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Multi-level simulation concept for multidisciplinary analysis and optimization of production systems

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Abstract

In the context of digitization and industry 4.0, the production-related disciplines developed powerful simulation models with different scopes and varying levels of detail. As these simulation systems are usually not built in a compatible way, the models cannot be combined easily. Co-simulation techniques provide a promising basis for combining these models into one superordinate model and utilizing it for planning new factories, adapting existing ones or for production planning. However, today’s co-simulation systems do not benefit from the inherent flexibility of the represented production systems. Simulation-based optimization is carried out inside each discipline’s simulation system, which means that interdisciplinary, global optima are often impossible to reach. Additionally, the aspect of human interaction with such complex co-simulation systems is often disregarded. Addressing these two issues, this paper presents a concept for combining different simulation models to interdisciplinary multi-level simulations of production systems. In this concept, the inherent flexibilities are capitalized to enhance the flexibility and performance of production systems. The concept includes three hierarchical levels of production systems and allows human interaction with the simulation system. These three levels are the Process Simulation level, the Factory Simulation level, and the Human Interaction level, but the concept is easily extendable to support additional levels. Within the multi-level structure, each simulation system carries out a multi-objective optimization. Pareto-optimal solutions are forwarded to simulations on higher hierarchical levels in order to combine them and meet flexibly adaptable objectives of the entire production system. The concept is tested by means of a simplified production system, to optimize it in terms of throughput time and electric energy consumption. Results show that the presented interdisciplinary combination of heterogeneous simulation models in multi-level simulations has the potential to optimize the productivity and efficiency of production systems.

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References

  1. Schluse M, Priggemeyer M, Atorf L, Rossmann J (2018) Experimentable digital twins - streamlining simulation-based systems engineering for industry 4.0. IEEE Transactions on Industrial Informatics. http://ieeexplore.ieee.org/document/8289327/

  2. Leobner I, Ponweiser K, Neugschwandtner G, Kastner W (2011) Energy efficient production. In: 2011 World Congress on Sustainable Technologies (WCST), pp 62–67

  3. Altintas Y, Kersting P, Biermann D, Budak E, Denkena B, Lazoglu I (2014) Virtual process systems for part machining operations. CIRP Ann Manuf Technol 63(2):585–605

    Article  Google Scholar 

  4. Wang J, Xue J, Feng Y, Li S, Fu Y, Chang Q (2016) Active energy saving strategy for sensible manufacturing systems operatin based on real time production status. In: IEEE International Conference on Industrial Engineering and Engineering Management. IEEE, Bali, pp 1001-1005

  5. Graßl M, Vikdahl E, Reinhart G (2013) A petri-net based approach for evaluating energy flexibility of production machines. In: 5th International Conference on Changeable, Agile, Reconfigurable and Virtual Production, pp 303–308

  6. Berglund J, Michaloski J, Leong S, Shao G, Riddick F, Arinez J, Biller S (2011) Energy efficiency analysis for a casting production system. In: Proceedings of the 2011 Winter Simulation Conference, pp 1060–1071

  7. Zienkiewicz OC, Cheung YK (1967) The finite element method in structural and continuum mechanics: numerical solution of problems in structural and continuum mechanics. European civil engineering series. McGraw-Hill, New York

    MATH  Google Scholar 

  8. Klocke F, Döbbeler B, Peng B, Lakner T (2017) Fe-simulation of the cutting process under consideration of cutting fluid. Procedia CIRP 58:341–346

    Article  Google Scholar 

  9. Liao T, Jiang F, Yan L, Cheng X (2017) Optimizing the geometric parameters of cutting edge for finishing machining of fe-cr-ni stainless steel. Int J Adv Manuf Technol 88(5-8):2061–2073

    Article  Google Scholar 

  10. Aurich JC, Linke B, Hauschild M, Carrella M, Kirsch B (2013) Sustainability of abrasive processes. CIRP Ann Manuf Technol 62(2):653–672

    Article  Google Scholar 

  11. Siebrecht T, Rausch S, Kersting P, Biermann D (2014) Grinding process simulation of free-formed wc-co hard material coated surfaces on machining centers using poisson-disk sampled dexel representations. CIRP J Manuf Sci Technol 7(2):168–175

    Article  Google Scholar 

  12. Denkena B, Schmidt A, Maaß P, Niederwestberg D, Niebuhr C, Vehmeyer J (2015) Prediction of temperature induced shape deviations in dry milling. Procedia CIRP 31:340–345

    Article  Google Scholar 

  13. Willms H (2008) Methodisches system zur Auslegung von kostenoptimalen und prozessstabilen Fertigungsverkettungen. Dissertation, Rheinisch-Westfälische Technische Hochschule, Aachen

  14. Schlette C, Kaigom EG, Losch D, Grinshpun G, Emde M, Waspe R, Wantia N, Roßmann J (2016) 3d simulation-based user interfaces for a highly-reconfigurable industrial assembly cell. In: 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE, pp 1–6

  15. Colledani M, Tolio T (2013) Integrated process and system modelling for the design of material recycling systems. CIRP Ann Manuf Technol 62:447–452

    Article  Google Scholar 

  16. Liang S, Yao X (2008) Multi-level modeling for hybrid manufacturing systems using arena and matlab. In: 2008 International Workshop on Modelling, Simulation and Optimization. pp. 155–159

  17. Thiede S, Schönemann M, Kurle D, Herrmann C (2016) Multi-level simulation in manufacturing companies: the water-energy nexus case. J Clean Prod 139:1118–1127

    Article  Google Scholar 

  18. Despeisse M, Oates MR, Ball PD (2013) Sustainable manufacturing tactics and cross-functional factory modelling. J Clean Prod 42:31–41

    Article  Google Scholar 

  19. Oates MR, Wright A, Greenough R, Shao L (2011) A new modelling approach which combines energy flows in manufacturing with those in a factory building. In: Proceedings of Building Simulation 2011: 12th Conference of International Building Performance Simulation Association. Sydney, Australia, pp 223–230

  20. Schönemann M, Schmidt C, Herrmann C, Thiede S (2015) Multi-level modeling and simulation of manufacturing systems for lightweight automotive components. In: 48th CIRP Conference on Manufacturing Systems - CIRP CMS 2015, pp 1049–1054

  21. Kuhl F, Weatherly R, Dahmann J (2000) Creating computer simulation systems: an introduction to the high level architecture. Prentice Hall, Upper Saddle River

    MATH  Google Scholar 

  22. McLean C, Riddick F, Lee YT (2016) An architecture and interfaces for distributed manufacturing simulation. Simulation 81:15–32

    Article  Google Scholar 

  23. Bleicher F, Duer F, Leobner I, Kovacic I, Heinzl B, Kastner W (2014) Co-simulation environment for optimizing energy efficiency in production systems. CIRP Annals 63(1):441–444

    Article  Google Scholar 

  24. Schönemann M (2017) Multiscale simulation approach for battery production systems. Springer International Publishing, Cham

    Book  Google Scholar 

  25. Brecher C, Esser M, Witt S (2009) Interaction of manufacturing process and machine tool. CIRP Ann Manuf Technol 58:588–607

    Article  Google Scholar 

  26. Aurich JC, Biermann D, Blum H, Brecher C, Carstensen C, Denkena B, Klocke F, Kröger M, Steinmann P, Weinert K (2009) Modelling and simulation of process: machine interaction in grinding. Prod Eng 3(1):111–120

    Article  Google Scholar 

  27. de Wit A, Van Kulen F (2010) Overview of methods for multi-level and/or multi-disciplinary optimization. In: 51St AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Structures, Structural Dynamics, and Materials and Co-located Conferences, American Institute of Aeronautics and Astronautics

  28. Negri E, Fumagalli L, Macchi M (2017) A review of the roles of digital twin in CPS-based production systems. Procedia Manufacturing 11:939–948

    Article  Google Scholar 

  29. Bauernhansl T, Krüger J, Reinhart G, Schuh G (2016) WGP-Standpunkt Industrie 4.0. https://www.ipa.fraunhofer.de/content/dam/ipa/de/documents/Presse/Presseinformationen/2016/Juni/WGP_Standpunkt_Industrie_40.pdf

  30. Rossmann J, Schluse M, Waspe R, Moshammer R (2011) Simulation in the woods: from remote sensing based data acquisition and processing to various simulation applications. In: Jain S (ed) Proceedings of the 2011 Winter Simulation Conference. IEEE, Piscataway, pp 984–996

  31. Kaigom E, Priggemeyer M, Rossmann J (2014) 3d advanced simulation approach to address energy consumption issues of robot manipulators – an erobotics approach. In: Proceedings for the joint conference of ISR 2014, 45th International Symposium on Robotics, Robotik 2014, 8th German Conference on Robotics. VDE-Verl., Berlin and Offenbach. http://ieeexplore.ieee.org/document/6840208/

  32. Kaufmann D, Roßmann J (2018) Finite element analysis as a key functionality for eRobotics to predict the interdependencies between robot control and structural deformation. In: Schüppstuhl T, Tracht K., Franke J (eds) Tagungsband des 3. Kongresses Montage Handhabung Industrieroboter. Springer, Berlin, pp 103–110

  33. Delbrügger T, Roßmann J (2018) A flexible framework to model and evaluate factory control systems in virtual testbeds. In: Schüppstuhl T, Tracht K., Franke J (eds) Tagungsband des 3. Kongresses Montage Handhabung Industrieroboter. Springer, Berlin, pp 149–157

  34. Duro JA, Yan Y, Purshouse RC, Fleming PJ (2018) Collaborative multi-objective optimization for distributed design of complex products. In: Takadama K, Aguirre H (eds) Proceedings of the Genetic and Evolutionary Computation Conference on - GECCO ’18. ACM Press, New York, pp 625–632

  35. Tappeta RV, Renaud JE (1997) Multiobjective collaborative optimization. J Mech Des 119(3):403

    Article  Google Scholar 

  36. Jackson TL (2006) Hoshin kanri for the lean enterprise: developing competitive capabilities and managing profit. Productivity Press, New York

    Book  Google Scholar 

  37. Oakland J (2011) Leadership and policy deployment: the backbone of tqm. Total Qual Manag Bus Excell 22 (5):517–534

    Article  Google Scholar 

  38. IEC 62714 (2014) Engineering data exchange format for use in industrial automation systems engineering - AutomationML. https://www.iec.ch

  39. Drath R, Luder A, Peschke J, Hundt L (2008) Automation ML - the glue for seamless automation engineering. In: 2008 IEEE Conference on Emerging Technologies & Factory Automation. IEEE, Piscataway, pp 616–623

  40. ISO/PAS 17506:2012 (2012) Industrial automation systems and integration – collada digital asset schema specification for 3d visualization of industrial data. https://www.iso.org/standard/59902.html

  41. IEC 61131-3 (2013) Programmable controllers - part 3: programming languages. https://www.iec.ch

  42. IEC 62424:2016 (2016) Representation of process control engineering - requests in P&I diagrams and data exchange between P&ID tools and PCE-CAE tools. https://www.iec.ch

  43. Wiederkehr P, Siebrecht T (2016) Virtual machining: Capabilities and challenges of process simulations in the aerospace industry. Procedia Manuf 6:80–87

    Article  Google Scholar 

  44. Wirtz A, Meißner M, Wiederkehr P, Myrzik J (2017) Simulation-assisted investigation of the electric power consumption of milling processes and machine tools. Procedia CIRP 67:87–92

    Article  Google Scholar 

  45. Wiederkehr P, Siebrecht T, Potthoff N (2018) Stochastic modeling of grain wear in geometric physically-based grinding simulations. CIRP Ann 67(1):325–328

    Article  Google Scholar 

  46. Tilger M, Siebrecht T, Biermann D (2017) Fundamental investigations of honing processes related to the material removal mechanisms. 7. WGP-Jahreskongress, 5.10.-6.10

  47. Siebrecht T, Odendahl S, Hense R, Kersting P (2014) Interpolation methode for the oscillator-based modeling of workpiece vibrations. In: Proceedings of the 3rd International Conference on Virtual Machining Process Technology

  48. Schmitz TL, Smith KS (2009) Machining dynamics: frequency response to improved productivity. Springer Science+Business Media LLC, New York

    Book  Google Scholar 

  49. Biermann D, Surmann T, Kehl G (2008) Oscillator model of machine tools for the simulation of self excited vibrations in machining processes. In: Denkena B (ed) The 1st International Conference on Process Machine Interactions (PMI 2008), pp 23–29

  50. Kienzle O, Victor H (1952) Die Bestimmung von kräften und Leistungen an spanenden Werkzeugen und Werkzeugmaschinen. VDI-Z 94(11-12):299–305

    Google Scholar 

  51. Wiederkehr P, Siebrecht T, Baumann J, Biermann D (2018) Point-based tool representations for modeling complex tool shapes and runout for the simulation of process forces and chatter vibrations. Adv Manuf 6 (3):301–307

    Article  Google Scholar 

  52. Le CV, Pang C, Gan O (2012) Energy saving and monitoring technologies in manufacturing systems with industrial case studies. In: 7th IEEE Conference on Industrial Electronics and Application (ICIEA), pp 1916–1921

  53. Cataldo A, Taisch M, Stahl B (2013) Modelling, simulation and evaluation of energy consumptions for a manufacturing production line. In: IECON 2013 -39Th Annual Conference of the IEEE Industrial Electronics Society. IEEE, Vienna, pp 7537– 7542

  54. GEPPERT-Band GmbH (2016) Bedienungsanleitung GAL-25. Jülich, Germany, http://www.geppert-band.de, Language: German

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The project was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 276879186.

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Correspondence to Matthias Meißner.

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Delbrügger, T., Meißner, M., Wirtz, A. et al. Multi-level simulation concept for multidisciplinary analysis and optimization of production systems. Int J Adv Manuf Technol 103, 3993–4012 (2019). https://doi.org/10.1007/s00170-019-03722-1

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