KEYNOTE: VIRTUAL MANUFACTURING PRACTICE AND ADVANCED APPLICATIONS

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1 KEYNOTE: VIRTUAL MANUFACTURING PRACTICE AND ADVANCED APPLICATIONS Professor Leo J De Vin Virtual Systems Research Centre University of Skövde, Sweden THREE UNIVERSITY RESEARCH CENTRES + ONE RESEARCH PROGRAM Bild 1 Bild 2 VIRTUAL SKÖVDE PRESENTATION OUTLINE Intelligent Automation a main actor in the Virtual Systems Research Centre. 2009: Preferred Academic Research Partner under the Volvo Group Academic Partner Program in the area of Virtual Manufacturing (world wide). Simulation what & how, examples Virtual Manufacturing good practice & pitfalls REVVA (Verification, Validation, Acceptance) daisy (support for industrial VM projects) Advanced applications Information Fusion and Simulation Bild 3 Bild 4 BUT FIRST: CHILDREN S PLAY COWBOYS AND INDIANS What about the use of a walkie talkie radio set, was that: realistic,,given the game influencing the fun of playing something we decided to accept, yes or no? A DEFINITION OF SIMULATION Simulation: Experimentation with some model of a System of Interest (SoI) This is very similar to VV&A (Verification, Validation and Acceptance) in simulation projects! Bild 5 Bild 6 1

2 MANY WAYS TO USE SIMULATION, E.G.: To study an SoI To train operators As part of a synthetic (AR/VR) environment To support distributed development work To test/benchmark algorithms or control code Serious gaming To test people s reactions or attitudes Gaming primarily as entertainment COMMENTS ON SIMULATION The results from a simulation must be assumed to be wrong, unless there is evidence that they are correct - Stumbled upon on Computer simulation i usually provides more insight into the model than into the simulated system - Eisso Atzema, 1994 SoI System of Interest, but also Situation/Scenario of Interest Bild 7 Bild 8 VIRTUAL MANUFACTURING RESOURCE SIMULATION VIRTUAL MANUFACTURING TOOLS, EXAMPLES Geometry simulation / continuous simulation / Computer Aided Robotics (CAR) Ergonomy simulation Production flow simulation / Discrete Event Simulation (DES / DEVS) PPR Hub Systems Dynamics (SD) Bild 9 Bild 10 EXAMPLE OF CAR EXAMPLE OF DES ANIMATION SNAPSHOT Simulated assembly cell (left) and real cell (right) Bild 11 Bild 12 2

3 VIRTUAL WORKPLACE EVALUATION TYPICAL SIMULATION PROJECT PHASES Bild 13 Bild 14 MOST COMMON SIMULATION PITFALLS 1. Ill-defined goals or problem 2. Modelling and data acquisition take too much time not enough time left for the rest 3. Start too ambitious simplifications along the way simulation may become a goal in itself 4. Unintended extended use of model 5. Own conclusions drawn from animation 6. Used as replacement for engineering knowledge / common sense 7. Decision was already made REVVA: VERIFICATION, VALIDATION AND ACCREDITATION/ACCEPTANCE Defense industry based, organisational aspects Bild 15 Bild 16 REVVA PROCESS IN A NUTSHELL Bild 17 Bild 18 3

4 VV&A ORGANISATION, PLAYERS RISK OF GUESSING VERSUS COST OF KNOWING Bild 19 Bild 20 LIMITATIONS OF HUMAN PERCEPTION PERCEIVED VALIDITY INDICATORS Bild 21 Bild 22 INDUSTRIAL NEED FOR SIMULATION PROJECT SUPPORT DAISY DAISY ELECTRONIC HANDBOOK Introduction Typical problems for the manufacturing industry: Implementation Strategy General Simulation Technique Simulation handbooks typically too thick and too general Doctoral theses, research papers etcetera are often too theoretical Simulation consultants have their own agenda Methods like REVVA focus on defense industry, not manufacturing Concept Documentation of Simulation Models Design and Implementation Life-Cycle Phases Operation Redesign Manager/ Strategic Planner Project Manager Simulation Engineer Bild 23 Bild 24 4

5 SOME ADVANCED APPLICATIONS OF VIRTUAL MANUFACTURING MONITORING EXAMPLE: MASSIVE Hardware-in-the-loop simulation DES: On-line monitoring, simulationsupported planning and scheduling CAR: Remote monitoring, diagnostics and service support Simulation-based optimisation Operation allocation, line balancing Multi-objective optimisation Bild 25 Bild 26 MASSIVE MAIN COMPONENT GROUPS PRESS LINE EXAMPLE Bild 27 Bild 28 CONFIGURABLE USER INTERFACE SIMULATION-BASED OPTIMISATION Simulation itself is no optimisation tool! Bild 29 Bild 30 5

6 SIMPLE EXAMPLE: SIMBOSEER MULTI-OBJECTIVE OPTIMISATION Real-world problems are inherently complex Partially or wholly conflicting objectives, e.g. Minimise production costs Maximise production output Reduce delivery time Minimise environmental impact Best compromise varies from case to case and over time Bild 31 Bild 32 PARETO NEAR-OPTIMAL FRONT FROM SIMULATION-BASED OPTIMISATION SIMULATION AND INFORMATION FUSION Information Fusion: Synergistic combination of information from different sources, e.g.: databases, sensors, simulations, humans different in time, place and/or modality Examples: sensor fusion (low), conflict resolution in databases (low), decision support (high) Generic reference models: OODA loop and JDL model Bild 33 Bild 34 JDL MODEL ORIGINAL JDL MODEL Describes levels of processing/refinement Describes outcomes in terms of a.o. Object (event) identification Situation awareness Impact analysis Origin in U.S. defense research (JDL = Joint Directors of Laboratories) Bild 35 Bild 36 6

7 LEVELS IN JDL MODEL LEVELS 0 AND 1 Different levels of processing/refinement 0. Pre-processing (of, e.g., sensor data) 1. Object refinement 2. Situation refinement/awareness 3. Threat refinement / impact analysis 4. IF process refinement 5. User refinement (Blasch & Plano) Will now be compared to phases/activities in simulation projects Bild 37 Bild 38 LEVELS 2 AND 3 LEVELS 4 AND 5 Bild 39 Bild 40 MULTIPLE PROJECTS LEVEL 3 4 AND 5 MULTIPLE PROJECTS LEVEL 3 4 AND 5 Bild 41 Bild 42 7

8 THANK YOU, QUESTIONS? June 2014 ISC2014 Mechatronics Sensor fusion workshop (?) Contact Adolfsson, J. Ng, A.H.C. and Moore, P.R. (2000), Modular Machine System Design Using Graphical Simulation, 33rd CIRP International Seminar on Manufacturing Systems, Royal Institute of Technology (KTH), Stockholm, Sweden, pp Banks, J., Carson, J.S. and Nelson, B.L. (1996), Discreteevent system simulation, Prentice-Hall, Inc., Upper Saddle River, New Jersey 07458, 2nd edition Brade, D. (2004), A Generalized Process for the Verification and Validation of Models and Simulation Results. Dissertation, Fakultät für Informatik, Universität der Bundeswehr München, Bild 43 Bild 44 De Vin, L.J., Oscarsson, J., Ng, A.H.C., Jägstam M. and Karlsson, T. (2004), Manufacturing Simulation: Good Practice, Pitfalls, and Advanced Applications, IMC-21, Limerick Ireland, pp De Vin, L.J., Lagerström, H. and Brade, D. (2006), Verification, Validation and Accreditation for Manufacturing Simulation, FAIM 2006, Limerick, i Ireland, pp De Vin, L.J., Ng, A.H.C., Oscarsson, J., Andler, S.F. (2006), Information Fusion for Simulation Based Decision Support in Manufacturing, Robotics and Computer-Integrated Manufacture, Vol 22, pp De Vin, L.J. (2007), The Role of Simulation in Engineering Design and Production Development Processes, FAIM 2007, Philadelphia, pp De Vin, L.J. (2009), On the Suitability of the Information Fusion JDL-U Model as a Reference Model for Virtual Product and Production Development, FAIM 2009, Middlesbrough UK, pp Holm, M., Doverborn, J., Ng, A.H.C. and De Vin, L.J. (2009), Optimisation of Operation Sequences in Flexible Manufacturing Cells using Virtual Manufacturing Tools, FAIM 2009 Conference, Middlesbrough UK, pp Bild 45 Bild 46 Jägstam, M., Oscarsson, J. and De Vin, L.J. (2004), Implementation in industry of a handbook for simulation methodology, 37th CIRP Seminar on Manufacturing Systems, Budapest. Ng, A.H.C., Svensson, J. and Urenda Moris, M. (2008), Introducing Simulation-based Optimization for Production Systems Design to Industry: the FACTS Game, FAIM 2008 Workshop Paper, CD-ROM Proceedings pp NSF (2006), Simulation-Based Engineering Science, df Pettersson, L., Adolfsson, J., Ng, A.H.C. and De Vin, L.J. (2007), Cell Monitoring and Diagnostics Using Computer Aided Robotics, Proceedings of 40th CIRP International Seminar on Manufacturing Systems, Liverpool UK Urenda Moris, M., De Vin, L.J. and Eriksson, P. (2004), Introducing discrete event simulation for decision i support in the Swedish health care system, In Proceedings of the 2004 Western MultiConference: Health Sciences Simulation 2004 (Eds. Anderson J.G. and Katzper M.), San Diego CA:SCS-Society for Modeling and Simulation International, San Diego, CA., pp Bild 47 Bild 48 8