the product and machine. In this research ANFIS system is used to predict the flank wear of the cutter in an end-milling process. A total of 150 sets of data were selected from the total of 300 sets obtained in the end-milling experiments for the purpose of training in ANFIS. The other 150 sets were then used for testing after the training was completed to verify the accuracy of the predicted values of flank wear. The experimental results indicate that the proposed ANFIS model has a high accuracy for estimating flank wear with small computational time. The following conclusions can be drawn from the analysis: 1. The flank wear could efficiently be predicted

by using cutting conditions and forces as the fuzzy input variables in ANFIS system.

2. The error of the tool wear values predicted by ANFIS with the triangular membership function is only 4%, reaching accuracy as high as 94%. When the trapezoidal membership function is adopted the average error is around 5.4%, with an accuracy of 92%.

3. The ANFIS system could predict flank wear for different cutting conditions with an average percentage deviation of 4.72%, or an accuracy of 93.64%.

4. The predicted flank wear was found to be significantly sensitive to the measured maximum cutting forces (radial), especially the thrust cutting component (Fx).

Fig. 5 shows the scatter diagram of the predicted values and measurement values of



the flank wear of 150 sets of testing data when triangular membership functions are used in ANFIS.

Fig. 5. Scatter diagram of measured WB and predicted for the testing data using the triangular

membership function

Fig. 6. Comparison of measured and predicted flank wear (v=180 m/min, AD=2 mm,

f = 0.1 mm/tooth)

It shows that the predicted values of flank wear between 0.15 and 0.4 all follow the 45° line very closely. In other words, the predicted values are not far from the experimental measurement values. Fig. 6 compares the predicted values and measurement values after training by ANFIS with triangular membership functions.

6 CONCLUSION

We developed a system for monitoring tool condition in real time and obtained the following results through verification experiments: (1) The proposed monitoring system of cutting process may be very useful because of its parallel

processing capability; (2) It enables the monitoring of the cutting process with high reliability; ANFIS component can estimate flank wear progress very quickly and accurately, once the maximum cutting forces are known. A monitoring system using a neural network is able to classify the various cutting states such as tool breakage, and tool wear. In the future different decision making tools, such as fuzzy logic should be applied to see which one could obtain a smaller error of detection.

7 REFERENCES

[1] Fu, P., Hope, A.D. (2008). Intelligent Classification of Cutting Tool Wear States. Advances in Neural Networks, vol. 39, p. 1611-3349.

[2] Mulc, T., Udiljak, T., Čuš, F., Milfelner, M. (2004). Monitoring cutting- tool wear using signals from the control system. Strojniški vestnik – Journal of Mechanical Engineering, vol. 50, no. 12, p. 568-579.

[3] Kuo, R.J. (2003). Multi-sensor integration for on-line tool wear estimation through artificial neural networks and fuzzy neural network. Engineering Applications of Artificial Intelligence, vol. 3, p. 49-261.

[4] Achiche, S., Balazinski, M., Baron, L., Jemielniak, K. (2008). Tool wear monitoring using genetically-generated fuzzy knowledge bases. Engineering Applications of Artificial Intelligence, vol. 15, p. 303-314.

[5] Kopač, J. (2002). Cutting forces and their influence on the economics of machining. Strojniški vestnik – Journal of Mechanical Engineering, vol. 48, no. 3, p. 72-79.

[6] Iqbal, A., He, N., Dar, N.U., Li, L. (2009). Comparison of fuzzy expert system based strategies of offline and online estimation of flank wear in hard milling process. Expert Systems with Applications, vol. 33, p. 61-66.

[7] Haber, R.E., Alique, A. (2005). Intelligent process supervision for predicting tool wear in machining processes. Mechatronics, vol. 13, p. 825-849.

[8] Chien, W.T., Tsai, C.S. (2005). The investigation on the prediction of tool wear and the determination of optimum cutting conditions in machining 17-4PH stainless



steel. Journal of Materials Processing Technology, vol. 140, p. 340-345.

[9] Čuš, F., Župerl, U. (2007). Adaptive self-learning controller design for feedrate maximization of machining process. Advances in Production Engineering & Management, vol. 2, no. 1, p. 18-27.

[10] Smaoui, M., Bouaziz, Z., Zghal, A. (2008). Simulation of cutting forces for complex surfaces in ball-end milling. International Journal of Simulation Modelling, vol. 7, no. 2, p. 93-105.

[11] Sirkant, R., Subrahmanyam, S., Chen, K., Krishna, V.P. (2010). Experimental selection of special geometry cutting tool for minimal tool wear. Advances in Production Engineering & Management, vol. 5, no. 1, p. 13-24.

[12] Milfelner, M., Župerl, U., Čuš, F. (2004). Optimisation of cutting parameters in high speed milling process by GA. International Journal of Simulation Modelling, vol. 3, no. 4, p. 121-131.


*Corr. Author’s Address: University of Montenegro, Faculty of Mechanical Engineering, 81000 Podgorica, Cetinjska bb, Podgorica, Montenegro, [email protected] 151

Improvement of Business Processes Performances through Establishment of the Analogy:

Quality Management System – Human Organism

Aleksandar Vujovic1,* – Zdravko Krivokapic1 – Mirko Sokovic2

1 Faculty of Mechanical Engineering Podgorica, University of Montenegro, Montenegro 2 Faculty of Mechanical Engineering, University of Ljubljana, Slovenia

This paper establish a model for improvement of business processes performances based on quality management system (QMS) through a comparison with top organizational performances characterized by criteria i.e. particularities of the business excellence (BE) model. Analogy between QMS and human organism has been established to that effect. This analogy develops into two directions, one of which is used and presented in this paper (related to the analogy with willing functions of the human organism). Based on identification, measurement and analyses of occurrences of nonconformities in relation to requests of ISO 9001 standard, and based on the analogy with the human organism, conclusions about the readiness of organizations of productive and service type in terms of achieving BE have been drawn and areas and priorities regarding the improvement with the aim of achieving top performances have been identified. © 2011 Journal of Mechanical Engineering. All rights reserved. Keywords: improvement processes performances, analogy, decision support system, degree of readiness

0 INTRODUCTION

The main objective of this paper is to find an analogy through a comparison between an organization and human body function and to develop an appropriate model for performance improvement of the organization [1]. Applying this model points out crucial points in organization (product/service) which should be improved by priority and tend to gain BE.

There are numerous studies that deal with the research of benefits and disadvantages in systems with implemented QMS. Premises on the insignificance of the system of quality regarding the improvement of performance are based on allegations that by that system, procedures are over-emphasized through an excessive care of implementation or non-coverage by procedures and real quality is neglected [2] to [4]. However, most research works point to real benefits of the ISO 9001 implementation, contrary to those who claim that the price of implementation and maintenance of QMS is bigger than profits realized by it [5] to [8]. There are negative premises in literature related with total quality

management (TQM) model regarding influence on organizational performances, as it is also the case with the ISO 9001 model. Such premises point to its inapplicability, and therefore, in this paper and in the idea of association of the ISO 9001 and the BE model in direction of the improvement, comparison with performances of organizations that have won an award for excellence as a measure of level of the TQM implementation, was pointless. Therefore, the authors [9] and [10] have chosen to point here to pessimistic attitudes and to promote optimistic premises through review and analysis of literary sources related to that subject. Premises that TQM has no efficiency regarding organizational performances have been included. These premises are accompanied by research works that indicate the difficulty or near impossibility of establishing a relation between TQM and organizational values and the belief that such a relation is unreal [11] and [12].

There are many studies that indicate how TQM model implemented into organizational management is not just effective but also efficient even in terms of financial results of the organization [13] to [17].


152 Vujovic, A. – Krivokapic, Z. – Sokovic, M.

On such a defined basis and assertions that present the majority in literary sources, and point to the positive influence of the ISO 9001 and TQM on organizational performances even in the part of finance, an opportunity emerges that improvement in these models would directly contribute to the improvement of organizational performances. Every nonconformity i.e. disagreement with requests of the ISO 9001 model brings results in weakening of quality management system performances and thereby organizational performances. Hereby conditions are created to identify nonconformities with a term of error and apply the theory of learning based on errors or CBR (Case Based Reasoning) in this paper. Through the application of this theory or learning based on experiences, a system to predict the possibility of error occurrence in the identified areas of the ISO 9001 and to present measures for preventive action in that direction as to indicate possibilities and places for improvement for organizations, is being developed.

Through the established analogy with the human organism and division of its activity to willing and unwilling, an opportunity is created to act upon the analogy with e.g. sportsman, and to make a comparison between middle-class sportsmen (QMS) and top-class sportsmen (system with the award for BE) according to these activities (the ISO 9001 requests). Therefore, it is possible to point to areas that are critical in view of performances and indicate priorities regarding improvements with the aim of achieving top performances.

1 ESTABLISHMENT OF THE ANALOGY BETWEEN THE QMS AND THE HUMAN

ORGANISM

This paper attempts at dealing with the perfection of functioning of the human body compared with a process modelling structures of the implemented QMS. The challenge was to create a system that is universal for all sizes of organization, which incorporate a large number of gathered data, or a large number of experiences, in order to get a better image of the system status. This should be added to the primary goal which is to develop a model for an improvement of the management system, aimed at achieving BE

according to show off how to maintain and improve the performance of the human body. However, the goal is also to develop a system for measuring the performance and capacity of each activity in the QMS, in order to obtain a true picture of the systems and capabilities in order to define the areas where improvements should be made with a clearly defined intensity of improvement. On this basis, the established analogy is made to compare the elements of implemented QMS to the systems that have been applied for the Quality Award for BE as a system with high performance. A comparison is made from two aspects; the aspect of errors and in terms of significance of achieving BE. Therefore, indicators about the performance of the system and possible action for improving the system are achieved.

The reason for the application of this approach may be to determinate and establish the links between QMS and artificial intelligence (AI) applications. In this way a basis for developing systems that simulate the human way of its functioning and reasoning is created. These activities are carried out especially in the field of expert systems. Expert systems are highly rated as the most commercialized branch of AI [18] and [19]. These systems are particularly suitable for organizations that tend to BE and the implementation of TQM strategy [20] and [21].

The authors [20] also have conducted a detailed analysis of the 143 software in the field of quality. The results indicate that the software in the field of quality is usually aimed at the control of documentation, statistical control and analysis, Six Sigma model, corrective action, flowchart and mapping process. Consequently, these represent a kind of automation tool for: the implementation process of document management system, describing the information flows, implementation methods and techniques of quality and more. Thus, it can be concluded that there is no software that is based on the application of AI-tools in terms of defining preventive actions for the improvement of processes.

These views and attitudes were in agreement: that there is no correct programming software that has a strong base of knowledge that could assist in identification of a problem, that not a single expert system that deals with the measurement, evaluation, corrective and


153Improvement of Business Processes Performances through Establishment of the Analogy...

their very existence. Those are functions that are the same in all professions and all the people (considering that they exist, i.e. that an organism is healthy) and do not depend on a man‘s will but are simply executed. For example, those are the functions of secreting enzymes, hormones, heartbeats, and similarly. They regulate the functions of an organism and those that cannot be controlled [31] and [32]. With such a ratio of functions in the organism, an analogy of the system with implemented quality management system can be established. In order to meet requirements of this paper, only an analogy in terms of willing functions has been considered. An analogy that regards willing functions goes in the direction of classifying organizations into productive and service organizations (Fig. 1), as to: • Identification of weak spots, namely

organizational performances through acquisition of nonconformities and isolation of critical areas by the Pareto method;

• Comparison with top organizational performances in accordance with the assessment applied in the competition for the Oscar for quality award (Serbian national quality award);

• Analysis and conclusion on capacities of production or service organizations for achieving BE.

preventive action to improve organizational performance and the like has been developed [22] to [24]. Such analogies give incentive to create a foundation to set up and enter the field of AI in order to obtain knowledge as one of the most important factors for creating competitiveness in the market [25] to [28]. In need to establish the analogy between the process modulated organizational structure and the human organism, so as to create the system that is independent from organizational functions and based only on the process model, the following division of man functions was made [29] and [30]:• willing and • unwilling functions.

Willing functions (term “functions” is used in medical terminology, although it is equally correct, to use a term “activities” in view of the ISO 9000 terminology. For reasons of consistent referencing and the use of theories from the field of medicine, the authors have chosen to use the term functions) are those dependent on man’s profession and performed by man’s will. They are variable and dictated by a central control of the organism. For example, when a worker at the construction site lifts his hand, this is not the same as when a referee lifts his hand and etc. Willing functions refer to functions of external motoric organs.

The second category is made of unwilling or automatic functions and their use is given by

Fig. 1. Analogy with the human organism in order to improve organizational performance



The analogy established like this justifies the approach to work, which is, just as it is with the man, to monitor process performances in process modulated structure and based on, in medical terms, their condition and diagnostics of non-functionalities, to perform preventive actions in order to maintain the top form.

All functions (willing and unwilling) exist in the organism i.e. they are carried out, under condition that it is a healthy organism, or in better said an organism whose organs perform their given functions. Analogically with that, systems observed from the standpoint of this paper are certified quality management systems, which meet all requirements i.e. perform all functions provided by the ISO 9001. That is confirmed by the certificate relating to the mentioned referential.

On the other hand, willing functions are carried out by all organisms, but in a different manner depending on activity or profession. Thereby space is created to observe quality management systems and functions they perform based on analogy in different ways, depending on system activities. This justifies a tendency to monitor productive and service organizations in order to reach conclusions about specificities and abilities to achieve BE.

2 APPLICATIONS OF ANALOGY AND DATA ANALYSIS

Unique database consisting of 1009 non-conformities, identified in over 350 organizations was created to meet the demands of this paper, and in that way, a basis was found to carry out an analysis with the aim of defining critical areas, specificities of an organization and guidelines for an improvement in the direction of defining preventive measures and a decrease of corrective action that would correspond to the ISO 9001.

The term nonconformities refer to any nonconformance of requirements of ISO 9001, nonconformity - non-fulfilment of a requirement [33]. During the external audits of QMS, competent and trained auditors can identify several types of nonconformities. The distribution of nonconformities depends on the rules that define the certification body itself. However, for the purpose of this paper classification, which is most common in the literature [34], favoured by

authoritative schools in the field of management system and clearly recommended by European guidelines in the subject area, which is split into three levels, is used. The first level is the disagreements that are evaluated as insignificant deviations from the standards and requirements which are interpreted as an oversight or random error. The other two categories are interpreted as nonconformities that represent a great deviation from the essential requirements, which are reflected in the frequent discrepancies in individual requirements, representing a deviation that puts in doubt the stability of the management system and threatens the operations of the organization.

The data base of nonconformities which is under consideration in this paper contains only nonconformities in the domain of the other two categories, and that giving greater importance to this research and gives more significant results. Non-conformances are identified in accordance with the structure requirements defined in the ISO 9001 as follows:• Quality management systems: 4.1-General

requirements, 4.2-documentation requirements,

• Management responsibility: 5.1-management commitment, 5.2-customer focus, 5.3-quality policy, 5.4 -planning, 5.5-responsibility, authority and communication, 5.6-management review,

• Resource management: 6.1-provision of resource, 6.2-human resources, 6.3-infrastructure, 6.4-work environment,

• Product realization: 7.1-Planning of product realization, 7.2-customer-realated processes, 7.3-design and development, 7.4-purchasing, 7.5-production and service provision, 7.6-control of monitoring and measuring devices,

• Measurement, analysis and improvement: 8.1-general, 8.2.1-customer satisfaction, 8.2.2-internal audit, 8.2.3-monitoring and measurement of processes, 8.2.4-monitoring and measurements of product, 8.3-control of nonconforming product, 8.4-analysis of data, 8.5-improvement.

Accordingly, in the field of 8.2.1 from the standpoint of the appearance of non-conformance, organizations are subject to significant and



frequent or large deviations in the sense that the information about the observations of users is not followed and the methods for obtaining this information are not defined. Moreover, they are not characterized by good communication with customers and similarly.

Similarly, in the field of 8.2.3 with the observed aspect, organizations do not apply appropriate methods for monitoring and performance measurement processes, have no mechanisms for the implementation of corrective measures in cases that have not achieved the planned performance of processes and the like.

If we consider that there are 500 certificates in Serbia and Montenegro in the part of the most competent certification bodies, then the number of 350 makes 70% out of total number, which indicates the significance of the sample for analysis. Nonconformities represent findings of external revisers who underwent demanding trainings mostly with foreign and competent trainers, which of course increases the significance of data for analysis. These nonconformities were stored in the database classified in relation to requests of standard and activities of the organization by application of realized DSS (Decision Support System) i.e. system for the support of decision-making.

Furthermore, we enter data analysis and integration with results of displayed software solution for DSS. Based on the analogy with willing functions of the human organism, database is being searched separately for productive and service organizations.

During that, the distribution of nonconformities is being observed in the percentage form individually for every request of the standard in accordance with the previously defined division. For the analogy with the human organism, the monitoring of the condition of every activity is realized (or in medical terms, “every function”) i.e. transposed in the field of standard, condition of every spot, i.e. the request of the standard, is monitored.

Thereby the inevitable process approach up to the level of activity is being respected, i.e. promoted. In that direction, the percentage display of nonconformities appearance for every request of the standard into a positive form has been converted by subtracting the number of one

hundred so as to create a positivistic standpoint and identify the power of activity.

Afterwards, through an introduction of the term Degree of readiness (Si) multiplication of the power of every request given in terms of percentage with weight coefficient is done in relation to the BE (according to EFQM model) obtained as a result of application of AHP (Analytic Hierarchy Process) approach and point to significance of every request of ISO 9001 individually for achieving business excellence [1] and [35] in accordance with the following Eq.:

S N Ki

i p u z p u z p u( / ) ( / ) ( / )% ,

, , ...,

= [ ]×= 1 2 26 ,

(1)

where Si(p/u) is the degree of readiness for productive/service organizations; Nz(p/u) is the power of a standard clause in terms of percentage and Kz(p/u) is coefficient of significance for achieving business excellence. The results of the presented approach are shown in Table 1.

Based on the addition of powers for every clause of the standard individually, on the power, i.e. degree of readiness (S(p/u)) for the entire organization can be predicted, that is to say, the quality management system in terms of achieving business excellence in accordance with the Eq.:

S Sp u i p ui

( / ) ( / )==∑

1

26, (2)

where S(p/u) is the degree of readiness for the entire system of productive/service organization.

Applying the defined expression, the following degrees have been obtained:S(p) = 55.44538 - Degree of readiness for

productive organization;S(u) = 56.37187 - Degree of readiness for service

organization.As S(u)> S(p) i.e. service organizations have

a larger degree of readiness in terms of achieving business excellence, it can be concluded that: By observing the entire certified QMS, service organizations in our environment, observed from the perspective of occurrence of nonconformities i.e. errors in the system, namely transferred in terms of powers of every request of the standard individually, have a bigger capacity for achieving BE regarding the European Award for Business Excellence. Difference of 0.9265, regarding the scope of numbers occurring in this research, is significant. If you have a look at the Table 1, this



difference could signify that service organizations have the “advantage” of e.g. approximately three requests (7.4, 6.3 i 6.4 or some other combination) because the sum of their degrees of readiness covers up for this difference, and alike. However, if you take a look at the influence of weight coefficients on achieving business excellence, you can find a significant difference both in productive and service organizations in terms of their significance. Thereby, even fulfilment of certain requests of the standard and completion of their improvement in terms of excellence do not have the same meaning. For example, a significant difference between coefficients of significance of clause 8.2.1 and clause 6.4 of the standard has been observed.

Thereby, both fulfilment of these clauses and completion of their improvement do not have the same meaning. Research was continued in that direction, and by applying Pareto method, the most significant requests of the standard were identified i.e. requests that participate with over 70% in the total sum of their weight coefficients, for productive and service organizations independently.

Through application of the Pareto method, following requests were identified as the most significant requests in achieving business excellence:• for productive and service organizations,

requests - 8.2.3, 8.2.1, 8.5, 8.4, 5.5, 8.2.4, 5.6, 5.3, 7.1 and 4.1,

We can examine readiness of productive and service organizations in terms of achieving critical requests for BE. That conclusion points to the speed of achieving business excellence considering that there are great preconditions for organizations with bigger capacity to meet critical requests and improvements in that part, to achieve business excellence faster too.

In that direction, a term of critical degree of readiness in accordance with the Eq. has been introduced:

S Sk p u i p ui

( / ) ( / )==∑

1

10

, (3)

where Sk(p/u) is critical degree of readiness for the entire system of productive/service organization.

Based on the expression for critical degree of readiness, the following is obtained:Sk(p) = 34.13428 - Critical degree of readiness for

productive organizations,Sk(u) = 31.47164 - Critical degree of readiness for

service organizations.

Table 1. Review of the degree of readiness for productive and service organizations in relation to every request of the standard

Req

uest

s

Prod

uctio

n(p

ower

)

Kz(

p)

S i(p

)

Serv

ice

(pow

er)

Kz(

u)

S i(u

)

823 50 0.083 4.15 50 0.083 4.15821 66.67 0.082 5.46694 33.33 0.083 2.7663985 37.14 0.069 2.56266 68.57 0.069 4.7313384 57.9 0.067 3.8793 42.1 0.067 2.820755 66.67 0.066 4.40022 36.67 0.066 2.42022824 40 0.064 2.56 70 0.064 4.4856 44 0.059 2.596 58.67 0.056 3.2855253 70 0.054 3.78 50 0.054 2.771 56.52 0.04 2.2608 47.83 0.04 1.913241 65.22 0.038 2.47836 56.52 0.039 2.2042851 72.73 0.038 2.76374 72.73 0.037 2.6910175 37.5 0.038 1.425 67.19 0.037 2.4860354 48.72 0.037 1.80264 53.85 0.037 1.9924583 50 0.036 1.8 65.39 0.036 2.3540452 40 0.035 1.4 100 0.035 3.581 60 0.033 1.98 100 0.034 3.4822 46.3 0.032 1.4816 57.41 0.032 1.8371273 76.19 0.026 1.98094 47.62 0.026 1.2381261 100 0.022 2.2 50 0.021 1.0572 56.76 0.022 1.24872 48.65 0.022 1.070342 49.26 0.019 0.93594 51.85 0.019 0.9851576 44.68 0.018 0.80424 61.7 0.018 1.110662 62.07 0.008 0.49656 43.1 0.008 0.344874 52.94 0.008 0.42352 49.02 0.008 0.3921663 50 0.005 0.25 53.33 0.005 0.2666564 63.64 0.005 0.3182 36.36 0.005 0.1818

As Sk(p) > Sk(u) it can be concluded that productive organizations in the system of numbers shown in Table 1 have a significant “advantage” in achieving BE in relation to service organizations. Therefore, productive organizations may establish the BE “faster” in the critical part.



The difference between critical degrees of readiness is 2.66264, which could e.g. conform to request 8.5 at the productive organizations.

The research was further elaborated in order to indicate critical areas from the standpoint of occurrence of nonconformities in productive and service organizations and the examination of correspondence with significances of the request in the part of achieving BE.

The aim was to indicate whether certain critical areas in the system correspond to significant requests for achieving BE i.e. requests that have pronounced weight coefficients.

Those are also requests that require special attention from the standpoint of improvement and defining measures in terms of prevention in order to “strengthen” those areas and thereby create preconditions for achieving BE, namely, improve organizational performances in accordance with a tendency of achieving BE.

At the same time, the Pareto method (70/30) was carried out and based on that critical areas can be defined as follows:• for productive organizations, requests - 4.2,

5.6, 7.5, 8.2.2, 7.6, 7.4, 8.5, 6.2,• for service organizations, requests - 4.2, 6.2,

5.6, 7.4, 8.2.2, 7.5, 8.2.1, 5.5, 7.2, 5.4.Now common requests that are critical from

the standpoint of occurrence of nonconformities and that have significant weight coefficients in terms of achieving business excellence are searched for. Common requests in those terms that present either critical areas or areas of special significance for the implementation of improvement measures in one are shown in Table 2.

Therefore, priority requests or areas of the standard wherein urgent improvements from the standpoint of achieving top performances based on the BE model should be done, i.e. areas whereupon “strong” and priority preventive measures should take place, are as follows:• requests 5.6 and 8.5 for productive

organizations and• requests 5.6, 5.5 and 8.2.1 for service

organizations.Now an analysis of individual requests,

observing their “readiness” regarding the percentage of occurrences of nonconformities, and on the other hand their weight coefficients in

achieving BE, with a previously stated goal which initiated further analysis up to this level, can be performed.

Clause 5.6 observed from the perspective of occurrence of nonconformities, is especially pronounced both at productive and service organizations, and a significant number of nonconformities have been found within it.

On the other hand, in relation to BE it has a medium value of weight coefficient in the part of critical coefficients. Coefficients imply that it is necessary to carry out improvement measures of “medium intensity” both at productive and service organizations in order to reduce a great number of nonconformities and satisfy a coefficient of medium significance in the group of critical coefficients.

Service organizations have the “advantage” in this part, considering that the coefficient of significance is approximately the same, while the number of nonconformities is smaller in relation to productive organizations and area 5.6. A considerable number of nonconformities imply the considerable number of experiences of others too, and based on the approach applied in this doctoral thesis or the approach of learning based on experiences of others, it is possible to carry out a significant number of preventive actions for this area, that would provide an improvement of this area of the standard.

Area 8.5 has been identified as critical in the part of productive sector regarding the occurrence of nonconformities and belongs to the “lower” A class of the Pareto classification. Still, as a critical area it is significant for improvement and has a priority in relation to other areas, and especially because its weight coefficient in achieving BE is pronouncedly big. Therefore, improvement in this part with “strong” preventive measures provides a significant advance of organization i.e. QMS towards BE. The results presented in [36] show a certain congruence and similarity in the conclusion. According to this approach, this is also the last area of the standard that has special and priority significance for improvement.

In service organizations, the area of the clause 8.2.1 emerges as a critical area regarding the occurrence of nonconformities. According to the Pareto’s division this area belongs to the “lower” A class. Great significance has been



identified also regarding the significance of the coefficient that belongs to this clause in terms of achieving BE. In addition, it is “worrying” that a large number of nonconformities at service organizations has occurred exactly within one of the essential requests of the standard, i.e. user satisfaction. This observation becomes more significant if we bear in mind that in our situation it is evident that the economy now for the most part relies, on the service sector.

Table 2. Review of priority areas for improvement

Productive organizations Service organizations

Req

uest

s of s

peci

al

sign

ifica

nce

for a

chie

ving

bu

sine

ss e

xcel

lenc

e

8.2.38.2.18.58.45.5

8.2.45.65.37.14.1

Prio

rity

area

s for

impr

ovem

ent o

f pro

duct

ive

orga

niza

tion

5.68.5

Req

uest

s of s

peci

al

sign

ifica

nce

for a

chie

ving

bu

sine

ss e

xcel

lenc

e

8.2.38.2.18.58.45.5

8.2.45.65.37.14.1

Prio

rity

area

s for

impr

ovem

ent o

f ser

vice

org

aniz

atio

n

5.68.2.15.5

Req

uest

s tha

t are

crit

ical

re

gard

ing

the

occu

rren

ce o

f no

ncon

form

ities

4.25.67.5

8.2.27.67.48.56.2 R

eque

sts t

hat a

re c

ritic

al

rega

rdin

g th

e oc

curr

ence

of

nonc

onfo

rmiti

es

4.26.25.67.4

8.2.27.5

8.2.15.57.25.4

The above mentioned is also the reason why a special analysis of nonconformities in this part for service organizations should be carried out, and a strong and priority improvement should be defined.

In this way service organizations can “strengthen” in terms of achieving top performances and thereby create preconditions for strengthening entire economic structure and the most important area in our conditions, i.e. service sector and relation with the user, namely accomplishment of their unreserved pleasure.

As the last one out of three critical areas in the part of the service sector, with a significant weight coefficient in the part of achieving BE, area 5.5 has been identified. It belongs to the “lower” A class of Pareto’s classification of nonconformities and brings the medium significant weight

coefficient for BE. It does not occur as a critical area in productive organizations, which can be “justified” by clearly defined procedures in technological descriptions of jobs at processes of production and in productive systems in general. This can indicate that service organizations must finally commit to a procedural way of operation and that service must finally be considered as a product in terms of a division into four generic products, as defined by the standard. Therefore, it is also a priority in this part to define and implement clear preventive measures that shall bring service organizations closer to the systems with excellent organizational performances.

It is now interesting to move in the direction of identification of areas for achieving BE in service organizations in relation to productive organizations in terms of the previously presented difference regarding the satisfaction of requests with critical coefficients and regarding the “advantage” of productive organizations. This approach provides priorities for improvement in service organizations, which is highly significant considering the orientation of our business systems towards the service sector. It has already been mentioned that in the part of priority areas for improvement, in the clause 5.6, service organizations have an advantage over productive organizations. It is the similar with clause 8.5, which is critical from the standpoint of nonconformities for productive organizations. Service organizations have the advantage in that part. Therefore, it is evident that the mentioned difference emerges in clauses 8.2.1 and 5.5 regarding service organizations and that service organizations lack the “capabilities” in that part. This is a cause for concern, because those are two very important areas that represent one of the essential values of the standard. Therefore, it can be concluded that improvement measures must be urgently defined with priority in terms of preventive action in user satisfaction and in the part of responsibilities, authorizations and communications of the service sector.

If we take a look at the list of “coefficients of significance” for BE achieving (Table 1), especially the most important ones and make a comparison with the list of variables and their significance in terms of: Business Process Reengineering (BPR), manufacturing strategy,



benchmarking and performance measurement, being the result of the appreciated research conducted in Slovenia [37] and [38] significant intercompatibility may be found.

The concerned compatibility is especially reflected in the following variables, evaluated in the relative research as highly significant for the following four projects, i.e.: customer satisfaction, quality, employee satisfaction and personal growth, customer adaptability, identification of top managers with BPR goals, strong process orientation, results orientation, direct customer cooperation. On the other hand, the above mentioned four areas are considered as highly important for any market-oriented organization, thence it can be concluded that organizations by strengthening their capacities in areas of presented “coefficients of significance” (especially the most important ones), are not only strengthened in terms of the BE achieving as per European Award model, but also in the stated four areas.

3 FINAL OBSERVATIONS

Through respect of the stated approaches and models, we can point to the situation of service and productive sector in our conditions of business operation. It proves that, observed at the level of entire certified quality management system (QMS), including all requests of the standard, service organizations have a significant advantage in comparison with productive sector in terms of achieving business excellence (BE). Therefore, expressed in sports terminology, productive organizations in our conditions are in better form and have a better ability for achieving top results. Or in accordance with the process approach, service organizations collectively have better performances of activities in the certified QMS. It is interesting that through further analysis based on stated approaches and models, condition was changed in the part of the most significant activities, which build organizational BE or are characterized as a “top sportsman” in sport. In those terms, productive organizations have a significant advantage over organizations from the service sector. It could be said that productive organizations would faster achieve BE in that critical part, and thereby they would have conditions to achieve that in the entire QMS,

considering that the critical part implies 70% of the most significant and demanding areas of the ISO 9001 standard regarding the influence on achieving BE.

Conditions like this represent a good foundation for research in terms of identification of critical areas, i.e. areas of priority significance concerning improvement. In that way, requests 5.6 and 8.5 for productive organizations and requests 5.6, 8.2.1 and 5.5 for service organization have been isolated as critical and of the biggest priority for improvement.

The research has resulted in a difference between degrees of readiness, in terms of the advantage of productive organizations in the direction of achieving BE. The analysis led to the conclusion that service organization should inevitably and with priority focus their activities on improvement of their process on areas of user satisfaction (8.2.1) and responsibilities, authorizations and communications (5.5), in order to annul their delay after productive organizations towards the BE. Through urgent and planned improvements in these areas, service organizations can make an important stride towards BE and improvement of their performances to that effect. The strengthening, especially in areas having high “coefficients of significance”, leads to a significant progress in terms of: business process reengineering, manufacturing strategy, performance measurement and benchmarking, as very important aspects of market-oriented organization.

In terms of practical implementation, through the solutions presented in this paper, the organizations are suggested to consider the areas of priority from the viewpoint of defining and implementing the measures for improvement in the form of corrective and preventive actions as well. These areas are preferential, but not the only ones. The intensity of improvement measures is defined by “coefficients of significance” presented in this paper and leads to top results based on the model for the European Award for Business Excellence. By using this model in practice, the organizations can efficiently and effectively reach the “top form” based on the priorities and guidelines presented in this paper.

On the basis of “Case Based Reasoning theory”, the organization planning to implement



QMS or upgrading a performance of the system to the highest level, a strategy to prevent the errors in the critical areas and also strengthen the areas of priority for BE achieving should be developed. These experiences and results may be useful to the organizations for defining their strategies based on experiences (cases) of the other organizations. Also, it can be used for encouraging and stimulating the organization to achieve top results and also their involvement in European norms and criteria for BE achieving.

Throughout this methodology and based on the indicators obtained during previous QMS internal and external audits, the organization can measure its “degree of readiness” for BE achieving, and find its position and compare to the other organizations accordingly.

The expert system was implemented and tested in practical, real conditions in the organization that has a clear commitment to participate in the competition for the European Award for Business Excellence, also providing important measures in that direction. The implementation of the system is scored with 92% satisfactory mark in terms of: impact of the selected solution to the organization of business activities and new expert system benefits. In this sense, within the organization, a reorganization of the priority areas from the viewpoint of improvement has been carried out, preventive measures for the potentially unstable areas have been implemented and the measures for the improvement (offered by this system) leading to achieving BE have been applied.

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Strojniški vestnik - Journal of Mechanical Engineering 57(2011)2Vsebina

Vsebina

Strojniški vestnik - Journal of Mechanical Engineeringletnik 57, (2011), številka 2

Ljubljana, februar 2011ISSN 0039-2480

Izhaja mesečno

Povzetki člankovDarko Lovrec, Vito Tič: Energijsko varčna hladilna enota na strojih za pihanje plastike SI 23Huseyin Gurbuz, Abdullah Kurt, Ibrahim Ciftci, Ulvi Seker: Vpliv geometrije lomilca odrezkov

na napetosti v orodju pri struženju SI 24Vladimir Popovic, Branko Vasic, Milos Petrovic, Sasa Mitic: Sistemski pristop k krmiljenju

sistema vzmetenja vozila v okolju CAE SI 25Aleš Slak, Jože Tavčar, Jože Duhovnik: Uporaba genetskega algoritma pri večkriterijskem

razporejanju serijske proizvodnje SI 26Uroš Stritih, Vincenc Butala: Energijski prihranki v stavbi s sistemom PCM prostega hlajenja SI 27Mário João Simões Ferreira dos Santos, Jaime Batista dos Santos: Krmilni sistem na osnovi

čipa FPGA za ultrazvočno fazno polje SI 28Franci Čuš, Uroš Župerl: Nadzor stanja rezalnega orodja v realnem času pri rezkanju SI 29Aleksandar Vujovic, Zdravko Krivokapic, Mirko Sokovic: Izboljšanje zmogljivosti poslovnih

procesov z vzpostavitvijo analogije: sistem menedžmenta kakovosti – človeški organizem SI 30

Navodila avtorjem SI 31

Osebne vestiDoktorati, magisteriji, specializacije in diplome SI 33

Strojniški vestnik - Journal of Mechanical Engineering 57(2011)2, SI 23 Prejeto: 13.04.2010 Sprejeto: 06.10.2010

*Naslov avtorja za dopisovanje: Univerza v Mariboru, Fakulteta za strojništvo, Smetanova 17, 2000 Maribor, Slovenija, [email protected] SI 23

Energijsko varčna hladilna enota na strojih za pihanje plastike

Darko Lovrec1 - Vito Tič1, 2

1 Univerza v Mariboru, Fakulteta za strojništvo, Maribor, Slovenija 2 Olma d.d., Ljubljana, Slovenija

Stisnjen zrak je eden izmed pomembnejših, a hkrati tudi najdražjih virov energije v industriji. Zato je potrebno posebno pozornost nameniti njegovi racionalni rabi, še posebej v primerih, ko stisnjen zrak pihamo v »prazno« okolje. Tipični primeri tovrstne uporabe so vse vrste čiščenja in tudi hlajenja, ki ga izvajamo s stisnjenim zrakom. Tako se na področju obdelave kovin ali plastičnih mas stisnjen zrak uporablja npr. za hlajenje žaginih listov ali visokozmogljivih HM ali HSS frezal, ali pri izdelavi izdelkov, ki ne smejo biti hlajeni s hladilnimi tekočinami. Med slednje spada tudi postopek izdelave plastičnih izdelkov, ki se izdelujejo na strojih za pihanje plastike. Vsi omenjeni postopki so tudi največji porabniki stisnjenega zraka in so energijsko zelo potratni. Zato se pri omenjenih aplikacijah vlaga veliko naporov v iskanje primernih ukrepov za racionalno rabo stisnjenega zraka.

V prispevku je predstavljena rešitev tega problema. Rešitev se opira na uporabo energijsko varčnih izpihovalnih šob s stranskimi sesalnimi odprtinami. Princip delovanja takšne šobe temelji na Bernoullijevi in kontinuitetni enačbi: če se zmanjša presek, se poveča hitrost, pri čemer se tlak zmanjša vse do podtlaka, tako da skozi stranske odprtine sesamo zrak iz okolice. Tega pa ni bilo potrebno stiskati.

Optimalna geometrija takšne šobe je bila zasnovana na podlagi ustreznega matematičnega modela in numerične simulacije pretoka zraka skozi šobo, pri čemer je bila v ospredju učinkovitost sesanja okoliškega zraka. Simulacija je bila izvedena s programskim paketom Ansys Workbench, ki temelji na metodi končnih prostornin. Uporabljen je bil SST (shear-stress transport) model, ki zelo dobro opisuje dogajanje v šobi, pri čemer za dogajanje ob steni elementa uporablja turbulentno-frekvenčni k-ω model, v preostalem področju pa k-ε model.

Na osnovi optimirane geometrije enojne šobe je bila zasnovana celotna energijsko varčna hladilna enota. V primerjavi z dokaj enostavno geometrijo posamične šobe je geometrija hladilne enote neprimerno bolj zapletena in kompleksna. Pri snovanju enote je bila uporabljena ideja, da večje število varčnih šob z optimirano geometrijo namestimo drugo ob drugi, zrcalno v dveh vrstah, pri čemer so sicer posamične izstopne odprtine pri bloku povezane v skupen izpustni kolektor. Razporeditev posameznih šob je zasnovana glede na obliko posameznega izdelka. Celotna geometrija hladilne enote je bila, podobno kot posamična šoba, optimirana na podlagi numeričnih simulacij pretoka zraka in nato izdelana s postopkom dodajnih tehnologij – postopki hitre izdelave prototipov oz. izdelkov (RP/RM – Rapid Prototyping/Rapid Manufacturing).

Skrbno načrtovan sistem izpihovalnih šob je bil praktično uporabljen pri hlajenju v proizvodnem procesu, pri čemer je kot primer služila izdelava plastičnega kanistra. Učinkovitost tako zasnovane in izdelane hladilne enote je bila eksperimentalno preverjena z uporabo termografske kamere, s pomočjo katere smo lahko dovolj natančno posneli razmere med procesom ohlajanja kanistra. Rezultati so pokazali, da nova energijsko varčna hladilna enota omogoča enako učinkovito ohlajanje, kot se dosega z obstoječo enoto, vendar ob skoraj polovico manjšem pretoku dovedenega zraka. Preostala količina zraka, ki sodeluje pri ohlajanju, je bila »brezplačno« posesana iz okolice.

Kljub že doseženi učinkovitosti hladilne enote obstaja še veliko možnosti nadaljnjega razvoja tega sistema, bodisi v smeri dodatnega povečanja učinkovitosti ali pa ustrezne konstrukcije, ki omogoča večji prihranek materiala pri izdelavi tega izdelka. Predstavljeno hlajenje s stisnjenim zrakom pri postopku pihanja plastičnih izdelkov je samo eden od številnih možnih primerov uporabe tovrstnega hlajenja, ki ga je pa z dodatnimi ukrepi možno zasnovati še veliko bolj učinkovito.© 2011 Strojniški vestnik. Vse pravice pridržane.Ključne besede: stisnjen zrak, hlajenje, pihanje plastike, simulacija, eksperiment


*Naslov avtorja za dopisovanje: Univerza Hacettepe, Tehniška fakulteta, 06800 Beytepe, Ankara, Turčija, [email protected] 24

Vpliv geometrije lomilca odrezkov na napetosti v orodju pri struženju

Huseyin Gurbuz1,* Abdullah Kurt2, Ibrahim Ciftci3, Ulvi Seker2

1 Univerza Hacettepe, Tehniška fakulteta, Turčija 2 Univerza Gazi, Tehniška fakulteta, Turčija

3 Univerza Karabuk, Tehniška fakulteta, Turčija

Namen študije je ugotoviti vpliv različnih geometrij lomilcev odrezkov na sile pri rezanju in napetosti v orodju med struženjem. V ta namen so bili izvedeni preizkusi struženja v skladu s standardom ISO 3685 na jeklu AISI 1050 s prevlečenimi in neprevlečenimi karbidnimi rezalnimi orodji z lomilci odrezkov različnih geometrij. Uporabljeno je bilo rezalno orodje oblike SNMG120408R in orodno držalo oblike PSBNR252512. Orodno držalo ima stranski kot 75°. Uporabljena so bila rezalna orodja proizvajalca Mitsubishi Carbide z lomilci odrezkov tipa MA, SA, MS, GH in standard (STD). Vsa orodja so prevlečena. Poleg tega so bila uporabljena tudi neprevlečena orodja tipa MS in STD. Opravljeni so bili preizkusi obdelave s petimi rezalnimi hitrostmi (150, 200, 250, 300, 350 m/min), tremi hitrostmi podajanja (0,15, 0,25 in 0,35 mm/vrt.) ter dvema globinama reza (1,6 in 2,5 mm).

Sile pri rezanju so bile merjene z dinamometrom Kistler 9257B. Dinamometer je bil priključen na računalnik in izvedenih je bilo skupno 210 preizkusov struženja (30 preizkusov za vsako rezalno orodje) brez hladilne tekočine. Vpliv oblike lomilcev odrezkov na rezalne sile je bil določen eksperimentalno za različne parametre rezanja. Vpliv sprememb rezalne sile pri različnih oblikah lomilcev odrezkov na napetosti v orodju je bil analiziran s programsko opremo za analizo po metodi končnih elementov (ANSYS). Orodno držalo in orodja iz karbidne trdine so bila za namene analize modelirana s programsko opremo CATIA V5R15 in shranjena kot model CATIA. Modeli so bili nato odprti v paketu ANSYS z dodatkom za modele.

Rezultati eksperimentov: Ugotovljeno je bilo, da povečanje rezalne hitrosti v splošnem zmanjša glavno rezalno silo (FC) za vse oblike lomilcev odrezkov do rezalne hitrosti 300 m/min, nad to hitrostjo pa se začne sila spet povečevati. Povečanje hitrosti podajanja in globine reza je pri vseh pogojih rezanja in pri vseh oblikah lomilcev odrezkov prineslo povečanje glavne rezalne sile (FC). Največje rezalne sile FC so bile ugotovljene pri lomilcih odrezkov tipa SA, kompleksna geometrija lomilcev odrezkov pa povzroča večje rezalne sile. Rezultati analize: Ugotovljeno je bilo, da povečanje hitrosti podajanja povzroči povečanje največjih glavnih napetosti (S1) in najmanjših glavnih napetosti (S3), medtem ko so se napetosti S1, S3 zmanjšale odvisno od rezalne hitrosti in globine reza za vse oblike rezalnih orodij. Rezultati analize kažejo, da se največje vrednosti maksimalnih glavnih napetosti (S1) in minimalnih glavnih napetosti (S3) pojavijo pri najbolj kompleksnih oblikah prevlečenih lomilcev odrezkov tipa SA in MA. Najmanjše vrednosti teh napetosti pa so bile ugotovljene pri neprevlečenih lomilcih odrezkov tipa STD. Analiza grafov (S1, S3) razkrije, da se napetosti pri uporabi lomilca odrezkov tipa MA signifikantno povečajo pri vseh podajalnih hitrostih in rezalnih hitrostih, ko se globina reza iz 2,5 mm zmanjša na 1,6 mm. To je možno pojasniti z globino reza in z vrednostmi rezalnih hitrosti, ki so zunaj priporočenega območja za lomilec odrezkov tipa MA.

Literatura ne obravnava vpliva sprememb rezalnih sil pri različnih oblikah lomilcev odrezkov na napetosti v orodju pri struženju. Rezalne sile povzročajo obrabo, razpoke, lom in različne deformacije rezalnega orodja, zato so tudi glavni kriterij za obdelovalnost. Te napake povzročijo, da rezalno orodje ne opravlja svoje funkcije. To pa je pomemben dejavnik pri optimizaciji procesa. V tej študiji je zato bila uporabljena metoda končnih elementov s programskim paketom ANSYS za določitev mesta in velikosti napetosti v rezalnem orodju, ki jih povzročijo različne oblike geometrij lomilcev odrezkov.© 2011 Strojniški vestnik. Vse pravice pridržane.Ključne besede: odrezavanje kovine, oblika lomilca odrezkov, rezalne sile, napetosti v orodju, glavne napetosti, AISI 1050


*Naslov avtorja za dopisovanje: Univerza v Beogradu, Fakulteta za strojništvo, Kraljice Marije 16, Beograd, Srbija, [email protected] SI 25

Sistemski pristop k krmiljenju sistema vzmetenja vozila v okolju CAE

Vladimir Popović1,* - Branko Vasić1 - Miloš Petrović2 - Saša Mitić1

1 Univerza v Beogradu, Fakulteta za strojništvo, Srbija 2 Institut za raziskave in projektiranje v gospodarstvu, Srbija

Industrija motornih vozil v zadnjih letih izkazuje trend menjave elektromehanskih komponent z mehatronskimi sistemi, ki delujejo inteligentno in avtonomno. Za ta proces menjave je značilna integracija komponent strojne opreme in implementacija naprednih krmilnih funkcij. V članku smo uporabili sistemski pristop in metode sistemskega inženiringa v začetni fazi razvoja aktivnega vzmetenja vozila. Poudarek je na medsebojnih povezavah med računalniško podprto simulacijo in drugimi elementi razvojnega procesa. Prednosti uporabe simulacije aktivnega vzmetenja so številne: skrajšanje časa do plasiranja izdelka na trg, nove in izboljšane funkcije mehatronskih komponent/naprav ter povečana zanesljivost sistema. Pomembno vlogo ima interaktivno preizkušanje mehatronskih komponent z naprednimi hardverskimi simulatorji v povratni zanki. Pristop sloni na vgradnji mehatronskih komponent v virtualno okolje, kjer poteka simulacija gibanja vozila, zunanjih obremenitev in sosednjih mehanskih sistemov v realnem času. Pri razvoju modela vzmetenja smo uporabili orodja CAD/CAE in večnamenske simulacijske programe. Na osnovi reduciranih modelov je bilo treba razviti multivariabilen in zmogljiv sistem za aktivno krmiljenje vzmetenja. Pri tem smo uporabili samo digitalne sisteme za samodejno regulacijo.

Model sistema vzmetenja je bil izdelan v interaktivnem okolju Matlab in z enačbami prostora stanj za model vozila v razmerju ¼. Pri obravnavi nihanj vozila smo privzeli naslednja izhodišča: vozilo se giblje premočrtno s konstantno hitrostjo, kolesa so vedno v enotočkovnem stiku s cesto, motnje na vozišču so enake na levem in na desnem kolesu in vozilo je simetrično glede na vzdolžno os, koeficient porazdelitve mase je približno 1. Koračni vhod je enotska koračna funkcija oz. določena vrednost motnje na vozišču. Ugotovljeno je bilo, da sta čas umirjanja in prekoračitev vzmetenja vozila po vsaki oviri predolga, zato je treba sistem vzmetenja opremiti s krmilnikom. Zasnovali smo digitalni krmilnik po metodi polov, ki daje samo eno od možnih rešitev. Čeprav je realen sistem vzmetenja nelinearen, smo v tem članku privzeli konstantne vrednosti, ki omogočajo linearizacijo modela do določene mere. Tudi koračna funkcija, ki smo jo uporabili za simulacijo, ima določene omejitve. Če je kolo vozila vzbujeno s koračno funkcijo, bo namreč poskočilo in izgubilo stik s površino vozišča. Ravno tako je blaženje stisnjenega amortizerja nekajkrat manjše kot pri iztegnjenem amortizerju. Podatki, ki smo jih uporabili med simulacijo, se nanašajo na avtobus. Predstavljeni dinamični model je le zelo groba predstavitev pravega dinamičnega sistema, zato je uporaben samo v zgodnjih fazah raziskav in razvoja. Pričakujemo lahko, da bo takšen pristop z omenjenimi spremembami in potrebnimi izboljšavami uporaben za snovanje sistemov vzmetenja v industriji motornih vozil naše države.© 2011 Strojniški vestnik. Vse pravice pridržane.Ključne besede: sistem aktivnega vzmetenja, sistemski pristop, simulacija, krmilni sistem, PID-krmilnik, model vozila


*Naslov avtorja za dopisovanje: Iskra ISD-Strugarstvo d.o.o., Savska loka 4, SI-4000 Kranj, Slovenija, [email protected] 26

Uporaba genetskega algoritma pri večkriterijskem razporejanju serijske proizvodnje

Aleš Slak1,*, Jože Tavčar2, Jože Duhovnik3

1 Iskra ISD-Strugarstvo d.o.o., Slovenija 2 Iskra Mehanizmi, Slovenija

3 Univerza v Ljubljani, Fakulteta za strojništvo, Slovenija

Tehnične novosti se na področju logistike v proizvodnji uvajajo parcialno in zato ne izkoriščajo vsega potenciala. Za optimalno učinkovitost proizvodnih procesov struženja smo analizirali proces in izvedli njegovo prenovo. Problem planiranja in razporejanja izdelkov pri proizvodnji struženih delov spada med kompleksnejše probleme. »n« izdelkov je treba razdeliti med »m« strojev tako, da bodo pravočasno izdelani in poslani kupcu. Vsak izdelek ima najmanj en alternativni plan, pri katerem nastopa več operacij. Ročni način je vseboval veliko ustnega dogovarjanja in fizičnega pregledovanja poteka dela na strojih. Raziskava je bila usmerjena v razvoj avtomatskega način razporejanja izdelkov na stroje glede na postavljene kriterije in omejitve. Cilj je bil povečati produktivnost in izkoriščenost strojev, zagotoviti pravočasnost dobav kupcem ter doseči hitro odzivnost ob spremembah v proizvodnji. Model planiranja in razporejanja je razdeljen na dva dela: v prvem delu se določi vse alternativne plane za vsak artikel, v drugem optimizacijskem delu pa se uporabi pristop z genetskimi algoritmi. Ko so znani alternativni plani z zaporedjem operacij, potrebne količine materiala in orodja, preidemo na drugo fazo, za katero so to vhodni podatki. Fazo imenujemo faza razporejanja in njena ključna naloga je razporeditev vseh operacij izdelka na stroje tako, da bodo doseženi kriteriji razporeda. Preden se preide na podrobno planiranje, se v sistemu preveri stanje zalog materiala in orodja. V prispevku je predstavljen genetski algoritem za razporejanje serijske proizvodnje. V času razvoja genetskega algoritma so bile sistematično preizkušene različne nastavitve in pristopi. Testi so pokazali, da je zadovoljivo rešitev plana možno poiskati z okvirno 1000 evalvacijami. Izbiro optimalne selekcije smo določili s primerjavo selekcij brez uporabe operatorjev križanja in mutacije. Kot najboljši sta se izkazali ruletna in turnirska selekcija. Dvotočkovno križanje z enotočkovno mutacijo hitro konvergira proti dobri rešitvi, vendar se hitro tudi umiri in ni več sposobno iskati še boljše rešitve. Naključna mutacija išče rešitve v večjem obsegu, zato je na začetku konvergenca populacije proti dobri rešitvi počasnejša, a končna rešitev je boljša. Zaradi navedenega smo v končni verziji uporabili dvotočkovno križanje z naključno mutacijo. Pri razvoju algoritma je bil uporabljen sistematični pristop, ki je algoritmu omogočal, da je postopoma preverjal omejitve in zahteve v danem trenutku, in skrbel, da so bili kriteriji na koncu izpolnjeni. Avtomatski sistem razvrščanja omogoča tudi pregled zalog končnih izdelkov, materiala in orodja. Pri tem se zahteva doslednost pri spremljanju izdelkov na posamezni operaciji skozi proizvodnjo. Ta podatek omogoča točno napoved, koliko izdelkov se lahko dokonča v kratkem časovnem obdobju. Najbolj pomembna pri obvladovanju dinamičnega proizvodnega okolja je hitra in učinkovita odzivnost ob spremembah, kot so na primer okvara stroja, vzdrževalna dela in nujna naročila. To omogoča nov pristop, ki lahko predlaga izvedljive alternativne rešitve (prerazporeditev izdelkov). Pri uvajanju modela smo planiranje proizvodnih kapacitet povezali z dobaviteljsko verigo in odjemalci. Pomembna ugotovitev je, da se optimirani plan izdela zelo hitro ter da ga je možno brez veliko napora tudi spremeniti glede na trenutne potrebe. Z dobljenimi rezultati se je dvignila produktivnost, povečala izkoriščenost strojev in pravočasnost dobav.Izviren prispevek članka je predstavljena rešitev na primeru razporejanja naročil v proizvodnji struženih delov. V optimizacijskem postopku nastopa več ciljnih funkcij in kriterijev, ki so bili upoštevani pri iskanju zadovoljive rešitve. V tem članku smo na primeru proizvodnje struženih delov prikazali, kako povezati planiranje procesa in razporejanje v integriran in zato učinkovitejši sistem. Podatki se znotraj algoritma izvažajo iz informacijskega sistema. Za optimizacijski proces je bila ustvarjena namenska aplikacija, ki na osnovi podatkov iz informacijskega sistema ERP v realnem času optimalno razporeja delo v proizvodnji. Genetski algoritmi so se izkazali za učinkovito metodo razporejanja izdelkov. Predstavljeni način uporabe genetskega algoritma se izvaja enkrat dnevno ali na zahtevo planerja. Tako praktične kot teoretične ugotovitve so namenjene vsem, ki se srečujejo s podobnim problemom razporejanja naročil na stroje. Algoritem je namreč sestavljen tako, da ga je možno uporabiti tudi pri drugih tipih serijske proizvodnje. © 2011 Strojniški vestnik. Vse pravice pridržane.Ključne besede: Genetski algoritem, serijska proizvodnja, ciljne funkcije, večkriterijsko razporejanje


*Naslov avtorja za dopisovanje: Univerza v Ljubljani, Fakulteta za strojništvo,Aškerčeva 6, 1000 Ljubljana, Slovenija, [email protected] SI 27

Energijski prihranki v stavbi s sistemom PCM prostega hlajenja

Uroš Stritih* - Vincenc ButalaUniverza v Ljubljani, Fakulteta za strojništvo, Slovenija

Glavni namen prispevka je predstaviti energijske prihranke v stavbi z uporabo sistema PCM prostega hlajenja. Gre za alternativni sistem hlajenja stavb, ki kombinira metodi povečane akumulacijske sposobnosti stavbe in nočnega prezračevanja. Akumulacijsko sposobnost stavbe smo povečali z uporabo snovi, ki spreminja agregatno stanje (angl. Phase Change Material – PCM). Le-ta je vgrajena v spuščen strop stavbe. Hladen zrak se v nočnem času vodi preko PCM snovi, ki se na ta način strjuje. V dnevnem času se topel zrak vodi preko PCM-snovi, ki absorbira toploto zraka. Zrak se na ta način hladi, sama PCM-snov pa prehaja iz trdnega v tekoče agregatno stanje.

Izdelana je bila merilna proga za eksperimentalne analize prenosa toplote PCM prostega hlajenja. Shranjevalnik hladu, napolnjen s PCM-snovjo (Rubitherm RT22), je bil vstavljen v zračni kanal, ki omogoča shranjevanje hladu v nočnem času. Hladen zrak se vodi preko PCM-snovi, ki se strjuje. V dnevnem času preko shranjevalnika vodimo topel zrak, ki se ohlaja zaradi akumuliranega hladu v parafinu. Zračni kanal je bil izoliran, da se s tem zmanjšajo toplotne izgube v okolico. Merilna proga je sestavljena iz osebnega računalnika, analogno-digitalnega pretvornika, vhodno/izhodne enote, merilnih zaznaval ter merilnika hitrosti. Zračni tok vstopa v kanal skozi vstopno odprtino, gre preko hranilnika hladu ter izstopa preko izstopne odprtine s pomočjo ventilatorja. Merili smo vstopno in izstopno temperaturo ter pretok zraka. Izdelan je bil matematični model, s katerim smo izračunali vrednosti energijskih prihrankov. Model prenosa toplote, ki je bil obravnavan dvodimenzionalno, smo reševali numerično z uporabo diferenčne metode, ki temelji na izračunu entalpij. Izdelan je bil računalniški program v jeziku Fortran, ki omogoča izračunavanje temperaturnih polj v PCM-snovi v različnih časih, kot tudi temperature na zraku. Prihranki energije so bili izračunani s pomočjo meteoroloških podatkov za več evropskih mest: Ljubljano, Rim, London in Stockholm.

Hranilnik hladu bi lahko bil zelo uporabna komponenta prezračevalnega sistema, saj izkorišča energijo okolice za hlajenje stavbe, kar je iz stališča rabe energije nadvse smotrno. Vendar pa je iz analize razvidno, da je lahko tak sistem v večini primerov le dopolnilo konvencionalnemu (kompresorskemu) hladilnemu sistemu in ne samostojna enota. Deleži prihrankov energije se razlikujejo od kraja do kraja, pri čemer se je potrebno zavedati, da je prihranek energije močno odvisen od podnebnih razmer – dnevne in nočne temperature zraka, latentne toplote fazno spremenljive snovi in količine zraka, ki ga vodimo ob hranilniku. V krajih s toplejšim podnebjem bodo temperature ponoči previsoke, da bi z njimi občutno ohladili PCM, kar pomeni, da podnevi z PCM-om ne bomo mogli hladiti zraka. Po drugi strani pa je v krajih s hladnejšim podnebjem nočnega hladu na pretek, vendar ni tako velike potrebe po hlajenju.

Latentna toplota je najpomembnejša lastnost PCM-a. Fazno spremenljiva snov z veliko latentno toploto lahko sprejme veliko količino toplote, vendar pa to pomeni tudi počasen dinamični odziv. Vpliv pretoka zraka je dobro razviden iz diagramov. Pri nizkem pretoku je prihranek energije večji – razlika temperatur vhodnega in izhodnega zraka je večja, kot v primeru višjega pretoka. Vendar pa nizki pretoki pogosto ne zadostujejo zahtevanim izmenjavam zraka v prostoru. Iz tega sledi, da bi bil najbolj učinkovit sistem takšen, ki bi vseboval PCM z veliko latentno toploto, tok zraka skozi hranilnik hladu pa bi moral biti čim manjši. Program je pomemben zato, ker bi z njim lahko optimirali hranilnik hladu pri projektiranju prezračevalnega sistema v stavbi. Pri poznanem testnem referenčnem letu za določen kraj in izmenjavi zraka v stavbi bi lahko določili dimenzijske parametre hranilnika, ustrezno PCM-snov in na koncu še prihranke energije.© 2011 Strojniški vestnik. Vse pravice pridržane.Ključne besede: hlajenje, shranjevalnik hladu, fazno spremenljiva snov, ekperimentalna analiza, numerična analiza


*Naslov avtorja za dopisovanje: Inštitut za znanost in tehnologijo materialov, Oddelek za elektrotehniko in računalništvo, Univerza v Coimbri, 3030-290 Coimbra, Portugalska, [email protected] 28

Krmilni sistem na osnovi čipa FPGA za ultrazvočno fazno polje

Mário João Simões Ferreira dos Santos* - Jaime Batista dos SantosInštitut za znanost in tehnologijo materialov, Oddelek za elektrotehniko in računalništvo,

Univerza v Coimbri, Portugalska

Pri običajnih ultrazvočnih preiskavah se pretežno uporablja ena sama sonda in zbrane informacije so iz osi širjenja valov. Če takšno enodelno sondo razdelimo na več elementov (polje) s širino, ki je precej manjša od dolžine, lahko vsakega od teh elementov obravnavamo kot linearen vir cilindričnih valov. Ena glavnih značilnosti ultrazvočnih faznih polj je sposobnost ustvarjanja fokusiranega ultrazvočnega snopa tako, da posamezni elementi polja dobivajo časovno odložene signale. Če je fokusiran snop zmožen opisati določeno območje, je možno ustvariti posnetek B-scan. Ta postopek se imenuje oblikovanje snopa.

Za ustvarjanje snopa s konstruktivnimi interferencami je posamezne elemente polja treba vzbujati s signali, ki so med seboj zamaknjeni za majhno časovno razliko. Na ta način je snop možno fokusirati v želeno točko. Zbrani signali so pred seštevanjem premaknjeni v času. Rezultat je signal A-scan, ki poudarja odziv želene točke izostritve in slabi odmeve iz ostalih točk preizkušanega materiala. Za homogen medij s konstantno hitrostjo valov je nato potrebno določiti razdalje posameznih elementov polja do točk izostritve. Časovni zamiki so pridobljeni iz razmerja med razdaljami in hitrostjo širjenja valov v mediju.

Cilj predstavljenega sistema je krmiliti polje 30 elementov z medsebojno razdaljo 1 mm, sposobno skenirati območje, ki sega od 10 do 60 mm v globino pod kotom ±30º. Posnetek nizke ločljivosti ima 2.500 slikovnih točk (50 x 50), kar ustreza ločljivosti 1 slikovna točka/mm v smeri pravokotno na polje (v globino) in najmanj 1,35 slikovne točke/mm v smeri kota. Časovni zamik je bil implementiran s pomočjo 50 MHz oscilatorja, ki omogoča najmanjši časovni zamik 20 ns. Z devet bitnimi števci lahko dosežemo največji zamik med krmilnimi signali 10,24 µs, kar za približno dvakrat presega specificirane zahteve.

Vhodna parametra, ki ju sistem potrebuje za preračun izostritve v določeni točki, sta polmer in kot, izhod pa je 30 signalov, ki so med seboj različno časovno zamaknjeni. Modul Calc_sin daje vrednosti sin za kot, ki ustreza točki izostritve glede na središče polja. Jezik VHDL funkcije sin ne podpira, zato je bila kot uveljavljena rešitev za programirljive naprave uporabljena iskalna tabela (LUT). Modul Calc_dist, ki računa kvadratni koren, uporablja razvoj v Taylorjevo vrsto. Sistem ima toliko vzporednih kanalov, kot je število elementov v polju, pri predstavljenem sistemu, torej 30. Modul Calc_delay_n iz množice prej izračunanih razdalj in z največjo vrednostjo izračuna časovne zamike. Izračunane vrednosti zamikov je nato treba pretvoriti v večkratnike časovne enote sistema, ki znaša 20 ns. Končni modul Contn ima funkcijo števca. Ko so naloženi vsi signali L_contn, se vsem števcem pošlje krmilni signal, ki sproži štetje od izhodiščne vrednosti L_contn. Krmilnim signalom polja Sn se spremeni stanje, ko vsak števec doseže največjo vrednost, s čimer vsak element dobi ustrezen zamik.

Za novo točko izostritve se cel postopek ponovi z novimi parametri kota in polmera.Ena glavnih prednosti predstavljene implementacije je visoka stopnja miniaturizacije v primerjavi

s kovencionalnimi elektronskimi vezji. Uporabljeni FPGA zasede površino, manjšo od 3 cm2. Je tudi zelo vsestranski, saj je cel krmilni sistem možno namestiti v en sam standardni FPGA, ki ga je možno enostavno programirati in nadgrajevati. Pomembna prednost je tudi velik prihranek na stroških, saj vizualizacijo omogoča že enostaven prenosni računalnik.

Razvita strojna oprema omogoča krmiljenje funkcije oddajanja polja. Prihodnje delo bo usmerjeno na sprejem in obdelavo signalov za ustvarjanje posnetka B-scan notranjosti preiskovanih struktur.© 2011 Strojniški vestnik. Vse pravice pridržane.Ključne besede: ultrazvočne preiskave, fazno polje, B-scan, FPGA


*Naslov avtorja za dopisovanje: Univerza v Mariboru, Fakulteta za strojništvo, Smetanova 17, 2000 Maribor, Slovenija, [email protected] SI 29

Nadzor stanja rezalnega orodja v realnem času pri rezkanju

Franci Čuš - Uroš Župerl* Univerza v Mariboru, Fakulteta za strojništvo, Slovenija

Namen članka je predstaviti sistem za nadzor rezalnega orodja pri odrezovalnih procesih. Glavni cilj raziskave je bil izdelati sistem nadzora orodja, ki je sposoben v realnem času identificirati obrabo oziroma poškodbe rezalnega orodja in ustrezno korigirati nadaljnji proces obdelave. To mu omogoča inovativna zgradba, ki se sestoji iz kombinacije nevronskega sistema odločanja in napovedovanja obrabe orodja ANFIS (Adaptive Neural Fuzzy Inference System).

Fundamentalna omejitev raziskave je bila izdelava enosenzorskega nadzornega sistema, ki je zanesljiv in učinkovit kot komercialni sistemi, hkrati pa mnogo cenejši kot obstoječi večsenzorski pristopi. Največ raziskovalnega napora je bilo namenjenega izbiri primernih senzorjev in pripadajočih metod za obdelavo signalov, ki zagotavljajo minimalno napako klasifikacije defektov procesa.

Glavna predpostavka raziskave je, da signali izmerjenih rezalnih sil vsebujejo največ uporabnih informacij o stanju orodja. Zato je uporabljena metoda ANFIS, ki iz signalov izmerjenih rezalnih sil izlušči pomembne značilnosti o stanju orodja. Rezultati potrjujejo, da metoda ANFIS pripravi uporaben lingvistični model za napovedovanje obrabe orodja na osnovi znanja, zbranega v naučeni nevronski mreži. Metoda napoveduje obrabo orodja na osnovi korelacije med rezalno silo in obrabo proste ploskve orodja. Izvedena je bila serija eksperimentov za določitev povezave med obrabo, rezalno silo in rezalnimi parametri. Za merjenje rezalnih sil je bil uporabljen piezoelektrični dinamometer, vključen v sistem zajemanja podatkov Labview. Obraba proste ploskev na rezalnem robu je bila simultano spremljana na orodnem mikroskopu. Eksperimenti potrjujejo, da ima aksialna komponenta rezalne sile največji vpliv na napovedano obrabo.

Z eksperimenti pridobljeni podatki so bili nato uporabljeni pri ANFIS modeliranju. Naučen model ANFIS je bil nato integriran z nevronsko mrežo za identifikacijo stanja orodja (brezhibno, obrabljeno, poškodovano). Naloga nevronske mreže kot odločitvenega sistema je prepoznavanje tipičnih poškodb orodja na osnovi napovedane obrabe in signalov izmerjenih sil. Na osnovi obsežnega preizkušanja je bilo ugotovljeno, da nadzorni sistem spremlja proces visokohitrostne obdelave z visoko zanesljivostjo in ga zaustavi v primeru poškodb oziroma prekoračene dovoljene obrabe orodja. Na ta način je sistem v 97% preprečil nadaljnje poškodbe obdelovanca in orodja.

Nov prispevek k teoriji optimalnega vodenja odrezavanja je vključevanje in uporaba sodobnih informacijskih orodij in umetnih samoučečih sistemov, ki abstrahirajo delovanje človeškega razuma, v procesih napovedovanja rezalnih veličin v realnem času. Na tej osnovi je definirano tudi orodje za on-line nadzor orodja. Prispevek k praksi je očiten, saj je sistem razvit za konkreten stroj. Z uspešno realizacijo zastavljenih idej se bodo znatno izboljšale lastnosti obstoječih obdelovalnih sistemov. Opisani sistem bo imel največji pomen pri visokohitrostni obdelavi v slovenskih orodjarnah.

Glavne prednosti sistema za kovinsko-predelovalno industrijo so: krajši čas izdelave in manjši stroški obdelave, razbremenitev operaterja, daljša življenjska doba orodja in stroja, visoko- kakovostna proizvodnja z minimalnimi napakami ter večja avtomatizacija procesa.

Cilj nadaljnjih raziskav bo povečati učinkovitost odločitvene komponente sistema in preveriti, s katerimi novimi tehnikami bi lahko zmanjšali napako detekcije. Izdelani sistem vključuje enostavne fiksne omejitve pri detekciji poškodb orodja. V prihodnje bi bilo fiksne omejitve primerno nadomestiti s samoprilagodljivimi omejitvami.© 2011 Strojniški vestnik. Vse pravice pridržane.Ključne besede: Nadzor orodja, oblikovno frezanje, obraba, lom orodja, ANFIS, nevronska mreža


*Naslov avtorja za dopisovanje: Univerza v Črni Gori, Fakulteta za strojništvo, Cetinjska bb, 81000 Podgorica, Črna Gora, [email protected] 30

Izboljšanje zmogljivosti poslovnih procesov z vzpostavitvijo analogije: sistem menedžmenta kakovosti – človeški organizem

Aleksandar Vujović1, * – Zdravko Krivokapić1 – Mirko Soković2

1 Univerza v Črni Gori, Fakulteta za strojništvo, Črna Gora 2 Univerza v Ljubljani, Fakulteta za strojništvo, Slovenia

Osnovni namen članka je predstaviti analogijo med popolnostjo delovanja človeškega organizma in možno preslikave le-tega na procesno modelirane organizacijske strukture, z namenom izboljšanja organizacijske zmožnosti in doseganja poslovne odličnosti pri delovanju podjetij.

Problem, ki je obravnavan v prispevku, se nanaša na vzpostavljanje povezave med zahtevami sistema menedžmenta v procesno modelirani organizacijski strukturi in modelom evropske nagrade za poslovno odličnost. V ta namen se vzpostavlja in izračunava stopnja pomembnosti posameznih zahtev za doseganje vrhunskih rezultatov poslovanja. Prav tako se določata in izračunavata tudi celotna in kritična »stopnja pripravljenosti« za doseganje poslovne odličnosti. Podani so tudi ukrepi in področja, kjer je treba delovati, da bi dosegli vrhunsko organizacijsko obliko.

Za doseganje ciljev so v članku uporabljene naslednje znanstveno-raziskovalne metode: induktivno sklepanje v fazi pridobivanja splošno uporabnega znanja na podlagi posebnih dejstev (edinstven realni eksperiment); deduktivno sklepanje in preverjanje splošnega znanja na posameznih primerih; analogije pri povezovanju področij; naravni eksperimenti (ex post facto – predhodna dejstva), pridobivanje znanja in sklepanje iz izkušenj velikega števila podjetij; ekspertne analize in sklepanja.

Osnovo prispevka predstavlja razmeroma mlada in ne pogosto uporabljena teorija CBR (Case Base Reasoning), ki je bila razvita kot osnova za delovanje dinamičnih spominov. Na osnovi razvitega in uporabljenega sistema DSS (Decision Support Systems) se izvaja skladiščenje in organizacija podatkov, kakor tudi določeni izračuni in izvajanja, ki so potrebna za podporo poznejšim aktivnostim. DSS je razvit v okolju MS Access s podporo za Visual Basic. Vpeljani pojem “koeficient pomembnosti” se izračunava z metodo AHP (Analytic Hierarchy Process) s pripadajočim softverskim modulom Expert Choice. Za klasifikacijo, izpostavljanje in prikazovanje so bile uporabljeni ekspertni sistemi in diagrami Pareto.

Rezultati kažejo, da so storitvene organizacije pri doseganju najboljših (svetovno primerljivih) rezultatov v prednosti pred proizvodnimi. Kljub temu pa imajo proizvodne organizacije boljše predpogoje, da hitreje dosežejo poslovno odličnost pri nekaterih kritičnih področjih sistema menedžmenta, s tem pa tudi v celoti. V prispevku so predstavljena tudi prednostna področja, kjer je treba okrepiti delovanje, da bi dosegli rezultate najboljših svetovno primerljivih organizacij. Na osnovi tega pristopa je bil izdelan in v realnih pogojih (okolju) preizkušen učinkovit ekspertni sistem, ki služi kot sistem za preventivo in podporo na poti k najboljšim karakteristikam (zmožnostim) organizacije.

Članek predstavlja prispevek k raziskavam vpliva sistema menedžmenta kakovosti na izboljšanje vseh organizacijskih karakteristik, tudi tistih finančnega značaja. Novost pri tem je tudi vzpostavljena analogija in pristop povezovanja zahtev standarda z modelom poslovne odličnosti, s pomočjo novih pojmov »koeficienta pomembnosti« za doseganje poslovne odličnosti in »stopnje pripravljenosti«. Prispevek je pomemben tudi zaradi baze eksperimentalnih podatkov. Rezultati članka lahko koristijo tako posameznikom, ki imajo znanstvene vzgibe na obravnavanih področjih, kakor tudi organizacijam, ki želijo svoje rezultate povzdigniti na nivo najboljših svetovno primerljivih organizacij. © 2011 Strojniški vestnik. Vse pravice pridržane.Key words: izboljšanje, značilnosti procesov, analogija, sistem za podporo odločanju, stopnja pripravljenosti, koeficient pomembnosti

Strojniški vestnik - Journal of Mechanical Engineering 57(2011)2, SI 31-32Navodila avtorjem

SI 31

Navodila avtorjem

Članke pošljite na naslov:Strojniški vestnik -Journal of Mechanical EngineeringAškerčeva 6, 1000 Ljubljana, SlovenijaTel.: 00386 1 4771 137Faks: 00386 1 2518 567E-mail: [email protected] [email protected]

Članki morajo biti napisani v angleškem jeziku. Strani morajo biti zaporedno označene. Prispevki so lahko dolgi največ 10 strani. Daljši članki so lahko v objavo sprejeti iz posebnih razlogov, katere morate navesti v spremnem dopisu. Kratki članki naj ne bodo daljši od štirih strani.

Navodila so v celoti na voljo v rubriki “Informacija za avtorje” na spletni strani revije: http://en.sv-jme.eu/

Prosimo vas, da članku priložite spremno pismo, ki naj vsebuje:1. naslov članka, seznam avtorjev ter podatke

avtorjev;2. opredelitev članka v eno izmed tipologij; izvirni

znanstveni (1.01), pregledni znanstveni (1.02) ali kratki znanstveni članek (1.03);

3. opredelitev, da članek ni objavljen oziroma poslan v presojo za objavo drugam;

4. zaželeno je, da avtorji v spremnem pismu opredelijo ključni doprinos članka;

5. predlog dveh potencialnih recenzentov, ter kontaktne podatke recenzentov. Navedete lahko tudi razloge, zaradi katerih ne želite, da bi določen recenzent recenziral vaš članek.

OBLIKA ČLANKA

Članek naj bo napisan v naslednji obliki:- Naslov, ki primerno opisuje vsebino članka.- Povzetek, ki naj bo skrajšana oblika članka

in naj ne presega 250 besed. Povzetek mora vsebovati osnove, jedro in cilje raziskave, uporabljeno metodologijo dela, povzetek rezultatov in osnovne sklepe.

- Uvod, v katerem naj bo pregled novejšega stanja in zadostne informacije za razumevanje ter pregled rezultatov dela, predstavljenih v članku.

- Teorija.

- Eksperimentalni del, ki naj vsebuje podatke o postavitvi preskusa in metode, uporabljene pri pridobitvi rezultatov.

- Rezultati, ki naj bodo jasno prikazani, po potrebi v obliki slik in preglednic.

- Razprava, v kateri naj bodo prikazane povezave in posplošitve, uporabljene za pridobitev rezultatov. Prikazana naj bo tudi pomembnost rezultatov in primerjava s poprej objavljenimi deli. (Zaradi narave posameznih raziskav so lahko rezultati in razprava, za jasnost in preprostejše bralčevo razumevanje, združeni v eno poglavje.)

- Sklepi, v katerih naj bo prikazan en ali več sklepov, ki izhajajo iz rezultatov in razprave.

- Literatura, ki mora biti v besedilu oštevilčena zaporedno in označena z oglatimi oklepaji [1] ter na koncu članka zbrana v seznamu literature.

Enote - uporabljajte standardne SI simbole in okrajšave. Simboli za fizične veličine naj bodo v ležečem tisku (npr. v, T, n itd.). Simboli za enote, ki vsebujejo črke, naj bodo v navadnem tisku (npr. ms-1, K, min, mm itd.)

Okrajšave naj bodo, ko se prvič pojavijo v besedilu, izpisane v celoti, npr. časovno spremenljiva geometrija (ČSG).

Pomen simbolov in pripadajočih enot mora biti vedno razložen ali naveden v posebni tabeli na koncu članka pred referencami.

Slike morajo biti zaporedno oštevilčene in označene, v besedilu in podnaslovu, kot sl. 1, sl. 2 itn. Posnete naj bodo v ločljivosti, primerni za tisk, v kateremkoli od razširjenih formatov, npr. BMP, JPG, GIF. Diagrami in risbe morajo biti pripravljeni v vektorskem formatu, npr. CDR, AI.

Vse slike morajo biti pripravljene v črno-beli tehniki, brez obrob okoli slik in na beli podlagi. Ločeno pošljite vse slike v izvirni obliki Pri označevanju osi v diagramih, kadar je le mogoče, uporabite označbe veličin (npr. t, v, m itn.). V diagramih z več krivuljami, mora biti vsaka krivulja označena. Pomen oznake mora biti pojasnjen v podnapisu slike.

Tabele naj imajo svoj naslov in naj bodo zaporedno oštevilčene in tudi v besedilu poimenovane kot Tabela 1, Tabela 2 itd.. Poleg fizikalne veličine, npr t (v ležečem tisku), mora biti v oglatih oklepajih navedena tudi enota. V tabelah naj se ne podvajajo podatki, ki se nahajajo v besedilu.

Strojniški vestnik - Journal of Mechanical Engineering 57(2011)2, SI 31-32

SI 32

Potrditev sodelovanja ali pomoči pri pripravi članka je lahko navedena pred referencami. Navedite vir finančne podpore za raziskavo.

REFERENCE

Seznam referenc MORA biti vključen v članek, oblikovan pa mora biti v skladu s sledečimi navodili. Navedene reference morajo biti citirane v besedilu. Vsaka navedena referenca je v besedilu oštevilčena s številko v oglatem oklepaju (npr. [3] ali [2] do [6] za več referenc). Sklicevanje na avtorja ni potrebno.

Reference morajo biti oštevilčene in razvrščene glede na to, kdaj se prvič pojavijo v članku in ne po abecednem vrstnem redu. Reference morajo biti popolne in točne. Vse neangleške oz. nenemške naslove je potrebno prevesti v angleški jezik z dodano opombo (in Slovene) na koncu Navajamo primere:Članki iz revij:Priimek 1, začetnica imena, priimek 2, začetnica imena (leto). Naslov. Ime revije, letnik, številka, strani.[1] Zadnik, Ž., Karakašič, M., Kljajin, M.,

Duhovnik, J. (2009). Function and Functionality in the Conceptual Design Process. Strojniški vestnik – Journal of Mechanical Engineering, vol. 55, no. 7-8, p. 455-471.

Ime revije ne sme biti okrajšano. Ime revije je zapisano v ležečem tisku. Knjige:Priimek 1, začetnica imena, priimek 2, začetnica imena (leto). Naslov. Izdajatelj, kraj izdaje[2] Groover, M. P. (2007). Fundamentals of Modern

Manufacturing. John Wiley & Sons, Hoboken.Ime knjige je zapisano v ležečem tisku. Poglavja iz knjig:Priimek 1, začetnica imena, priimek 2, začetnica imena (leto). Naslov poglavja. Urednik(i) knjige, naslov knjige. Izdajatelj, kraj izdaje, strani. [3] Carbone, G., Ceccarelli, M. (2005). Legged

robotic systems. Kordić, V., Lazinica, A., Merdan, M. (Eds.), Cutting Edge Robotics. Pro literatur Verlag, Mammendorf, p. 553-576.

Članki s konferenc:Priimek 1, začetnica imena, priimek 2, začetnica imena (leto). Naslov. Naziv konference, strani.[4] Štefanić, N., Martinčević-Mikić, S., Tošanović,

N. (2009). Applied Lean System in Process Industry. MOTSP 2009 Conference Proceedings, p. 422-427.

Standardi:Standard (leto). Naslov. Ustanova. Kraj.[5] ISO/DIS 16000-6.2:2002. Indoor Air – Part 6:

Determination of Volatile Organic Compounds in Indoor and Chamber Air by Active Sampling on TENAX TA Sorbent, Thermal Desorption and Gas Chromatography using MSD/FID. International Organization for Standardization. Geneva.

Spletne strani:Priimek, Začetnice imena podjetja. Naslov, z naslova http://naslov, datum dostopa.[6] Rockwell Automation. Arena, from http://www.

arenasimulation.com, accessed on 2009-09-27.

RAZŠIRJENI POVZETEK

Ko je članek sprejet v objavo, avtorji pošljejo razširjeni povzetek na eni strani A4 (približno 3.000 - 3.500 znakov). Navodila za pripravo razširjenega povzetka so objavljeni na spletni strani http://sl.sv-jme.eu/informacije-za-avtorje/.

AVTORSKE PRAVICE

Avtorji v uredništvo predložijo članek ob predpostavki, da članek prej ni bil nikjer objavljen, ni v postopku sprejema v objavo drugje in je bil prebran in potrjen s strani vseh avtorjev. Predložitev članka pomeni, da se avtorji avtomatično strinjajo s prenosom avtorskih pravic SV-JME, ko je članek sprejet v objavo. Vsem sprejetim člankom mora biti priloženo soglasje za prenos avtorskih pravic, katerega avtorji pošljejo uredniku. Članek mora biti izvirno delo avtorjev in brez pisnega dovoljenja izdajatelja ne sme biti v katerem koli jeziku objavljeno drugje.

Avtorju bo v potrditev poslana zadnja verzija članka. Morebitni popravki morajo biti minimalni in poslani v kratkem času. Zato je pomembno, da so članki že ob predložitvi napisani natančno.

Avtorji lahko stanje svojih sprejetih člankov spremljajo na http://en.sv-jme.eu/.

PLAČILO OBJAVE

Domači avtorji vseh sprejetih prispevkov morajo za objavo plačati prispevek, le v primeru, da članek presega dovoljenih 10 strani oziroma za objavo barvnih strani v članku, in sicer za vsako dodatno stran 20 EUR ter dodatni strošek za barvni tisk, ki znaša 90,00 EUR na stran.

Strojniški vestnik - Journal of Mechanical Engineering 57(2011)2, SI 33Osebne objave

SI 33

Doktorat in diplome

DOKTORAT

Na Fakulteti za strojništvo Univerze v Ljubljani je z uspehom obranil svojo doktorsko disertacijo:

dne 13. januarja 2011 Anže JERIČ z naslovom: »Tvorjenje kapljic s soosnim vnosom laserske svetlobe v kovinsko žico« (mentor: prof.dr. Edvard Govekar, somentor: akad. prof. dr. Igor Grabec);

Poizkusi laserskega tvorjenja kapljic iz kovinske žice kažejo na relativno veliko variabilnost procesa, ki se odraža predvsem v raztrosu lege odloženih kapljic. Variabilnost je posledica kompleksnosti sistema zaradi velikega števila vplivnih parametrov in asimetrije procesa oziroma neenakomernega vnosa energije v žico. Na osnovi rezultatov raziskav je bil zasnovan, izdelan in okarakteriziran nov sistem s soosnim vnosom laserske svetlobe v kovinsko žico, ki ob zmanjšani kompleksnosti zagotavlja enakomeren vnos energije v žico. Okarakterizirani so vplivni parametri in procesno okno laserskega tvorjenja kapljic. Podrobneje sta obravnavana procesa tvorjenja kapljice z enim ter več zaporednimi laserskimi bliski. V obeh primerih enakomeren in osno simetričen vnos energije omogoča nov nadzorovan način ločevanja viseče kapljice, ki temelji na Plateau‒Rayleigh-jevi nestabilnosti stolpca fluida. Zato rezultati kažejo tudi značilno zmanjšanje variabilnosti procesa. Raziskali smo tudi uporabo lasersko tvorjenih kapljic za kapljično spajanje tanke pocinkane pločevine. Rezultati poskusov kažejo, da razviti postopek laserskega kapljičnega spajanja omogoča izvedbo estetskih, mehansko kakovostnih in korozijsko visoko odpornih soležnih in kotnih spojev tanke pocinkane pločevine.

*

DIPLOMIRALI SO

Na Fakulteti za strojništvo Univerze v Ljubljani so pridobili naziv univerzitetni diplomirani inženir strojništva:

dne 31. januarja 2011:Miha Lebič z naslovom: »Razvojno

vrednotenje orodja za večstopenjsko preoblikovanje

in rezanje pločevine« (mentor: doc. dr. Jernej Klemenc, , somentor: doc. dr. Tomaž Pepelnjak);

Tomislav Vargec z naslovom: »Energetska sanacija javne stavbe« (mentor: prof. dr. Alojz Poredoš);

Lluis Maria Anfruns Damians z naslovom: »Izkoriščanje sončne energije z integriranimi fotonapetostnimi in toplotnimi sistemi« (mentor: prof. dr. Sašo Medved, somentor: doc. dr. Ciril Arkar).

*

Na Fakulteti za strojništvo Univerze v Ljubljani so pridobili naziv diplomirani inženir strojništva:

dne 13. januarja 2011:Aljaž Birsa z naslovom: »Metode

preizkušanja netesnosti plinovodnega omrežja« (mentor: izr. prof. dr. Ivan Bajsić);

Tomaž Bren z naslovom: »Identifikacija družin izdelkov« (mentor: prof. dr. Marko Starbek, somentor: doc. dr. Janez Kušar);

Blaž Hudernik z naslovom: »Program vzdrževanja jadralnega letala LET BLANIK L 13« (mentor: pred. mag. Borut Horvat, somentor: doc. dr. Tadej Kosel);

Tadej Smrdel z naslovom: »Sočasno inženirstvo pri osvajanju novega izdelka iz termoplasta« (mentor: prof. dr. Karl Kuzman).

*

Na Fakulteti za strojništvo Univerze v Mariboru so pridobili naziv diplomirani inženir strojništva:

dne 27. januarja 2011:Zoran Lukežić z naslovom: »Uporaba metode

šest sigma pri vodenju kakovosti izdelave hladilnika HKS 3666« (mentor: izr. prof. dr. Bojan Ačko, somentor: izr. prof. dr. Borut Buchmeister);

Urban Rožej z naslovom: »Optimizacija razmestitve strojev v valjarni s pomočjo genetskega algoritma« (mentor: izr. prof. dr. Miran Brezočnik, somentor: dr. Miha Kovačič).

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