June 2015 4 Number 2 - Ingineria Automobilului · June 2015 Volume 21 4 th Series Number 2 RoJAE Ro...

ISSN ____ – ____ (Online, English) ISSN 1842 – 4074 (Print, Online, Romanian)

June 2015 Volume 21

4 th Series Number 2

RoJAE

Romanian Journal of Automotive Engineering

The Journal of the Society of Automotive Engineers of Romania www.siar.ro www.ro-jae.ro

SIAR – Society of Automotive Engineers of Romania is member of:

FISITA - International Federation of Automotive Engineers Societies www.fisita.com

EAEC - European Automotive Engineers Cooperation

RoJAE


Societatea Inginerilor de Automobile din România Society of Automotive Engineers of Romania

www.siar.ro SIAR – The Society of Automotive Engineers of Romania is the professional organization of automotive engineers, an independent legal entity, non-profit, active member of FISITA (Fédération Internationale des Sociétés d'Ingénieurs des Techniques de l'Automobile - International Federation of Automotive Engineering Societies) and EAEC (European Cooperation Automotive Engineers). Founded in January 1990 as a professional association, non-governmental, SIAR’s main objectives are: development and increase the exchange of professional information, promoting Romanian scientific research results, new technologies specific to automotive industry, international cooperation. Shortly after its constitution, SIAR was affiliated to FISITA - International Federation of Automotive Engineers and EAEC - European Conference of Automotive Engineers, thus ensuring full involvement in specific activities undertaken globally. In order to help promoting the science and technology in the automotive industry, SIAR is issuing 4 times a year RIA - Journal of Automotive Engineers (on paper in Romanian and electronically in Romanian and English). The organization of national and international scientific meetings with a large participation of experts from universities and research institutes and economic environment is an important part of SIAR’s. In this direction, SIAR holds an annual scientific event with a wide international participation. The SIAR annual congress is hosted successively by large universities that have ongoing programs of study in automotive engineering. Developing relationships with the economic environment is a constant concern. The presence in Romania of OEMs and their suppliers enables continuous communication between industry and academia. Actually, a constant priority in SIAR’s activity is to ensure optimal framework for collaboration between universities and research, industry and business specialists.

Honorary Committee of SIAR

Pascal CANDAU Renault Technologie Roumanie

www.renault-technologie-roumanie.com George-Adrian DINCA

Romanian Automotive Register www.rarom.ro

Florian MIHUT The National Union of Road Hauliers from Romania

www.untrr.ro Werner MOSER AVL Romania www.avl.com

The Society of Automotive Engineers of Romania President Adrian Constantin CLENCI University of Pitesti, Romania E-mail: Honorary President Mihai Eugen NEGRUS University „Politehnica” of Bucharest, Romania Vice-Presidents Cristian Nicolae ANDREESCU University „Politehnica” of Bucharest, Romania Nicolae BURNETE Technical University of Cluj-Napoca, Romania Anghel CHIRU „Transilvania” University of Brasov, Romania Victor OTAT University of Craiova, Romania Ion TABACU University of Pitesti, Romania General Secretary Minu MITREA Military Technical Academy of Bucharest, Romania

RoJAE


CONTENTS

Volume 21, Issue No. 2 June 2015

Study on the Three-Point Bending of Composite Materials Reinforced with Unidirectional Flax Fibers Alin HALOIU and Stefan TABACU................................................................................................

39 Manufacturing Processes for Composite Materials Steering Column Parts Lucian RAD and Anghel CHIRU.....................................................................................................

46

Probability Distributions Used to Study the Reliability of Vehicles Used in Ornamental Rock Quarries Monia Camelia DASCAR SECARA and Marin Silviu NAN.............................................................

56 A Regard Over the Implications of the New Test Cycles Regarding the Engine Calibration Maria Alina TUȚĂ, Florian IVAN and George Marian TRICĂ .......................................................

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The collections of the journals of the Society of Automotive Engineers of Romania are avaibles at the Internet website www.ro-jae.ro. The Romanian Journal of Automotive Engineering is indexed/abstracted in Directory of Science, WebInspect, GIF - Institute for Information Resources, MIAR - Information Matrix for the Analysis of Journals - Barcelona University, Georgetown University Library, SJIF - Scientific Journal Impact Factor - Innovative Space of Scientific Research, DRJI - Directory of Research Journal Indexing - Solapur University, Platforma Editorială Română SCIPIO – UEFISCU, International Society of Universal Research in Sciences, Pak Academic Search, Index Copernicus International RoJAE 21(2) 35 – 74 (2015) ISSN ____ – ____ (Online, English) ISSN 1842 – 4074 (Print, Online, Romanian)

RoJAE

Romanian Journal of Automotive Engineering Editor in Chief Cornel STAN West Saxon University of Zwickau, Germany E-mail: [email protected] Executive Editor Nicolae ISPAS „Transilvania” University of Brasov, Romania E-mail: [email protected] Deputy Executive Editor Radu CHIRIAC University „Politehnica” of Bucharest, Romania E-mail: [email protected] Ion COPAE Military Technical Academy of Bucharest, Romania E-mail: [email protected] Stefan TABACU University of Pitesti, Romania E-mail: [email protected] Editors Ilie DUMITRU University of Craiova, Romania E-mail: [email protected] Marin Stelian MARINESCU Military Technical Academy of Bucharest, Romania E-mail: [email protected] Adrian SACHELARIE „Gheorghe Asachi” Technical University of Iasi, Romania E-mail: [email protected] Marius BATAUS University „Politehnica” of Bucharest, Romania E-mail: [email protected] Cristian COLDEA Technical University of Cluj-Napoca, Romania E-mail: [email protected] George DRAGOMIR University of Oradea, Romania E-mail: [email protected]

Advisory Editorial Board Dennis ASSANIS

University of Michigan, USA Rodica A. BARANESCU

Chicago College of Engineering, USA Nicolae BURNETE

Technical University of Cluj-Napoca, Romania Giovanni CIPOLLA

Politecnico di Torino, Italy Felice E. CORCIONE

Engines Institute of Naples, Italy Georges DESCOMBES

Conservatoire National des Arts et Metiers de Paris, France Cedomir DUBOKA

University of Belgrade, Serbia Pedro ESTEBAN

Institute for Applied Automotive Research Tarragona, Spain Radu GAIGINSCHI

„Gheorghe Asachi” Technical University of Iasi, Romania Eduard GOLOVATAI-SCHMIDT

Schaeffler AG & Co. KG Herzogenaurach, Germany Peter KUCHAR

University for Applied Sciences, Konstanz, Germany Mircea OPREAN

University „Politehnica” of Bucharest, Romania Nicolae V. ORLANDEA

University of Michigan, USA Victor OTAT

University of Craiova, Romania Andreas SEELINGER

Institute of Mining and Metallurgical Engineering, Aachen, Germany

Ulrich SPICHER Kalrsuhe University, Karlsruhe, Germany

Cornel STAN West Saxon University of Zwickau, Germany

Dinu TARAZA Wayne State University,USA

The Journal of the Society of Automotive Engineers of Romania www.ro-jae.ro www.siar.ro Copyright © SIAR Production office: The Society of Automotive Engineers of Romania (Societatea Inginerilor de Automobile din România) Universitatea „Politehnica” din Bucuresti, Facultatea de Transporturi, Splaiul Independentei Nr. 313 060042 Bucharest ROMANIA Tel.: +4.021.316.96.08 Fax: +4.021.316.96.08 E-mail: [email protected] Staff: Prof. Minu MITREA, General Secretary of SIAR Subscriptions: Published quarterly. Individual subscription should be ordered to the Production office. Annual subscription rate can be found at SIAR website http://www.siar.ro. The members of the Society of Automotive Engineers of Romania receive free a printed copy of the journal (in Romanian).

RoJAE vol. 21 no. 2 / June2015 ISSN ____ – ____ (Online, English) Romanian Journal of Automotive Engineering ISSN 1842 – 4074 (Print, Online, Romanian)

39

STUDY ON THE THREE-POINT BENDING OF COMPOSITE MATERIALS REINFORCED WITH UNIDIRECTIONAL FLAX FIBERS

Alin HALOIU, Stefan TABACU*

University of Pitesti, Str. Targu din Vale, Nr. 1, 110040 Pitesti, Romania

(Received 23 January 2015; Revised 30 March 2015; Accepted 4 May 2015)

Abstract: In this research fabrication method for bio-source composite material – epoxy resin reinforced with UD flax fibers - has been presented. Three points bending tests have been conducted on a test machine developed in the Research Center “Automotive Engineering”. The developing and manufacturing of test machine, was also presented. The obtained mechanical properties for the composite material reinforced with UD flax fibers and with epoxy resin were analyzed and conclusions were drawn from these results. This stage is fundamental for the use of these materials in automotive parts. This fiber presents the best mechanical properties among most usually vegetal fibers.

Key-Words: bio-source, plastic parts, flax fibers, three point bending, mechanical properties.

NOMENCLATURE UD: Unidirectional Fibers FE: Flax Epoxy 1. INTRODUCTION At the moment, about 99% of the parts made of plastic, with wide usage are reinforced with glass fibers [12]. However, nowadays when environmental standards are getting more severe (95% of the vehicle weight must be recyclable), glass/polymer composites materials presents disadvantages related to environmental protection [4]. Composite materials reinforced with natural fibers and/or biopolymers have developed significantly in recent years due to their significant processing advantages, biodegradability, low cost, relative low density, high specific strength and renewable origin. These composites are intended to be used more in the near future, especially in Europe, where, due to both pressure from legislation [6], [7], [8] and public is increasing. Adhesion of natural fibers and the matrix will remain the key problem in terms of overall performance, as this dictates the final properties of the composites. Reducing the weight of the vehicle is a well-known strategy for improving the fuel consumption of motor vehicles and represents a significant opportunity to reduce fuel consumption in the transport sector. By reducing the weight of the vehicle, the forces of inertia that the engine must overcome are smaller, and the power required for the propulsion of the vehicle is thus reduced. A large number of studies and research have been conducted on the topic of three points bending of composite materials [5], [15], [16], but only few studies have been conducted on the topic of three points bending of bio-source composite materials and especially of composites reinforced with flax fibers. 2. BIO-SOURCE MATERIAL MANUFACTURING 2.1 Matrix The bio-source material was manufactured in the laboratory for laminated parts of DE-TP RTR within the Automobile Dacia Plant. The resin used is an epoxy resin, NEUKADUR EP 986[1], provided by BEST TOOL and produced by ALTROPOL. The epoxy resin is commonly used in the manufacture of laminated prototype parts. This resin is reinforced with glass fibers or carbon fibers.

* Corresponding author e-mail: [email protected]


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The matrix consists of a mixture of NEUKADUR EP 986 resin with hardener NEUKADUR 242 with density 1100kg/m3 and 950kg/m3. On the basis of the ratio between the masses of the two constituents which is 100/30, the matrix density NEUKADUR EP 986/NEUKADUR 242 is 1125kg/m3. 2.2 Reinforcements The reinforcements used are flax fibers with a density of 1350kg/m3[3]. Flax fibers are in the form of unidirectional fibers (UD), dried in rolls, provided by BCOMP Switzerland. The fibers are used «unaltered» without special treatment in their implementation. Fibers properties are presented in table 1 in average value. Flax fibers are sewn with polyester yarn. The distance between the sewing is 10 mm.

Table 1. Properties of UD flax fibers

Reinforcements Surface density

ms

Density

ρf Sewing thread

Distance between sewing

Flax UD 274(0.2) 1350 Polyester 10 mm 2.3 Composite material 2.3.1 Manufacturing process A composite plate with dimensions of 450x450 mm from flax/epoxy, with fibers orientation at 0 °, was manufactured by vacuuming (VARTM) at a pressure of ≈0,85 bar, using a vacuum pump Mils MINIVAC E40. This pressure is constant throughout the manufacturing process. Each layer is impregnated with the mixture (resin and hardener) and is arranged according to the desired direction (figure 1). All layers are placed in a plane mold and then surface is covered with a vacuum bag. The vacuum process has a duration of 24 hours[1], in which time an automatic correction of vacuuming pressure is assured.

Figure 1. Steps in the manufacturing process 2.3.2 Physical properties In order to calculate the physical properties of flax/epoxy composite material plate, 5 samples were measured with dimensions 30x30mm. The values obtained, in average value, are presented in table 2. The parameters t, ρc, Vf indicates the thickness of the laminate, the density and volume of fibers. The notation FE refers to the composite material flax/epoxy.

Table 2. Phisycal properties of flax/epoxy composite

Reinforcements Reinforcements type Notation Stratification t

[mm] ρc

[kg/m3] Vf

[%] Flax UD FE_0 [0]6 2.64 1065 46.1


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The laminate type [0]6 consists of 6 elementary layers arranged at 0 °. Fibers ratio is calculated according to the relation (1) given by ASTM D 3171-99 [2]. Where: n is the number of layers, ms weight per unit area, ρf the density of the fibers and t composite plate thickness.

(1) In the equation (1), the influence of pressure is translated through the thickness of the composite material obtained. To calculate the volume of fiber per plate, I got 5 square plate samples of size 30 x 30 mm. Each of these samples was measured 5 times. The density of the composite was calculated with equation (2), where m is the mass of the sample and its volume V.

(2) From the composite plate I cut out samples with dimensions of 100x15 mm (figure 2), using a circular saw equipped with abrasive disk which rotates at a speed ~1000rot/min, without liquid for cooling, so that the fibers do not absorb water. The composite rectangular strips were then deburred with abrasive paper of film grain: P60, P150 and P240 and used in experimental trials at the three-point bending tests. In figure 3 we can observe a sample prepared for bending test.

Figure 2. Geometrical dimensions of testing specimen

Figure 3. Test specimen for three points bending 3. Three points bending An experimental study on three-point bending was conducted to determine the breaking force and maximum deflection for the bio-source composite. Specimens of bio-source material have been tested in 3-point bending on a testing machine (figure 4), designed and developed within the Research Centre "Automotive Engineering" of the Faculty of Mechanics and Technology, Pitesti. The testing machine was equipped with an air cylinder type: CM2B25-50, produced by SMC, which has the following characteristics [14]:

- the diameter of the cylinder = 25 mm; - maximum pressure 1 MPa; - maximum stroke: 50 mm.

For the tests, the air cylinder was powered with compressed air using an air compressor. Also, the device has been equipped with a force transducer, type U9B, for measuring the tension/compression forces, with the capacity of 10kN produced by company HBM and having the following main features [10]:

- supply voltage: 1mV; - nominal sensitivity: 1mV/V - nominal measured strength: 10kN. - accuracy class: 0,5.


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Figure 4. Testing machine for three points bending tests, data acquisition system and PC The maximum pressure developed by the air cylinder was limited to 6 bar using a relay with analog extension module that allows programming and direct control of the device. This relay has an output voltage between 0-10V for a pressure between 0-9 bar [13]. Figure 5 shows the program designed and implemented with modular analog extension relay, to limit the pressure, achieved with the help of Zelio software. The testing machine was equipped with a displacement transducer type WA200 produced by company HBM that can measure displacements of up to 200 mm. Its technical characteristics are as follows [11]: - supply voltage: 15-30V; - nominal output voltage: 0.5-10V; - measured maximum displacement: 200 mm.

Figure 5. Program deployed in the pressure controller


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Experimental data acquisition was made with a data acquisition system WEBDAQ/100, with a maximum frequency of acquisition of 500KHz. This data acquisition system has 32 input channels and 8 channels for analog output. Data acquisition frequency for the tests of FE_0 specimens was 100 Hz. A first test was conducted in advance for calibration work, thus the air cylinder was powered by compressed air up to the maximum pressure (limited to 6 bar). Following this calibration has been computed a correlation coefficient (c≈0.82) for the transducer measured force with the force calculated according to the pressure in the air cylinder. Experimental study and subsequent processing of experimental data obtained have been carried out in accordance with ISO 14125 [9] concerning fiber reinforced plastics composites materials and determination of flexural properties. Two samples (FE_0) have been tested at the three-point bending up to breaking. The tests are validated according to the standard after the break mode of fibers, which was tensile fracture of fibers (figure 6).

Figure 6. Samples broken after three points bending tests All flexural stress values have been calculated using the equation (3): (3)

- F is the measured force, in newton; - L is the distance between the supports (the span), in millimeters; - b the width of the specimen, in millimeters; - h is the thickness of the specimen, in millimeters.

The nominal flexural strain is calculated using the equation (4) on the basis of the values recorded by the displacement transducer:

(4)

- s is the beam deflection in the mid-point of the beam, measured by displacement transducer, in millimeters.

The flexural modulus (Young modulus - E), is calculated with equation (5) between the two fixed strain values: [MPa] (5)

- flexural stress, in MPa, measured at strain value = 0,05%;

- flexural stress, in MPa, measured at strain value = 0,25%. In the case of a deflection, s, greater than 0.1L, the standard imposes a new equation for determination of flexural stress and strain, as follows:

(6)


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(7) In table 3, are presented the results obtained, in average value, as a result of experimental data

processing. The typical curves of evolution of flexural stress-strain ( and ) for samples FE_0 are plotted in figure 7.

Table 3. Mechanical properties of composite material flax/epoxy

E_max [GPa]

σ_max [MPa]

ε_max [%]

σf_max [MPa]

εf_max [%]

14,7 159,5 3,4 184,3 3,42

a) b)

Figure 7. Flexural stress-strain curves for composite material FE_0

FE_0 presents a linear behavior until , followed by a curvilinear trajectory. Breaking of the samples takes place gradually by breaking of the fibers due to stretching in the bottom of the sample. This is visible in the plotted curves of the material; the flexural stress slightly decreases on the last part of the curve, up to the final breaking of the sample. 4. CONCLUSION Composite materials reinforced with natural fibers and/or biopolymers have developed significantly in recent years due to their advantages of biodegradability, low cost, relative low density, high specific strength and renewable origin. Mechanical characterization of three-point bending of fiber-reinforced composite material and epoxy resin was made and the results were presented. These tests were conducted on a testing machine designed and executed within the Research Centre "Automotive Engineering" of the Faculty of Mechanics and Technology, Pitesti. Observations were made on the composite material flax/epoxy with 6 layers of with fiber orientation at 0°. Mechanical behavior for three points bending tests has been obtained, as well as mechanical properties have been identified.


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More tests should be performed for a complete mechanical characterization of composite material flax/epoxy and in order to identify variations in mechanical properties. A clear advantage of composites reinforced with flax fibers, from composites reinforced with glass fibers, is that they can be burned (euphemistically called "thermal recycling"), without leaving large amounts of slag. ACKNOWLEDGEMENT The research activity of A.I. HĂLOIU was supported through project POSDRU/159/1.5/S/138963 – PERFORM. REFERENCES [1] ALTROPOL, Datasheet. [2] ASTM D3171, Standard Test Methods for Constituent Content of Composite Materials, 1999. [3] BCOMP, Datasheet. [4] Corbière-Nicollier T, Gfeller Laban B, Lundquist L, Leterrier Y, Manson JAE and Jolliet O. Life cycle assessment of biofibres replacing glass fibres as reinforcement in plastics. Resources, Conservation and Recycling, Vol. 33, pp. 267-287, 2001.

[5] Carbajal N. and Mujika F., Determination of transverse compressive strength of long fibre composites by three-point bending of [90m/0n] cross-ply laminated strips, Polymer Testing, Volume 30, Issue 5, August 2011, Pages 578-584, ISSN 0142-9418.

[6] Directive 99/31/EC Landfill of Waste. [7] Directive 2000/53/EC of the European Parliament and of the Council, 18 September 2000. [8] ELV Directive: Review of the 2015 -Targets, Brussels, 28 January 2005. [9] ISO 14125, Fibre reinforced plastic composites – Determination of flexural properties, 1998. [10] HBM, Force Transducers - Datasheet. [11] HBM, Inductive Standard Displacement transducers – Datasheet. [12] RENARD J. Fatigue des matériaux composites renforcés de fibres continues. AM 5410, pp. 1-13. [13] Schneider-Electric, SR3B101BD Datasheet. [14] SMC, Air Cilinder - Datasheet. [15] Ziad K. Awad, Thiru Aravinthan and Allan Manalo, Geometry effect on the behaviour of single and glue-laminated glass fibre reinforced polymer composite sandwich beams loaded in four-point bending, Materials & Design, Volume 39, August 2012, Pages 93-103, ISSN 0261-3069.

[16] Ziad K. Awad, Thiru Aravinthan, Yan Zhuge and Allan Manalo, Geometry and restraint effects on the bending behaviour of the glass fibre reinforced polymer sandwich slabs under point load, Materials & Design, Volume 45, March 2013, Pages 125-134, ISSN 0261-3069.

RoJAE vol. 21 no. 2 / June 2015 ISSN ____ – ____ (Online, English) Romanian Journal of Automotive Engineering ISSN 1842 – 4074 (Print, Online, Romanian)

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MANUFACTURING PROCESSES FOR COMPOSITE MATERIALS STEERING COLUMN PARTS

Lucian RAD*, Anghel CHIRU

Transilvania University of Brasov, Str. Politehnicii Nr. 1, 500024 Brasov, Romania

(Received 16 February 2015; Revised 27 March 2015; Accepted 23 April 2015)

Abstract: The paper makes an analysis of automotive manufacturing processes in composite materials. The novelty is the new process of winding space which can obtain auto parts with complex geometry (console steering column) using composite materials. The challenge is to achieve a production time under 3 minutes/part, with high quality and meeting the specifications demands on steering columns.

Key-Words: composite materials, carbon fiber, space-winding technology

1. INTRODUCTION Due to rising prices for energy and raw materials in the financial markets and international requirements on CO2 emissions of passenger cars worldwide, we need to find solutions to reduce the fuel consumption of vehicles through weight reduction and development of lightweight construction. In this paper will be analyzed, based on requirements which consist of design concepts and new manufacturing processes, vehicle steering column (steering column console) (figure 1). The steering systems of motor vehicles consist of the following components: steering column, steering shaft, steering box. Figure 1. Steering system components Figure 2. Constructive versions steering column



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There are several types of steering columns (figure 2): A. Lower pivot tilt and telescope column type A; B. Lower pivot tilt and telescope column type B; C. Lower pivot tilt and telescope steering column type C -steering wheel position can be adjusted

towards / away from the driver & up / down. Light weight is achieved by the use of aluminium components. This also allows for ignition lock integration, improving vehicle security.

D. Manual steering column-telescope only steering column using pressed steel components for high stiffness.

E. Upper pivot tilt steering column- steering wheel position can be adjusted up and down. The tilt away steering column feature raises the steering wheel for ease when exiting the vehicle.

F. Electric tilt and telescopic steering column-electrical adjustment mechanism with position memory.

G. Upper pivot tilt and telescopic steering column-steering wheel position can be adjusted back and forth, and up and down. The tilt away steering column feature raises the steering wheel for ease when exiting the vehicle.

H. Lower pivot tilt and telescopic steering column-steering wheel position can be adjusted towards / away from the drive and up / down. Lightweight achieved by using magnesium materials. The integrated design of the steering column and lock is an added deterrent to vehicle theft.

I. Lower pivot tilt steering column-steering wheel position can be adjusted up and down. Steering column components are shown in figure 3.

Figure 3. Steering column components

We chose as a component for production of a steering column bracket VW Golf 6. Requirements that must be fulfilled steering columns were extracted from the specification of the VW Golf 6:

• Natural frequency : - Vertical > 50 Hz; - Horizontal > 50 Hz;

• Stiffness: - Vertical ≤ 1.6 mm; - Horizontal ≤ 1 mm;

• Weight saving;

• Force control in climatic conditions of -30 to + 80 °C;

• Locking way and blocking force of action lever, including acoustic and a haptic behavior;

• Maximum permissible torsional moment;

• Torsional stiffness.

High strength steels and aluminium are used for light construction materials in the automotive industry.


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Unconventional materials (composite materials based on carbon fiber) have a high potential for use in

industry, due to low weight and high rigidity, successfully replacing traditional materials.

Manufacture technologies of composite materials are varied and reflect how increasing levels of

automation affect freedom in design (figure 4).

Figure 4. Production technologies Figure 5. Costs of different production technologies

Depending on the number of components produced annually and estimated production costs (in Euro), were examined autoclave process for small series, RTM process for medium series and the new process of winding space for large series (figure 5). With increasing levels of automation and the number of parts produced annually, the cost of production per piece decreases [1]. 2. STRUCTURE CALCULATION FOR COMPOSITE MATERIALS To determine the characteristics of the composite material, we take into account the following assumptions:

- at the interface between matrix and fiber sliding processes do not occur; - composite material is loaded in the elastic range; - it is recognized as mixing rule can be applied.

Micromechanical models are based on strength of materials arguments applied to a representative element of composite named unit cell (figure 6, figure 7) [4]. Figure 6. Section from layer of composite Figure 7. Composite deformation on fiber direction 2.1 E-modulus for tensile parallel fiber

mmffX VEVEE ∗+∗= (1)


49

X

f

fA

AV =

X

mm

A

AV = (2)

where mfX EEE ,, - fiber modulus in the x direction, modulus of the composite in the direction of the

fibers, and modulus of the matrix;

Xmf AAA ,, - cross sections of the fiber, matrix and composite material studied area;

mf VV , - are volume percentage of fibers, namely the matrix.

2.2 E-modulus for tensile perpendicular on fiber

)**(

*

mfftm

ftm

YEVEV

EEE

+= (3)

where ftE - modulus of the fibers in direction perpendicular to the direction of the fibers.

2.3 Shear modulus of the composite material

)**(

*

mfftm

ftm

XYGVGV

GGG

+= (4)

where ftm GG , - transversal elastic modulus of the matrix and transversal elastic modulus of the fiber.

2.4 Modulus of the composite material after a certain direction

)]2

1(**2)[(

1

2244

XXYYx EGsc

E

s

E

cE

νβ

−++

= (5)

θθ sin,cos == sc

where θ - angle between the fiber and direction load and ν -Poisson coefficient.

2.5 Tensile break strength after a certain direction

)]1

(**2)[(

1

22

2244

XRYRXR

R

scsc

σ

ν

τσσ

σ β

−++

= (6)

where xRσ; yRσ

; τ - breaking stress of the composite material in the fiber direction; breaking stress of the composite material in the direction perpendicular to the fiber and breaking tensile shear in fiber plan. 2.6 Poisson ratio

mmff VV ** ννν += (7)

where mf νν , are Poisson coefficient for fibers and matrix.


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3. MANUFACTURING TECHNOLOGIES FOR STEERING COLUMN BRACKET For restore the design of structural components of the steering column is important to make considerations of both integral and differential methods of design, in terms of the economy and the manufacturing process. The methodology for the development of a product is described in figure 8.

Figure 8. Product development methodology Following this methodology, we developed a new design for the steering column VW Golf 6, using Catia and Solidworks and using different manufacturing processes: autoclave, RTM and space winding.

Figure 9. Manufacturing processes: Autoclave, RTM, Space Winding 3.1 Autoclave The process of making the console box and steering column corresponds to the current state of the art. For this reason, the exposure on the implementation of this process will not be detailed. Production structural components of the steering column with autoclave process allows a variety of degrees of freedom and it is ideal for small series production up to 1'000 pcs. /year and the production costs and the components are comparable to those prototypes of steel or aluminum in the same number of parts. In terms of requirements specification, maximum reduction of weight of the two components obtained at best was 742 g (comparing with steel components). FEM simulations have shown that this composite console meets the requirements of the specification, but actual measurements and tests for stiffness and natural frequency shows that the console does not meet the specification (Table 1). Summarizing it can extract the following: - estimates of material characteristics are very suitable for simple calculations and assessments of fiber composite components with the aim of substituting: - the absolute necessity of using MEF - fabrics made an appearance superior optical and surface structure typical for CFK, because of their structure.


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Figure 10. Steering column console and case made by autoclave process 3.2 RTM (Resin Transfer Molding) Steering column with column and console casing made by RTM process is shown in figure 11. In all investigations conducted had very positive results have been achieved since the main requirements of the specification. FEM calculations proved to be an aid of great importance for fast and accurate sizing constructive. The method has a high automation potential and it can be realized production tact <3 min. / Piece. Economics saddle there for small series of up to 10'000 pieces / year. Unable to demonstrate the ability to produce 50.000 pieces/year (medium series).

Figure 11. Comparison between steel, carbon for natural frequency and stiffness-Autoclave process

Figure 12. FEM simulation for natural frequency in case of carbon console obtain by autoclave


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Figure 13. Steering column console and case made by RTM process

For FEM computations were selected following marginal conditions: - mass replacement steering wheel 3.58 kg (Series); - inner tubing steel panels (Series) - features low CFK- plaiting materials depending on the angle of braiding with 10-30% - hypothesis: the content of fiber volume φ = 50% instead of 60% to increase safety objective.

Figure 14. Comparison between steel, carbon for natural frequency and stiffness-RTM process Both processes RTM were conducted stable after a few initial adjustments. The console is too complex and laborious. Made in the lab surfaces are good. The 0 ° fibers show strong waves due to tension and additional glass fibers. This effect reduces the rigidity of the steering column box. Improved stiffness can be achieved by stretching the fibers as far as possible.

Figure 15. FEM simulation for natural frequency in case of carbon console obtain by RTM


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3.3 Winding space technology Newly developed method allows through the use of robots, making additional degrees of freedom to the above, even in combination with metal inserts. This process for produce structural components of the steering column with its special requirements on flexural and torsional rigidity and functional integration, limiting the movement of longitudinal reinforcement to absorb collision energy was innovative developed. The advance is the complexity component of 0.5-1 m / min. With this clock during manufacturing to a length of 300 mm caste is 20-40 s. Both manufacturing processes pultrusion-winding and winding space suitable for mass production, with> 50'000 pcs./year. In both processes are not generated scrap / waste during production or after post processing and have a low volume.

Figure 16. Steering column console made by Winding Space process A big advantage of this newly developed method is to be submitted by roving, fiber directions consistent with the flow of forces. Overall reduce weight is 639 g and 52% decrease from steel version.

Figure 17. FEM simulation for natural frequency in case of carbon console obtain by Winding Space Due insert metal sites, construction console as a hybrid structure is more complex, because in addition to fiber orientation changes should be taken into account the inserts. In this way results a new composite material. It was accepted the existence of overlapping but they have little influence on the deposit component due under tensile fibers. Figure 18. Comparison between steel, carbon for natural frequency and stiffness Winding space process


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4. CONCLUSION Components made by the autoclave processes and RTM can be considered as equal, the difference mass reduction is only 17g. The biggest difference in weight is obtained by autoclave process. Winding space process allows obtaining parts with comparable weights because of metal inserts.

Table 1. Comparison between manufacturing process- weight, stiffness, natural frequency

Requirements Series Steel Carbon(Autoclave) Carbon RTM Carbon Space winding

Weight [g] Console+Case 622+600 268+231 258+258 325+258

Stiffness

Vertical F=600N [ mm] ≤1,6 1 3,65 1,56 1,58

Horizontal F=250N [ mm] ≤1 0,46 2,68 1,2 0.98

Natural frequency -wheel position-

fully extended/down:

Vertical [ Hz] >50 Hz 65,8 39,3 52,6 59

Horizontal [ Hz] >50 Hz 54,2 26,1 48,2 54 Regarding the specification requirements on the stiffness and natural frequency, spatial winding process allows high quality components that are registered in requirements

Figure 19. Comparison between manufacturing processes regarding weight saving Is brought into question the existence of a concept for economic production in large series of structural components of the steering column. To achieve such production is necessary to meet the requirements of the customers and for large series (over 50'000 pcs. / Year) to obtain a maximum delta costs 20 Euro, additional costs, together with a reduction in weight 1 kg, without affecting functionality. In principle, the process of winding space, showed that it can achieve complex geometry parts in carbon fiber. Process must be optimized for integration into large production and also to be revised final geometry of the console. ACKNOWLEDGEMENT „This work was partially supported by the strategic grant POSDRU/159/1.5/S/137070 (2014) of the Ministry of National Education, Romania, co-financed by the European Social Fund – Investing in People, within the Sectoral Operational Programme Human Resources Development 2007-2013”


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REFERENCES [1] Heitz, T., The physical-mechanical properties of structural components made of fiber composite materials in the application of steering columns in cars, Doctoral Thesis, Brasov, Romania, 2013. [2] Heitz, T. and Chiru, A., Analysis of using CFK material for steering columns components in passenger vehicles - Part 2. Valencia, FISITA, 13 EAEC Automotive Congress, 2011. [3] Crasto, A.S. and Kim, R. Y., Compression strengths of advanced composites from a novel mini-sandwich beam, SAMPE Quarterly, 22(3) 29–39, 1990. [4] Taca, C. and Paunescu.,Composite materials, ISBN 9789737558440, MatrixRom, Bucuresti, 2012. [5] Hodgkinson, J. M., An Experimental Comparison of ASTM, BSI and CRAG Standard Test Methods for the Determination of Mechanical Properties of Composite Materials, The Centre for Composite Materials, Technical Report 90/02,Imperial College, London,1990. [6] Hodgkinson, J. M., Mechanical testing of advanced fibre reinforced composites, Published in North and South America by CRC Press LLC,Corporate Blvd, NW Boca Raton FL 33431, USA,1992. [7] Li, J., Journal of Composites Technology and Research, 174-183,1997.


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PROBABILITY DISTRIBUTIONS USED TO STUDY THE RELIABILITY OF VEHICLES USED IN ORNAMENTAL ROCK QUARRIES

Monia Camelia DASCAR SECARA*, Marin Silviu NAN

University of Petrosani, Str. Universității Nr. 20, 332006 Petroșani, România

(Received 18 March 2015; Revised 24 April 2015; Accepted 20 May 2015)

Abstract: The productivity of an open pit mine relies on a reliable transportation system. For a quarry, it is critical that haul trucks are maintained efficiently to have a high availability. Many authors have studied records and associated statistics in regards to failure data. Normal distribution has been used to describe the failures of the individual machine components of a complex system, but different variables and machine particularities, wear or other constrains, determine a real life data following a dynamic large distribution. The objective of this paper is to present two techniques of reliability estimation based on record statistics: two-parameter Weibull distribution theory with its parameters (shape β and scale α) and the Exponential Method with the survival time parameters. Finally, a real dataset of the failure data for haul trucks in operation at a marble quarry is analyzed to illustrate the fitting of data within Weibull and Exponential distributions, calculate the relevant parameters and obtain the fatigue life equation by regression under different failure probabilities. The distribution analysis in terms of reliability and durability shows a trend of increasing failure rate, opening the opportunity for setting a decision plan on reliability centered maintenance planning activities, possible improvements, limitations in respect to the optimal load/speed, and the need to improve the process of collecting maintenance data.

Key-Words: Reliability, Weibull distribution, Exponential distribution, Maintainability, Lifespan.

NOMENCLATURE MTBF, MTTR: mean time, h; λ: failure rate, defects/h; µ: rate of repairing operation, rep/h. 1. INTRODUCTION Reliability is the probability that parts, components, products and systems will perform the functions for which they were designed without damage under specified conditions, for a certain period of time and with a given confidence level. Although reliability is an independent notion, reliability and the concept of quality are closely related. The quality of a product represents all properties that make it suitable for the intended use; reliability is the ability to keep product quality throughout the operation. In other words, product quality reliability is extended in time [24]. Reliability engineering techniques provide theoretical and practical methods that analyze the likelihood and the ability of the parts, components, equipment, products and systems to perform the functions for which they were designed and built, during predetermined time, under specified and known levels confidence, can be specified in advance, designed, tested, proven even under conditions in which they were stored, packaged, transported and then installed, commissioned, monitored and information submitted by all involved and interested. The reliability of machinery is essential, particularly in quarries, since the breakdown of any machine would cause an unpredictable loss or damage [17]. Therefore, it is obvious that the reliability of such equipment would have considerable impact, not only on production, but also machine life and potentially on human life. Failure must be precisely defined in practice. For dealings between producers and consumers, it is essential that the definition of a failure be agreed upon in advance to minimize disputes.



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For many products failure is catastrophic and it is clear when failure occurs. For some products performance slowly degrades and there is no clear end of life. It can be defined that failure occurs when performance degrades below a specified value [18]. Prevention is better than cure. Instead of allowing the occurrence of failure and suffering from loss or damage of assets and environment, it is always worthwhile forestalling the occurrence. To operate in quarries with reduced number of failures, because of the harsh environment, the machines must be maintained to exhibit high reliability. The maintenance planning of equipment hence requires the orientation of reliability at every stage of its life. A great deal of research has been done on estimating the reliability through the Weibull distribution using classical methods, a very good summary of this work can be found in [12]. Many authors have studied other distribution methods to better analyze records and associated statistics on different fields [6][7][8][11][15]. The present study is on effort in this direction that can provide some guidelines while planning the maintenance activities with an orientation to reliability. The most difficult part of this process is the acquisition of trustworthy data. It is known that no amount of precision in the statistical treatment of the data will enable sound judgments to be made based on invalid data. 2. PROBLEM FORMULATION Reliability is characterized by four concepts: probability, performance achieved, operating conditions and duration. Operational reliability is determined in real operating conditions. In some cases non-economic laboratory experiments, the main source of data collection, are not feasible. Experience in the field is recommending the selection of a group of beneficiaries, by category of use, operating conditions, etc. and systematic tracking performance of products through group reliability. This information is collected through direct reports of the interventions to address the nonconformities. Information processing is done by one of the methods available. Operational reliability is divided in two parts: functional and technological. Functional reliability is known as the operational safety concern matters relating to the operation of the system in terms of primary kinematics [4]. Technological reliability concerns with keeping within the limits of working parameters values. E.g. for a hydro pneumatic cylinder-piston engine, functional reliability is achieved during movements for which the engine was developed and designed; technological reliability means keeping the speed of travel, breaking times, force to the working body. 2.1 Reliability indices The basic reliability indices, as parameters which express reliability from a quantitative point of view, are being expressed by: the good operating probability, reliability function, R(t); probability of deterioration, non-operation reliability function, F(t); probable density of deteriorations, f(t); intensity or rate of deterioration, z(t); mean time of good operation, MTBF; mean time for repairing operations, MTTR; the rate of repairing operations, µ. Limit failure rate is the ratio of the probability that a device be damaged within the given time estimated (t, t+dt) and the size of the sub-interval dt, provided that it is part of the devices that were in good condition early in the process. Any product lasts and during its use, it is subjected to a process of attrition, a process that usually includes three periods (figure 1), where upon it, someone must intervene effectively to restore performance to prolonged use, namely: initial period, when the number of faults that occur when running are relatively high, but decreasing; normal period (useful) life, when defects are reduced in number and random; the final period, when the number of failures due to wear or aging phenomena is growing. Looking from probabilistic perspective at the reliability problem [9], it can be said that time when a malfunction occurs cannot be establish with certainty, but only as a probability linked to a confidence interval. The concept of reliability has the statistical character in addition to the probabilistic. This is explained by the fact that the determination of reliability is based on data obtained by measurements (laboratory), or through operational monitoring of the product, when obtain data on defects found on samples. Reliability block diagram (RBD). A device or system is described as a collection of parts or components. The system operates successfully if all its components operate successfully (do not fail), but it may also operate if a subset of components has failed. RBD is a diagrammatic method for showing how component reliability contributes to the success or failure of a complex system. RBD is also known as a dependence diagram (DD).


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2.2 Reliability block diagram A system is described as a collection of various components. The system operates successfully when all components operate successfully, but can also operate when a subset of the components are operational. RBD (Reliability Block Diagram) is a detection method that shows how component reliability is contributing to the success or failure of a complex system. RBD is known as DD (dependency diagram). 2.3 Graphic Systems – Pareto Analysis Matrix of defects shows the number of failures recorded on each component of the system at equal time intervals. The failures shall sum horizontally for each component during the experiment. The histogram is built as a matrix showing the number of defects registered to a time "t" of each of the components of a system. Pareto chart allows highlighting the component with the lowest reliability in a system. Complex Pareto charts rises in successive steps to highlight simple elements with the highest rate of falls. The goal of Pareto analysis is to find the subsystems that affect overall system failure, characterizing the frequency of subsystems failures and ranking system for each subsystem failure. Pareto Chart is a priority failure analysis showing overall subsystem. Then fault numbers are added together vertically, to the intervals [1]. At the bottom of the matrix it builds a histogram showing the evolution of the number of failure time intervals ∆t of the entire system.

Figure 1. The evolution of failures on the entire life of a product 2.4 Weibull Distribution Sometimes there are physical arguments based on the probabilistic failure mode which tends to justify the choice of model. The models are used only because of its empirical success in real data failure sheet. The first step is to find the best fitting distribution function for the raw input data set [21]. We choose the calculation of reliability of transportation system by Weibull model which is an acceptable failure model where the item breaks down when the weakest part fails [10]. Weibull model is a very flexible method for modeling data sets containing values greater than zero, such as failure data. Weibull analysis can make predictions about the life of a product, compare the reliability of competing products, can establish policies to guarantee statistical or proactively manage stocks of spare parts [14]. Weibull analysis is primarily a graphical technique although it can be done analytically. One graphical technique is Weibull Probability Plotting [19]; other graphical methods: Maximum Likelihood Estimation or Hazard Plotting. Weibull distribution is characterized by three parameters [16][20]: - α, shape parameter; shows the stretching on the time axis of the Weibull distribution law. - β, scale parameter or characteristic life; changes the shape of variations of reliability curves. - γ, location parameter or minimum life.


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The Weibull distribution density function is given by the probability PDF [6][8][13]:

( )

β

αγ

β

αγ

αβ

γαβ

−−−

−=

t

et

tf1

,,, (1)

where 0,0,0,0 ≥≥>> γαβ t .

The cumulative Weibull distribution function is given by the cumulative distribution CDF [2][11][19]:

( )

β

αγ

−−

−=

t

etF 1 (2)

Reliability:

( )

β

α

−

=

t

etR (3)

Hazard function:

( )1−

=

β

αβ t

tth (4)

From equation (2), cumulative Weibull distribution function F(t) can be rearranged in a form to which we apply the linear regression:

( )t

tFlnln

1

1lnln βαβ +−=

− (5)

y(t)=-shape ln(scale)+shape ln(t) (6) Equation (6) is an equation of a straightline: y = intercept + slope t; y(t) is a linear function of ln(t) having slope=β and intercept = –βlnα, the basis for the linearization of the Weibull CDF (figure 2). It has been shown [3][12] that shape factor drops directly out of the regression equation, whilst the scale factor has to be derived from the intercept:

scale = exp (7)

Figure 2. Linearization of the Weibull CDF


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To plot F(t) versus t, we follow three steps: a) rank estimates in an ascending order; in our calculation, having a sample size less than 100, will consider the Median Rank method (Bernard’s approximation) [26], equation:

(n-0.3)/(n+0.4) (8)

where: n= Rank, is the rank number of the given nonconformity; b) estimate F(tn) of the nth failure; c) plot F(tn) versus t.

2.5 Mean Time Before Failure (MTBF) After a system is repaired, it does not have the same performance characteristics as a new one, because not always the repair of defective components is perfect, the system has suffered overheating components, or broken parts were not well repaired. The best estimate of the total MTBF for Weibull distribution [19][25] is given by:

γβ

α +

+Γ⋅=

11MTBF (9)

MTBF parameter value estimated using this statistical method often cannot be calculated because of incomplete field data. In most cases, this time decreases randomly with age, which demonstrates that there is a series of random factors that make the average cycle time to decrease. If all system faults can be rectified, implying a long service life of the system, the estimated average cycle time becomes constant, obviously taking into account the age of the system. This is known as steady state condition. Uptime and disruption may change depending on system’s age:

N

TMTBF = (10)

where: T is total working time of the system; N is total number of faults. MTBF parameter value estimated using this methodology must be corrected in order to reach a value as close to reality as possible, requiring a certain level of confidence. Correction factors can be achieved using the confidence interval method. 2.6 Exponential Method 1-parameter form of the exponential distribution is commonly used for components or systems exhibiting a constant failure rate. A model for the distribution of its lifespan can be any probability density function (PDF), f(t), defined in time interval from t = 0 to t = infinity. Cumulative distribution function CDF, F(t), is a useful model as it gives the probability that a randomly selected unit will fail during time t, [18][19]. Exponential model is widely used for two reasons: - most systems spend most of their useful life in constant repair portion of the “bathtub curve” graph; - it is easy to plan tests, estimate MTBF and calculate the confidence intervals. The key equations for the exponential function are shown below:

( ) tetf λλλ −=, (11)

( ) tetF λ−−= 1 (12)

( ) tetR λ−= (13)

( ) λ=th (14)


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The cumulative hazard function for the exponential is the integral of the failure rate or H(t): H(t) = λ ⋅ t (15) Exponential Mean Time Between Failures MTBF [9][23]: MTBF=T/N (16) The failure rate, λ: λ = 1/MTBF (17) MTTR (System Mean Time to Repair), where T=total downtime of the system, N= total number of failure: MTTR=T/N (18) The rate of repairing operation, µ: µ=1/MTTR (19) MTTF (Mean Time To Failure) relation with MTTR for repairable systems: MTBF = MTTF + MTTR (20) Confidence level selected is calculated with a simple equation: 100 ⋅ (1 - α) (21) Table 1 represents MTBFmin and MTBFmax values for most used confidence levels [19].

Table 1. MTBF max and min for selected confidence levels

Confidence level

60% 80% 90% 95% # of defects MTBF min

MTBF max

MTBF min

MTBF max

MTBF min

MTBF max

MTBF min

MTBF max

25 0.829 12063 0.764 13267 0.716 14383 0.677 15452

30 0.844 11848 0.783 12915 0.737 13893 0.701 14822

3. WORK METHODOLOGY In this section, we provide a failure data set in the form of Time between Failures (TBFs) and Time To Repair (TTRs), which is assumed to be distributed with Weibull law [12]. The data sets were recorded in a time period of 1 year for a number of 8 haul trucks in use at an open pit, marble quarry [7]. When reliability block diagram (RBD) for a vehicle is designed, blocks are arranged in series configuration with each critical subsystem [4]. For the reliability model, it is important that data is collected with consistency using a written field data collection process [27]. 3.1 Pareto Analysis The frequency of failures of each component or subsystem can be determined using the Pareto principle, or 80-20 rule [3], which states that for many events, 80 % of the effect was caused by 20% of the cause. Pareto Analysis shows the number of failures recorded for each component of the system at equal time intervals. The number of failures is summed horizontally for each component during the experiment.


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On the right side of the matrix the corresponding histogram is built, which is the Pareto chart of the system. Then, fault numbers are added together vertically for each time interval. At the bottom of the matrix the histogram is showing the evolution of failures in time intervals ∆t, for the entire system.

Figure 3. Pareto chart on the absolute incidence of faults Trend analysis (figure 3) of the system does not show any trend, the method proves that the system deteriorates [27]. System reliability is an indicator of the condition of the equipment’s overall performance; reliability analysis was done using each subsystem failure. Pareto chart is then analyzed to select the most important components affecting the system. 3.2 Application methods for calculating reliability – Weibull analysis Calculating only the MTBF to represent the system reliability could lead to misleading and unnecessary spares expenses, or not enough spares to continue work effectively. Failures are not normally distributed; MTBF does not provide information about the changing nature of failure rates over time. To provide reasonable accurate failure analysis and failure forecasts with a limited number of samples, we have chosen Weibull method because it provides a performance analysis using a simple and useful graphical plot of the failure data. 3.2.1 Preparing to analyze Weibull analysis requires some preparatory calculations: MedianRank column is an estimate of the proportion of the population that fails until the time listed in column TBF (Time Before Failure). To generate the graph of the corresponding regression, Weibull Analysis needs to generate median ranks as median values on the Y axis values, alpha ranks obtained with the method of calculating Median Ranks, formula (22), where n=1,2, ... 26; N=26 (total number of failures). The advantage of this method is that data corresponding to ln(ln(1/(1-MedianRank))) is graphical awarded in a straight line. By performing a simple linear regression we obtained estimated parameters which allow inferences on TBF values. To do this, in the next step we used Excel add-in Analysis ToolPak to calculate the parameters required to estimate Weibull parameters. 3.2.2 Estimation of Weibull parameters Weibull cumulative distribution function can be transformed so that it appears as a straight line. Using Excel Data Analysis [5], with ToolPack Analysis kit, we generated a new set of data representing Predicted=ln(ln(1/(1-n))) versus Residuals:

- β= CoefTBF = 1.42 - α= EXP(-CoefIntercept/CoefTBF)=13,126

3.2.3 Fitting a line to the data With data calculated, the next step was to generate the graphical representation for the two entries which determine the reliability curve: Predicted -> ln(ln(1/(1-n))), Residuals. Data plotted on X-axis, ln(TBF), and Y-axis, ln(ln(1/(1-n))), has been further adjusted to create the linear distribution (figure 4): Linear - > ln(ln(1/(1-n))).


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Survival probability and reliability were determined by selecting 20 TBF intervals of 1,000 hours (X) together with Microsoft Office Excel formula: WEIBULL(X,α,β,TRUE) (22) 3.2.4 TBF for a certain level Sometimes we need time before failure for a certain reliability level, given through the requirements (Table 2). TBF calculations are performed using formula (16):

Table 2.

TBF for a given reliability

Reliability TBF

0.01 38,432 0.10 23,601 0.50 10,143 0.90 2,696 0.99 516

Figure 4. Predicted line 3.2.5 Generathe survival chart The reliability chart calculated is shown on figure 5, Y-axis-Survival Probability, X-axis-Time Before Failure:

Figure 5. Survival chart β=1.42 3.3 Application methods for calculating the reliability - Exponential Model 3.3.1 Mean Time Before Failure for one vehicle Using relation (38): MTBF = 88.28 [h] . For a confidence level of 90%, the most widely used which corresponds to a coefficient α=0.1, from table 1, the min and max values of MTBF corresponding to 26 defects: with correction coef. 0.830992, MTBFmin = 63.58 [h] ; with correction coef. 1.210616, MTBFmax =126.1 [h]


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3.3.2 Failure rate, λ For the same confidence level of 90% and the min/max values of the MTBF, equation (17): λmax=0.0157 [defects/h]; λmin=0.0079 [defects/h] and the mean failure rate: λmed=0.0118 [defects/h] Our calculations show that, with a probability of 90%, the estimated mean time is found inside operating ranges 63-126 hours, and the failure rate inside interval 0.01572...0.00793 defects/h. 3.3.3 Probability density function of faults occurrence For the exponential distribution law, on the basis of relation (29) we determined the range of values that expresses the variation in density of the probability of failure occurrence with respect to time, (figure 6),

f1(t) and f2(t) - values calculated for the two margin values of the failure rate ( ) tetf λλλ −=, , λmax and λmin.

3.3.4 Cumulative distribution

The cumulative distribution function ( ) tetF λ−−= 1 , λmax λmin is presented in figure 7.

3.3.5. Reliability function

Survivor or reliability function ( ) tetR λ−= , λmax λmin is presented in figure 8.

Figura 6. Probability density function Figura 7. Cumulative distribution function

Figura 8. Reliability curve


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From this chart we concluded that the value of reliability of the fleet is large, e.g. for a running time of 50 hours, the probability of not having defects (reliability) is between 46-68%, this time being a time of actual work, not a calendaristic time. We disregarded the times spent to fix the defects of other equipments in the system, the times related to the technological disruptions, organizational deficiencies. Also, we have not considered current repairs and maintenance related times. 3.3.6 Average repair time (MTTR) and adjust the processing rate (µ) The total downtime for all trucks, equivalent with the time required to put them back into service as a result of the 26 faults that have occurred in the period under review, is 355 hours. Using equations (19) and (20): MTTR = 13.65 [h] ; µ = 0.073 [rep/h] The calculated values of the mean time to repair the subassemblies, namely the mean intensity or repair rate, relatively high, are explained by the difficulty of corrective maintenance work, given the large masses and gauges to be handled. 4. CONCLUSION This study is restrained to a relative small number of equipments investigated (8 haul trucks). The accuracy of the data collected is depending on the people concerned with maintenance activities, the collection in a systematic and organized way of failure/repair reports (understanding that this could be time consuming and requires proper processes in place). The equipment performance depends on its age and other factors. It is critical to record failure/repair data in such manner that can be used by the management team for spare parts provision, maintenance planning, ordering new equipment, or taking corrective actions about factors that have an influence on the equipment reliability (load, speed, roads, etc). Performance of a quarry not only depends upon production equipment like drills/cutters/excavators/ loaders but very much affected by the availability and utilization of service equipment. An integrated study of availability of all the equipment in a quarry can definitely improve the productivity through enhanced utilization of production equipment, based on their availability. Weibull shape parameter β indicates if the failure rate is increasing, constant or decreasing [10][13]. In our study we found β > 1.0 indicating an increase in the rate of failures. This is typical to products presenting the phenomenon of wear. In this study Weibull model shows that for a confidence level of 99 %, TBF has a value of at least 2,696 hours. To increase the reliability it is absolutely necessary to address, using also the analysis performed with Pareto charts, the major nonconformities on each subsystem: brakes, transmission, suspension, engine, gearbox, running system. Along with that, it is necessary to review the data collection process. Repairs of major systems may take several days and often requires removing other components to carry out the work. Effective identification, planning, scheduling and execution can significantly reduce the impact of these failures. Eliminating failures through a valid predictive maintenance would have the greatest positive impact. Another main cause of failure is a combination of truck speed, payload and road conditions. If any of these three cases is eliminated, the problem is minimized. A review of load conditions and truck speed are needed, also an evaluation of the road conditions which are a major cause of equipment downtime because of damages to the brakes and suspension. The cycle of freeze / thaw that perhaps last several months, determine a significant wear of roads, the holes appeared having the potential to cause significant damage to major mechanical components. Combining the data monitored by the pressure dampers, payload and GPS coordinates, it is possible to successfully locate inadequate road sections. This would allow an intelligent operation of road maintenance teams with a priority list of road sections requiring repair operations. While mining equipment wears, availability tends to decrease. The biggest challenge for a truck to operate within 90% availability is the sustainability of a robust maintenance program. Quality maintenance team is represented by the high availability of equipment which may be achieved through the development of consistent processes for maintaining equipment to world-class standards. An integrated part of the maintenance program is to remove old components, worn or that have reached the end of their useful life, and replace them with components that meet the standards of durability and reliability. A key element of success is monitoring program describing the collection of routines that facilitate early detection of changes in the functionality of the equipment and systems. These processes support a method of repair before the failure of equipment. In its simplest form, condition monitoring involves studying the state machinery, systems and components, as well as external factors.


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REFERENCES [1] Andreescu, C., Fiabilitatea și mentenanta autovehiculelor, Universitatea Politehnica Bucuresti. [2] Bajenescu, T.I., Fiabilitatea sistemelor tehnice, ISBN: 973-685-624-0, 2011. [3] Brighton Webs, Statistics for energy and the environment, Weibull Distribution (Two Parameter), http://www.brighton-webs.co.uk/, 2011. [4] Burlacu, G., Fiabilitate, mentenabilitate și disponibilitatea sistemelor tehnice, Matrix, București, 2011. [5] Dorner, W. W., Quality Digest, Using Microsoft Excel for Weibull Analysis, http://www.qualitydigest.com, 1999. [6] Hall, R., Analysis of Mobile Equipment Maintenance Data in an Underground Mine, Quenn's University, Ontario, 1997. [7] Hoseinie, S.H., Ataei, M., Khalokakaie R. and Kumar, U., Reliability and maintainability analysis of electrical system of drum shearers, Journal of Coal Science & Engineering, Vol.17, No.2, 2011. [8] Jula, D. and Dumitrescu, I., Fiabilitatea Sistemelor de Transport, Petrosani, Focus, 2009. [9] Jula, D., Praporgescu, G., Mihăilescu S., Cornaciu, N. and Deaconu, I., Considerations on the Reliability of Machines and Equipment used in the Open Pit Mines, Annals of the University of Petroşani, Mechanical Engineering, 2006. [10] Khan M. S., Pasha G.R. and Pasha A.H., Theoretical Analysis of Inverse Weibull Distribution, WSEAS Transactions on Mathematics, Issue 2, Volume 7, 2008. [11] Mann N. R., Schafer R. E. and Singpurwalla, N. D., Methods for statistical analysis of reliability and life data, John Wiley and Sons, New York, 1974. [12] McCool, J.I., Using the Weibull Distribution: Reliability, Modeling and Inference, John Willey & Sons, 2012. [13] Mohammad, A. Al-Fawzan, Methods for Estimating the Parameters of the Weibull Distribution, King Abdulaziz City for Science and Technology, 2000. [14] Mouli, C., Chamarthi, S., Chandra, R.G. and Kumar, A.V., Reliability Modeling and Performance Analysis of Dumper Systems in Mining by KME Method, International Journal of Current Engineering and Technology, 2014. [15] Munteanu, T., Gurguiatu, G. and Balanuta, C., Fiabilitate și Calitate în Energie Electrica, Galati University Press, 2009. [16] Murthy, D. N. P., Xie, M. and Jiang, R., Weibull Models, John Wiley, New York, 2004. [17] Nan, M.S., Capacitatea sistemelor de transport, Editura Universitas Petroşani, 2000. [18] Nelson, W., Applied Life Data Analysis, John Wiley, New York, 1982. [19] NIST, U.S. Commerce Department, NIST/ SEMATECH e-Handbook of Statistical Methods, Assessing Product Reliability, 2013. [20] Palakorn, S., Thidaporn, S. and Winai, B., The Length-Biased Exponentiated Inverted Weibull Distribution, International Journal of Pure and Applied Mathematics, Volume 92, No. 2, 2014. [21] Peng, S. and Vayenas, N., Maintainability Analysis of Underground Mining Equipment, Using Genetic Algorithms: Case Studies with an LHD Vehicle, Hindawi Publishing Corporation, Journal of Mining, http://dx.doi.org/, 2014. [22] Preda, V., Panaitescu, E., Constantinescu, A. and Udradjat, S., Estimations and predictions using record statistics from the modified Weibull model, WSEAS Transactions on Mathematics, Vol. 9, Issue 6, 2010. [23] System Reliability Analysis Center, The Journal of the Reliability Analysis Center, Second Quarter, https://src.alionscience.com, 2005. [24] Tudor, A., Durabilitatea şi fiabilitatea transmisiilor mecanice, Editura Tehnica, Bucureşti, 1988. [25] Uzgoren, N., Elevli, S., Elevli, B. and Uysa, l. O., Reliability Analysis of Draglines' Mechanical Failures, Eksploatacja I Niezawodnosc, NR 4/2010. [26] Warwick Manufacturing Group, Weibull analysis, Product Excellence using 6 Sigma (PEUSS), Section 8, 2007. [27] Widijanto E., Operational Reliability of an Open Pit Mine, Case study in Chuquicamata Mine, Santiago de Chile, 2010.


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A REGARD OVER THE IMPLICATIONS OF THE NEW TEST CYCLES REGARDING THE

ENGINE CALIBRATION

Maria Alina TUȚĂ *, Florian IVAN and George Marian TRICĂ

University of Pitesti, Str. Târgu din Vale Nr. 1, 110040 Pitesti, Romania

(Received 19 February 2015; Revised 26 March 2015; Accepted 15 April 2015)

Abstract: It is known that from January 1, 2020, the maximum limit of CO2 emissions will decrease steadily to the value of 95 g / km for all new vehicles produced and sold in the European Union (EU). Related to this, the EU has begun debate on draft legislation that would take effect no later than 2017 regarding the test methodology for determining emission of CO2. For this purpose new cycle, generic symbolized WLTP (Worldwide Harmonized Light Vehicles Test-Procedures) appeared, which is a new test cycle for determining pollutant emissions, aiming to reduce the gap between the theoretical values given by the manufacturers and the ones obtained by users (differences estimated at 1-2 Liters/100 km). So it is desired to reach a compromise which better reflect actual consumption and exhaust emissions of light vehicles. WLTP rules are quite complex compared to the current measurement cycle NEDC (New European Driving Cycle). This involves some background changes in diesel engine calibration activities. Perhaps the next step in legislation will not be the reduction of pollution limits for NOx concentrations, imposed by the Euro 6, but increasing the difficulty of testing cycles. That's why the new calibration strategies will consider monitoring the type of depollution cycles. Related to this, the attention will be focused on developing more refined methods for engine calibration aimed mainly to minimize the NOx / PM concentrations.

Key-Words: CO2, missions, pollution, WLTP, NEDC, calibration.

1. A LOOK UPON THE EUROPEAN RULES AND REGULATIONS Knowing the harmful effects of emissions produced by internal combustion engines has imposed theirs gradually limitation. The evolution of European standards regarding the emission concentration is shown in Table 1.

Table 1.

EURO standards evolution [1] Pollutants

Diesel standards

CO [g/km]

HC [g/km]

HC+NOx [g/km]

NOx [g/km]

Particles [g/km]

Euro 1 (1993) 2,72 - 0,97 - 0,14 Euro 2 (1996) 1 - 0,9 0,7 0,1 Euro 3 (2000/2001) 0,64 0,2 0,56 0,5 0,05 Euro 4 (2005/2006) 0,5 0,1 0,3 0,25 0,025 Euro 5a (2009/2011) 0,5 0,1 0,23 0,18 0,005 Euro 5b (2011/2014) 0,5 0,1 0,23 0,18 0,0045 Euro 6b (2014/2016) 0,5 0,1 0,17 0,08 0,0045

2. A REGARD OVER THE EMISSIONS TEST CYCLES 2.1 New European Driving Cycle (N.E.D.C.) European test cycle (NEDC - New European Driving Cycle) is the approved cycle used for all new cars in Europe. This cycle consists in tracking a reference speed correlated with a certain ratio of the gearbox. Before carrying out the cycle it is mandatory that the vehicle is acclimated for 12 hours at a temperature of 20 ° C.



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Figure 1. European test cycle

The parameters which characterize these cycles are summarized in Table 2.

Table 2. The characteristics of the European cycle

Characteristics ECE EUDC NEDC

Length [km] 0.9941 6.9549 10.9314

Total time [s] 195 400 1180

Idle speed/stabilized time [s] 57 39 267

Average speed with stops [km/h] 18.35 62.59 33.35

Average speed without stopping [km/h] 25.93 69.36 43.10

Maximum speed [km/h] 50 120 120

Average acceleration [m/s2 ] 0.599 0.354 0.506

Maximum acceleration [m/s2] 1.042 0.833 1.042 The test cycle is carried out at an ambient temperature, between: 20-30 C (typically 25 ° C), when the engine is cold. To obtain more representative results, the tests are performed on special vehicle dyno stands which can simulate the road resistance of the car [2]. Also the testing procedure is performed by turning off all the car accessories (air conditioning, lighting, window defroster, etc.). Declared mixed fuel consumption is calculated by combining the consumption results recorded on the two phases of the cycle (urban and extra urban phase). The result is a theoretical total distance of 11023 m traveled in a 1180 s at an average speed of 33,6 km/h. The main disadvantage of NEDC test cycle is that currently can’t reproduce the real driving conditions. Major differences in fuel consumption, between the declared one and the real one, are explained by numerous objective factors that influence fuel consumption and are not taken into consider in the test cycle (driving style, the technical condition of the vehicle, routes traveled, etc.). In the future, the NEDC cycle is intended to be replaced by a more realistic test cycle, together with associated test procedure called WLTP (Worldwide Harmonized Light Vehicles Test-Procedures). 2.2 Artemis test cycle (CADC-The Common Artemis Driving Cycles) Artemis cycle is a new procedure for testing the light vehicles on dyno bench developed within European Artemis project (Assessment and Reliability of Transport Emission Models and Inventory Systems) based on statistical analysis of a database that includes multiple models of specific traffic management and European style. Driving cycle includes three stages: (1) urban, (2) extra urban and (3) highway. Highway type driving style presents two versions, with a limit of 130 km/h and 150 km/h [3].


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Figures 2, 3, and 4 shows the change in vehicle speed to the 3-step cycle of Artemis. Definition cycle includes a number of strategies to change gear.

Figure 2. Artemis urban cycle phase Figure 3. Artemis extra-urban cycle phase

Figure 4. Artemis motorway cycle phase

Artemis cycle characteristics are presented in the following table.

Table 3. Artemis cycle (CADC)

Characteristics Urban cycle

Extra-urban cycle

Highway cycle 130 km/h

Highway cycle 150 km/h

Duration [s] 920 1081 1067 1067 Distance [km] 4.47 17.27 28.74 29.55 Average speed [km/h] 17.5 57.5 97 99.7 Maximum speed [km/h] 58 112 132 150 Speed distribution [%]

- Idling (S = 0 km/h) 29 3 2 2 - Low speed (0 < S ≤ 50) 69 31 15 15

- Medium speed (50 < S ≤ 90) 2 59 13 13 - High speed (S > 90) 0 7 70 70

For each cycle, RPA (relative acceleration- positive relative acceleration in m/s2), average vehicle speed (km/h) and emissions in mg/km were calculated.


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RPA is a value which characterizes the load on the cycle and is often used as a comparison factor between several different cycles, as calculated by equation 1:

s

tva

RPA

i

n

ii ∆⋅⋅

=

∑=1 (1)

In which: • ai: acceleration for the time step i only if ai>0, [m/s²] • vi: vehicle speed for time step i, [m/s] • ∆t: the difference between two successive steps (=1), [s] • s: the total cycle distance, [m].

Figure 5 presents all the vehicle acceleration function of vehicle speed NEDC and car speeds in NEDC and CADC. The graph shows that the NEDC cycle has constant accelerations and decelerations. In contrast, CADC cycle covers a wider range of engine field regimes. Comparing the engine operating conditions imposed by the two cycles we find that the steady speed percentage for the NEDC cycle is significant. On CADC cycle is observed that transients (acceleration / deceleration / idling) have a dominant share. It is concluded here that Artemis cycle is more severe, knowing that in a transient state are recorded the most significant concentrations of CO2 and pollutant emissions.

Figure 5.Vehicle acceleration vs vehicle speed on cycle NEDC and CADC

Figure 6 presents the test results of Euro 6 diesel vehicle equipped with specific depollution systems (catalyst, particle filter and NOx Trap), which was tested on CADC and NEDC cycles. It appears that areas with the highest NOx emissions are randomly distributed on the graph. It is noted that although we have the same value RPA and average speed can have different emissions. However, RPA and the average speed of the vehicle fully characterize emissions. Vehicle emission levels depend on the operating point of the engine required by calibration cartograms based on torque and speed, so two identical cycles compared with RPA and the average speed may differ depending on the gear ratio. This analysis shows the importance of imposing gears speeds on the depollution cycle. This fact was taken into account for the implementation of WLTP cycle. For manual gearboxes, gear shifting is based on vehicle speed. WLTP consider this as a first option, but keep the second alternative of imposing a single point of the gear shift depending on the vehicle. Another option would be changing gears using the gear shift indicator (GSI gear shift indicator) of each vehicle [4]. However must be noted that CADC is only one intermediate step to a cycle with a higher severity, namely the cycle WLTP.


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2.3 WLTP test cycle (Worldwide Harmonized Light Vehicles Test-Procedures) WLTP represent cycle aimed to test light vehicles. Testing it is still on a dyno bench for emission and fuel consumption. The test is developed by UN ECE GrpE (Working Group on Remediation and Energy). WLTP includes three test cycles applicable to vehicles divided into different categories (noted RPM- parameter power ratio [W] / weight [kg]). Its values are given explicitly in Table 4. Regimes classes given in this table are defined in relation to vehicle speed parameter values declared by the manufacturer and RPM.

Figure 6. NOx emissions of vehicle exhaust

Table 4. WLTP cycles

Category RPM Speed phases Comments

Class 3 >34 Low, medium, high, very high.

If Vmax < 135 km/h then the phase " very high " is replaced by repeated phase ”high”.

Class 2 34≥RPM≥22 Low, medium, high. If Vmax < 90 km/h then the phase " high " is

replaced by repeated phase ”low”.

Class 1 ≤22 Low, medium.

If Vmax ≥70 km/h then the phase " low " is replaced by repeated phase ”medium”. If Vmax <70 km/h then the phase " medium " is replaced by repeated phase ”low”.

With the largest ratio RPM, the class three cycle is representative for the vehicles driven in Europe and Japan. Selected parameters of class 3 cycle are presented in Table 5 and the vehicle speed and acceleration are shown in figure 7 [5]. 3. CONCLUSION The new calibration methods in order to be developed for new cycles of depollution CADC and WLTP involve large theoretical and experimental research to ensure compliance with regulations. There is a calibration severity as actual cycle involves more transient stages, however, at least in this moment, does not require changes to the norms of pollutant concentrations from 2014, but an increase of the transient regimes. In this context, the principles of the calibration require a different approach in terms of more stringent NOx emissions because the new test cycle is further characterized by the transient operating conditions. To achieve the development of a powertrain, in these conditions, requires a significant amount of experimental research. The objectives of good collaboration do not only obtain low values of pollutant concentrations but also preserving dynamic performance and economic conditions of low production and maintenance costs.


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Table 5. WLTP class 3 cycle

Characteristics Low Medium High Very high Total

Duration [s] 589 433 455 323 1800

Stop time [s] 156 48 31 7 242

Distance [m] 3095 4756 7158 8254 23262

Percentage of stops [%] 26.5% 11.1% 6.8% 2.2% 13.4%

Maximum speed [km/h] 56.5 76.6 97.4 131.3

Average speed without stops [km/h] 25.7 44.5 60.8 94.0 53.8

Average speed with stops [km/h] 18.9 39.5 56.6 92.0 46.5

Minimum acceleration [m/s2] -1.5 -1.5 -1.5 -1.2

Maximum acceleration [m/s2] 1.5 1.6 1.6 1.0

Figure 7. WLTP class 3 cycle REFERENCES [1] Tuță , A., Principii ale calibrării motoarelor cu aprindere prin comprimare, Raport de cercetare nr. 1, Universitatea din Pitesti, 2014. [2] Pacheco, A., Martins, F.M.E.S. and Zhao, H., New European Drive Cycle (NEDC) simulation of a passenger car with a HCCI engine: Emissions and fuel consumption results, Fuel, vol. 111, 2013. [3] Andre, M., The ARTEMIS European driving cycles for measuring car pollutant emissions, Science of the Total Environment, vol. 334–335, 2004. [4] Demuynck, J., Bosteels, D., De Paepe, M., Favre C., May J. and Verhelst, S., Recommendations for the new WLTP cycle based on an analysis of vehicle emission measurements on NEDC and CADC, Energy Policy, vol. 49, 2012. [5] http://www.dieselnet.com, Emission Test Cycles WLTP.

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The Scientific Journal of SIAR A Short History

The engineering of vehicles represents the engine of the global development of the economy. SIAR tracks the progress of the automotive engineering in Romania by: the development of automotive engineering, the development of technologies, and road transport services; supporting the work of the haulers, supporting the technical inspection and of the garage; encouraging young people to have a career in the automotive engineering and road haulage; stimulation and coordination of activities that promote an environment that is suitable for continuous education and improving of knowledge of the engineers; active exchange of ideas and experience, in particular for students, master students, PhD students, and young engineers, and dissemination of knowledge in the field of automotive engineering; cooperation with other technical and scientific organizations, employers’ and socio-professional associations through organization of joint actions, of mutual interest. By the accession to FISITA (International Federation of Automotive Engineering Societies) since its establishment, SIAR has been involved in achieving an overall professional community that is homogeneous in competence and performance, interactive, dynamic, and competitive at the same time, oriented towards a balanced and friendly relationship between people and the environment; this action will be constituted as a challenge worthy of effort and recognition. The insurance of a favorable framework for the initiation and the development of cooperation of the specialists in this field of activity allows for an efficient and easy exchange of information, specific knowledge and experience; it supports the cooperation between universities and between research centers and industry; it speeds up the process of implementing the new technologies, it simplifies the identification of training and specialization needs of the personnel involved in the engineering of motor vehicles, transport, and road safety. In order to succeed, ever since its founding, SIAR has considered that the stress should be put on the production and distribution, at national and international level, of a publication of scientific quality. Under these circumstances, the development of the scientific magazine of SIAR had the following evolution: 1. RIA – Revista inginerilor de automobile (in English: Journal of Automotive Engineers) ISSN 1222 – 5142 Period of publication: 1990 – 2000 Format: print, Romanian

Frequency: Quarterly Electronic publication on: www.ro-jae.ro

Total number of issues: 30 Type: Open Access

The above constitutes series nr. 1 of SIAR scientific magazine.

2. Ingineria automobilului (in English: Automotive Engineering) ISSN 1842 – 4074

Period of publication: as of 2006 Format: print and online, Romanian

Frequency: Quarterly Electronic publication on: www.ingineria-automobilului.ro

Total number of issues: 33

(including the December 2014 issue)

Type: Open Access

The above constitutes series nr. 2 of SIAR (Romanian version).

3. Ingineria automobilului (in English: Automotive Engineering) ISSN 2284 – 5690

Period of publication: 2011 – 2014 Format: online, English

Frequency: Quarterly Electronic publication on: www.ingineria-automobilului.ro

Total number of issues: 16

(including the December 2014 issue)

Type: Open Access

The above constitutes series nr. 3 of SIAR (English version).

4. Romanian Journal of Automotive Engineering ISSN 2284 – 5690

Period of publication: from 2015 Format: online, English

Frequency: Quarterly Electronic publication on: www.ro-jae.ro

Total number of issues: 1 (March 2015) Type: Open Access

The above constitutes series nr. 4 of SIAR (English version).

Summary – on March 31st. 2015 Total of series: 4 Total years of publication: 21 (11=1990 – 2000; 10=2006-2015) Publication frequency: Quarterly Total issues published: 64 (Romanian), out of which, the last 17 were also published in English

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