Technical Research Institute of JSPMI(Japan Society for the Promotion of Machine Industry),
1-1-12 Hachiman-cho, Higashikurume-city, Tokyo, Japan, [email protected]
SIMULATION ENVIRONMENT FORMANUFACTURING CELL TO SUPPORT FULL
PRODUCTION PERFORMANCEAT PRODUCT LAUNCH
Abstract
It is now important to reduce lead-time from the manufacturing system design stage to the manufactur-ing system implementation stage and support full production performance at the product launch, be-cause manufacturing industries face various problems such as shorter product life cycle. Before a manu-facturing system is implemented, it is difficult to complete all the facility control programs for severalpieces of equipment used in the manufacturing system currently. Before the real manufacturing systemruns, methods to confirm implementation of production management applications, which need to evalu-ate and improve manufacturing systems at the product launch such as a facility behaviours recordapplication, have not been developed. These difficulties stopped precise and rapid support of a manu-facturing engineering process.In order to solve these difficulties, it is necessary to develop a method to mix and synchronize realequipment, virtual factory models on the computers, and production management applications.In our research, in order to reduce the lead-time from the design stage to the implementation stage, amanufacturing engineering environment (MEE) is proposed. MEE consists of a manufacturing cell simu-lation environment (MCSE) and a distributed simulation environment (DSE). MCSE, which is used tocreate and evaluate facility control programs while mixing and synchronizing real equipment, virtualfactory models, and production management applications before manufacturing systems are imple-mented, is emphatically proposed in detail. MCSE consists of a manufacturing cell simulator, a soft-wiring system, and ORiN. The manufacturing cell simulator simulates facility behaviours by using datafrom the soft-wiring system. The soft-wiring system connects real world data, simulation world data onthe manufacturing cell simulator, and data on manufacturing management applications without hard-wiring. The soft-wiring system also connects manufacturing cell and production management applica-tions. ORiN is a standard distributed network system for manufacturing systems. Finally the case wascarried out to evaluate MCSE.
Keywords: Manufacturing cell, simulation, PLC, robot, production launch.
Author’s biography Dr. Eng. HIRONORI HIBINO is a Senior Researcher of the Technical Re-search Institute of Japan Society for the Promotion of Machine Industry (JSPMI),which is an affiliate of the Ministry of Economy, Trade and Industry in Japan. He isa guest professor at the Tokyo University of Agriculture and Technology concur-rently. His research fields are distributed simulation, manufacturing system designusing simulation, manufacturing cell simulation for combinations of real world andsimulation world. He is an academic member of JSME, JIMA, and JSPE. He isalso a director of the Scheduling Society of Japan.
Proc. EUROSIM 2007 (B. Zupančič, R. Karba, S. Blažič) 9-13 Sept. 2007, Ljubljana, Slovenia
simulator simulates facility behavior by using data fromthe soft-wiring system. The soft-wiring system connectsreal world data, simulation world data on themanufacturing cell simulator, and data on manufacturingmanagement applications without hard-wiring. The soft-wiring system also connects manufacturing cell andproduction management applications. ORiN is a standarddistributed network system for manufacturing systems.Finally, evaluation of the performance of cooperativework is shown.
2. Manufacturing Engineering Environment A manufacturing system is defined as a CIM refer-ence model in ISO standards[8]. The manufacturing sys-tem is classified into six layers in the model. Figure 1illustrates the typical system structure. In general, thesequence controller means a PLC, and the motion con-troller means a robot controller or a single axis controller.And simulation is mainly used at the design stage ofeach layer. The manufacturing engineering procedure is usuallybased on the waterfall model. Although the original pur-pose of the waterfall model is to reduce the waste ofloop-back and re-doing, still there are many loop-backsfor each manufacturing engineering process. Withoutreducing these loop-backs, shortening manufacturingsystem development time is difficult. The developmentstage is composed of the design stage and the imple-mentation stage, and its development time is defined by
the following equation.
Development time = Loop frequency × Average looptime
Therefore, it is necessary to reduce the loopfrequency or to shorten the loop time for developmenttime reduction.
To reduce the loop frequency, the upper-layereddesign process should be highly precise. Currentsimulators on the market such as a manufacturing linesimulator are designed for special purposes. In order todesign and implement the manufacturing system, many-faceted evaluation is required. To realize this, it isnecessary to collaborate with several simulators, or tocollaborate with the simulators and real equipment. Thecollaborations lead to the wide-use of the simulator atthe implementation stage that was difficult so far.Consequently, the average of loop time also can bereduced because of quick loop-backs in the simulation.However the simulators on the market are not consideredthe above usages.
Therefore we propose a manufacturing engineeringenvironment (MEE) to connect the design stage andthe implementation stage. MEE consists of two sub-environments. One sub-environment is a manufacturingcell simulation environment (MCSE). The main role ofMCSE is to make and evaluate facility control programsfor a manufacturing cell while mixing and synchronizingreal equipment, virtual factory models, and productionmanagement applications.
1 Introduction Recently, manufacturing industries face various prob-lems such as shorter product life cycle, more diversifiedcustomer needs[1]. It is now important to reduce the lead-time from the manufacturing system design stage to themanufacturing system implementation stage. A manu-facturing system simulator plays an important role indesigning and evaluating manufacturing systems byusing a virtual factory model at the manufacturing sys-tem design stage[2][3][4]. At the manufacturing systemimplementation stage, it is important to make and evalu-ate facility control programs for a manufacturing cell,such as ladder programs for programmable logical con-
trollers (PLCs) rapidly[5].Ordinarily the facility control programs not only
control a single piece of equipment such as a robot anda sensor, but often control several pieces of equipmentsimultaneously(6]. Therefore contents of the programsare usually complicated. It is thus very important toevaluate the programs before executing the programsusing real equipment. Concerning the facility controlprograms for a single piece of equipment such as partsof a robot program and a NC program, it is now possibleto evaluate the programs on computers[7].
However, before a manufacturing system isimplemented, it is difficult to complete all the facilitycontrol programs for several pieces of equipment usedin the manufacturing system. Although it is essentialfor completing the facility control programs to get realdata from the real equipment with precise timing, it isnot possible to get the data before the manufacturingsystem is implemented. Before real manufacturingsystem runs, methods to confirm implementation ofproduction management applications, which need toevaluate and improve manufacturing systems at theproduct launch such as a facility behavior recordapplication, have not been developed. These difficultiesstopped precise and rapid support of a manufacturingengineering process. Consequently the lead-time to getfull production performance at the product launch isnot reduced. To solve these difficulties, it is necessaryto develop a method to mix and synchronize realequipment, virtual factory models on the computers, andproduction management applications.
In our research, in order to reduce the lead-time fromthe design stage to the implementation stage, amanufacturing engineering environment (MEE) isproposed. MEE consists of a manufacturing cellsimulation environment (MCSE) and a distributedsimulation environment (DSE), where ‘environment’means a tool-set like an integrated developmentenvironment (IDE). MCSE, which is used to create andevaluate facility control programs while mixing andsynchronizing real equipment, virtual factory models,and production management applications beforemanufacturing systems are implemented, is emphaticallyproposed in detail.
MCSE consists of a manufacturing cell simulator, asoft-wiring system, and ORiN. The manufacturing cell
Proc. EUROSIM 2007 (B. Zupančič, R. Karba, S. Blažič) 9-13 Sept. 2007, Ljubljana, Slovenia
Another sub-environment is a distributed simulationenvironment (DSE). The main role of DSE is to connectMCSE and manufacturing system simulators at thedesign stage. Distributed simulation is defined as theexecution of a simulation while connecting andsynchronizing several different simulators. In the past,we have researched the distributed simulationtechnologies for manufacturing systems evaluation. Thedistributed simulation technologies are applied in DSE,and the two environments are mutually connected by agateway system[2][3][9][10].
Figure 2 shows an outline of MEE, where ‘emulator’means software simply imitating the existing equipment.On the other hand, ‘simulator’ means software whichhas an extended function to the real world such as timemanagement. In this paper, we propose MCSE in thefollowing chapters in detail.
3. Manufacturing Cell Simulation Environ-
mentTo realize the manufacturing cell simulation
environment (MCSE) which simulates manufacturing cellbehaviors by synchronizing the real controllers and the
emulators of real controller, the following functions arenecessary.
1. A simulation function to simulate manufacturingcell behaviors by using data from a real worldwhich consists of the real controller and the emu-lators of real controller.
2. A wiring function to logically wire the real worlddata, the simulation world data on a simulationworld implemented the simulation function, anddata on manufacturing management applications.
3. A transmission function to transmit the signals
Fig.1 Manufacturing system structures and a usagerange of the simulation in the CIM reference model.
Fig. 2 An outline of our proposed manufacturing engineering environment.
and the data between the simulation world andthe real world without considering the differencesbetween the real controllers and the emulators.
4. A network interface function to connect the realcontroller and the emulators with a standard man-
ner.
In order to realize these functions, we propose amanufacturing cell simulator to accomplish the simulationfunction 1, and a soft-wiring system to perform the wiringfunction 2. The manufacturing cell simulator simulatesmechanical behavior in accordance with signals fromthe soft-wiring system.
The function 3 and 4 are achieved by ORiN. ORiN(Open Resource interface for the Network / Open Robotinterface for the Network) is a middle-ware based on anobject oriented technology[11]. It provides a standardaccess method to various controllers.
ORiN consists of two parts, an engine and a provider.The ORiN engine provides the application interfaces andsome sophisticated common functions. An ORiNapplication operates abstracted equipment. This meansthat the real equipment is capsuled by the engine. Theabstracted equipment model is defined by the ORiNspecification. And the ORiN provider absorbs thedifferences between abstracted equipment and realequipment. For example, a request from the engine(originally from an application such as the soft-wiringsystem) is converted into a dedicated protocol of thereal equipment such as a PLC and a robot in the provider.
The soft-wiring system and the manufacturing cellsimulator are running on ORiN. Using ORiN, theproposed systems can become more general because ofthe independence from the equipment’s specifications.Figure 3 shows an outline of MCSE. Figure 4 showsinformation flows of manufacturing cell simulationenvironment. Figure 5 presents an outline of ORiN.
4. Soft-wiring SystemIn real manufacturing systems, wiring between
controllers is usually used hard-wiring. As MCSE is usedbefore manufacturing systems are implemented, we needto evaluate the facility control programs of the controllerwithout hard-wiring. Therefore the soft-wiring system,which logically wires the real world data, the simulationworld data, and data on manufacturing managementapplications, is necessary. This system executes thefollowing steps for each controller asynchronously andindependently.
Step 1. Making connection to real equipment or itsemulator through ORiN.
Step 2. Observing the item value of the equipmentcontinuously.
Step 3. Propagating the value to linked item whenever
it detects the change of value.
However there are many types of data on thecontroller such as integer type, real type, character type Fig. 5 An outline of ORiN.
Application based on other standards.
FDML
Internet
FDC
UPnPUPnPOPCOPCApp.Z
ORiN ORiN
A Co. B Co.
App.X
App.Y
Application(Area layerCell layerIn ISO)
Engine
Provider
Device(Station layer
Equipment layerIn ISO)
C Co. D Co.
Dev.B
Abstract Device
Application IF
Device IF
Rb(Robot)
Machine tool
PLCC/P(Control panel)
Barcode Reader
ORiN
Soft-wiring System
Manufacturing Cell Simulator
Robot Provider PLC ProviderBar Code Provider
Operation Panel Provider・・・
Real Equipment or Emulators
3. Cell
ISO/CIM Reference Model
Mfg. Management appl.
2. Station
1. Equip.
Fig. 3 An outline of manufacturing cell simulation envi-ronment.
Fig. 4 Information flows of manufacturing cell simula-tion environment.
and so on. In order to simulate manufacturing cellbehaviors using signals and data from a real world, it isnecessary to transform data on the real world (or asimulation world) into utilizable data on the simulationworld (or the real world). The real world means that aworld consists of real equipment. The simulation worldmeans that a world consists of equipment emulatorsand manufacturing cell simulation models. Therefore thesoft-wiring system realizes to logically wire the real worlddata and the simulation world data which are modeledon a simulation world. The system mainly needs to havethe following functions for step 3.
1. A function to logically wire data between the realworld and the simulation world while transform-
Robotprovider
Real equipment or Emulator
PLCprovider
Bar-codeprovider
OP-Panelprovider
Mfg. Cell Simulator
ORiN
Soft-wiring System
Manufacturing Management ApplicationsEx. Working Monitoring
3.Cell
2.Station
1.Equipment
Information flow at the execution stage.
Information flow at the implementation stage.
ISO/CIM Reference Model
Production Controlappl.
Proc. EUROSIM 2007 (B. Zupančič, R. Karba, S. Blažič) 9-13 Sept. 2007, Ljubljana, Slovenia
ing and propagating data in response to datatypes.
2. A function to perform the data conversions suchas BCD (Binary Coded Decimal) data conversion,data masking, data type conversion such as‘String to Integer’, data calculation and so on.
3. A function to filter out data which are over a limi-tation value as a dead band function.
4. A function to record a data transition log to evalu-
ate a timing chart of an action sequence.
The soft-wiring system which includes four functionswas developed in C++ program language. Figure 6 showsan outline of the soft-wiring system.
5. Manufacturing Cell Simulator In real manufacturing systems, it is possible to evalu-ate facility control programs by producing materials withflow while synchronizing real equipment. As MCSE isused before manufacturing systems are implemented, itis impossible to evaluate the facility control programsby the same way. Therefore we need to have a world tosimulate producing materials with flow while mixing andsynchronizing real equipment, its emulators and virtualfactory models. We propose the manufacturing cell simu-lator to realize the world. Figure 7 shows an outline ofthe simulator. The manufacturing cell simulator simulates manufac-turing cell behaviors using signals and data from the realworld through the soft-wiring system. The manufactur-ing cell simulator needs to have mainly following func-
tions.1. A definition function to define behaviors for
manufacturing cell simulation models.2. An animation function to visualize 3D manufac-
turing cell simulation models by synchronizingwith the results of manufacturing cell behaviors.
3. A connection function to link the simulation worlddata to the real world data through the soft-wir-
ing system.
We propose that the behaviors are expressed by atree structure. One task of computer processes is de-fined on one node of the tree structure. Complex taskscan be defined by combining several nodes. And there
are main four types of nodes.1) Flow control nodes such as branch, join, and trig-
ger.2) A link node to link data on the simulator to the
soft-wiring system3) An animation control node to execute the anima-
tion function4) A script node to define the details of behavior.
All nodes of the tree are executed in series or in paral-lel. The vertical direction is executed sequentially, andthe horizontal direction is executed in parallel.
A link node 2) can refer the real world data throughthe soft-wiring system. Then the manufacturing cell simu-lation model in the simulation world is behaved in re-sponse to the referred data. The node also can outputdata to the real world data through the soft-wiring sys-tem. Then the equipment in the real world executes pro-cesses in response to the output data. That is, the linksare bi-directional connection. For instance, when a cyl-inder reaches the terminal point, the cell simulator canoutput a signal to the soft-wiring system; it means thatthe system can output a signal to both real equipmentand an emulator. And then the equipment which received
a signal can continue to execute a program by the signal.
6. Case StudyA case study was carried out using a hypothetical
case of small size of a manufacturing cell which consistsof a robot, a tester, a palettizer and a conveyor. Thiscase study consists of a robot controller, a bar-codereader and a palate as real equipment. And a PLC emulatorand operation panel emulator are also connected. Onthe other hand, there are four systems, productioncontrol system, manufacturing cell simulator, soft-wiringsystem, and working monitoring system. The productioncontrol system and the working monitoring system arereal systems running in the real manufacturing system.Figure 8 shows the case study models on themanufacturing cell simulator.
・・・・・
Soft-wiring System
…..
…..
…..
Real equipment or Emulator
Process Flow:Step 1: Make a connection to the real world or the simulation world logically.
Step 2: Observe the item value of the device continuously.
Step 3: Propagate the value to linked item whenever the value is changed.
Data types:Integer, BCD, Real, String, Array, etc.
Data processing:Convert, Calc, Filter, etc.
Data types:Integer, BCD, Real, String, Array, etc.
Data processing:Convert, Calc, Filter, etc.
Rb(Robot) Machine tool PLC C/P(Control panel)
ORiN
3D Animation
XML(XVL)
3D Graphic Model Equipment Behavior Model Soft-wiring Model
Soft-wiring System
Manufacturing Cell Simulator
Motion Design
XML(EMU)
XML(CSQ)
All models are described in XML.Manufacturing Cell Simulator Simulation Model
Fig. 6 An outline of soft-wiring system.
Fig. 7 An outline of manufacturing cell simulator.
Proc. EUROSIM 2007 (B. Zupančič, R. Karba, S. Blažič) 9-13 Sept. 2007, Ljubljana, Slovenia
A typical procedure in this case study shows thefollowings. Figure 9 presents one of the typicalprocedures in this case study. Figure 10 shows thesystem structure of this case study. Figure 11 presentsone of the working monitoring applications windowswhich are monitored the manufacturing cell in this casestudy.
1. The barcode reader receives production orderinformation by a KANBAN.
2. The production control system receives the pro-duction order information via ORiN.
3. The production control system indicates to in-vestigate the quantity of a product along the pro-duction order information toward the PLC via thesoft-wiring system.
4. The assigned product is picked up and moved tothe conveyor by the palettizer in the simulator.These behaviors are controlled by the PLC viathe soft-wiring system.
5. The assigned product is translated in front of therobot by the conveyor in the simulator. This be-havior is controlled by the PLC via the soft-wir-ing system.
6. The assigned product is picked up and moved to
PALETTIZER
ROBOT
CONVEYOR
TESTER
the tester by the robot. These behaviors are con-trolled by the PLC and the robot controller viaORiN.
7. The assigned product is investigated by thetester in the simulator.
8. Results of this investigation in the simulator areinformed to the PLC via the soft-wiring system.
STEP1
STEP2 Production Control Application
KANBANBar Code Reader
Procedure System behaviors in the case study
STEP4
STEP5
STEP6STEP7
STEP9
STEP10
STEP3
STEP8
PLC
Control Panel
Patlite
Fig. 8 Case study models on manufacturing cell simulator. Fig. 9 Typical procedure in this case study.
Fig. 10 System structure of this case study.
Bar-cord Reader
PatliteProvider
Bar-cord Provider
PLC Emulater
RSLinx
LadderEditer
CAOSQL
ORiN
PC1PC2PC3
PLC
Production Control application
Patlite
Robot Provider
Robot
Network
Control Panel
Manufacturing Cell simulator
KANBAN
Working Monitoring
PC4
Soft-wiring system
OPC Provider
STEP 1 STEP 3,4,5,6,7,8,9,10 STEP8
STEP 3,4,5,6,7,8
STEP 2
STEP 4,5,6,7,9,10
STEP 1,2,4,5,6,7,9,10
STEP 8 STEP 6,9
Proc. EUROSIM 2007 (B. Zupančič, R. Karba, S. Blažič) 9-13 Sept. 2007, Ljubljana, Slovenia
Then the control panel displays the results viathe PLC. The Patlite shows a kind of neon signsalong the results via the PLC.
9. The assigned product is picked up and movedfrom the tester to the conveyor by the robot.These behaviors are controlled by the PLC andthe robot controller via ORiN.
10. The assigned product is translated to the end ofconveyor by the conveyor in the simulator. Thisbehavior is controlled by the PLC via the soft-wiring system.
11. The assigned product is destroyed in the simu-lator.
We confirmed that the simulation world and the realworld could be combined and synchronized using thereal equipment, the emulator, the virtual factory modelsand so on. We also confirmed that verification of thefacility control programs such as ladder program in thePLC could be confirmed by combining andsynchronizing the simulation world and the real world.We also confirmed that pract ical usages ofmanufacturing management applications such asworking monitoring application could be confirmed bycombining and synchronizing the simulation world andthe real world.
Through this case study, we confirmed that MCSEcould be used in the implementation stage. MCSEincluding the soft-wiring system and the manufacturingcell simulator and ORiN, is valid to carry out efficientmanufacturing system implementation at theimplementation stage.
7. ConclusionIn this paper, the manufacturing engineering
environment (MEE) to support manufacturingengineering processes using simulation technologiesis proposed. The manufacturing cell simulationenvironment (MCSE), which includes the soft-wiringsystem and the manufacturing cell simulator and ORiN,is proposed and developed.
The results were:1. To propose a concept of MEE to support manu-
facturing engineering processes using simulation
technologies.2. To propose MCSE to realize making and evaluat-
ing facility control programs while mixing andsynchronizing real equipment and virtual factorymodels before manufacturing systems are imple-mented.
3. To clarify necessary functions for a manufactur-ing cell simulator and a soft-wiring system inMCSE.
4. To confirm through a case study that a simulationworld and a real world could be combined andsynchronized using the manufacturing cell simu-lator and the soft-wiring system.
5. To confirm that verification of ladder programscould be confirmed by combination of the simu-lation world and the real world before real manu-facturing cells are implemented.
6. To confirm that practical usages of a workingmonitoring application as manufacturing manage-ment applications could be confirmed by combi-nation of the simulation world and the real world
before real manufacturing cells are implemented.7. To confirm that MCSE is valid to carry to support
full production performance at product launch.
AcknowledgementsThis research is a part of the study on digital
manufacturing research project of JSPMI. This projectwas supported by funding from the Japan KeirinAssociation.
References[1] Mehrabi, G., Ulsoy, G. and Koren, Y.,
Reconfigurable manufacturing systems: key tofuture manufacturing, Journal of the IntelligentManufacturing, 11:403-419, 2000.
[2] Hibino, H. and Fukuda, Y., A synchronizationmechanism without rollback function fordistributed manufacturing simulation systems,Journal of the Japan Society of MechanicalEngineers, 68:2472-2478,2002. [in Japanese]
[3] Hibino, H. and Fukuda, Y., A user support systemfor manufacturing system design using distributedsimulation, Production Planning and Control,17:128-142, 2006.
[4] Wohlke, G. and Schiller, E., Digital planningvalidation in automotive industry, Proceedings ofthe IFIP WG 5.7 Working Conference on HumanAspects in Production Management, 1:120-128,2003.
[5] Fukuda, Y., The state of the arts for digitalengineering, Journal of the Japan Society ofMechanical Engineers, 106:230-233, 2003. [inJapanese]
[6] Pires, J. and Costa, J., Object-oriented anddistributed approach for programming robotmanufacturing cells, Robotics and ComputerIntegrated Manufacturing, 16:29-42, 2000.
Fig. 11 Working Monitor in this case study.
Proc. EUROSIM 2007 (B. Zupančič, R. Karba, S. Blažič) 9-13 Sept. 2007, Ljubljana, Slovenia
[7] Milfelner, M. and Cus. F., Simulation of cuttingforces in ball-end milling, Robotics and ComputerIntegrated Manufacturing, 19:99-106, 2003.
[8] ISO/TR10314-1, Industrial automation: Shop floorproduction: Part 1: Reference model forstandardization and methodology for identificationof requirements, 1990.
[9] Hibino, H., Fukuda, Y., Yura, Y., Mitsuyuki, K.and Kaneda, K., Manufacturing adapter ofdistributed simulation systems using HLA,Proceedings of the 2002 Winter SimulationConference, 1099-1109, 2002.
[10] Inukai T., Hibino H. and Fukuda, Y., The Gatewayof Real Factory and Virtual Factory using ORiNand HLA, Proceedings of the 5th InternationalConference on Machine Automation , 429-434,2004.
[11] Inukai, T. and Sakakibara, S., Impact of open FAsystem on automobile manufacturing, Journal ofthe Automotive Engineers of Japan, 58:106-111,2004. [in Japanese]
Proc. EUROSIM 2007 (B. Zupančič, R. Karba, S. Blažič) 9-13 Sept. 2007, Ljubljana, Slovenia
