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Response Behavior Model for Process Deviations in Cyber-Physical Production Systems

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Transactions on Engineering Technologies (WCECS 2015)

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Abstract

Cyber-physical production systems are highly flexible systems that enable adaptive production processes. In these systems, all participants of the production process possess individual information about themselves and are equipped with sensors, actors, and communication interfaces. They can interact with each other and autonomously develop and execute process relevant decisions. For each component, a standard process sequence and alternative process sequences are defined. If a deviation in the standard process occurs during the production of an individual component, the process participants can interact with each other and autonomously define an appropriate response strategy and execute it by using actors of the participants and the intralogistics. Regardless of the deviation, the manufacturing and assembly process of individual components in cyber-physical production systems can still proceed. For this purpose, process deviations and the response behavior of cyber-physical production systems are analyzed, modeled, and simulated, to illustrate the benefits of cyber-physical production systems and to develop a process deviation management system for actual, physical production systems based on cyber-physical systems.

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Notes

  1. 1.

    More information about Industrie 4.0: www.plattform-i40.de.

  2. 2.

    More information about the IIC: www.iiconsortium.org.

References

  1. BMBF: Zukunftsbild Industrie 4.0. http://www.bmbf.de/pubRD/Zukunftsbild_Industrie_40.pdf

  2. Galaske N, Strang D, Anderl R (2015) Process deviations in cyber-physical production systems. Lecture Notes in Engineering and Computer Science: Proceedings of The World Congress on Engineering and Computer Science 2015, WCECS 2015, 21–23 Oct 2015, San Francisco, USA, pp 1035–1040

    Google Scholar 

  3. Lee EA (2010) CPS foundations. In: Design automation conference (ACM), 737–742 (2010)

    Google Scholar 

  4. acatech: Cyber-physical systems. Driving force for innovation in mobility, health, energy and production. Munich (2011)

    Google Scholar 

  5. Anderl R, Strang D, Picard A, Christ A (2014) Integriertes Bauteildatenmodell für Industrie 4.0. Informationsträger für cyber-physische Produktionssysteme. ZWF 109, pp 64–69

    Google Scholar 

  6. Bauernhansl T, ten Hompel M, Vogel-Heuser B (eds) (2014) Industrie 4.0 in Produktion, Automatisierung und Logistik. Anwendung, Technologien und Migration. Springer Vieweg, Wiesbaden

    Google Scholar 

  7. Kagermann H, Wahlster W, Helbig J (2013) Recommendations for implementing the strategic initiative Industrie 4.0. Securing the future of German manufacturing industry (2013)

    Google Scholar 

  8. Anderl R, Picard A, Albrecht K (2013) Smart Engineering for Smart Products. In: Abramovici M, Stark R (eds) Smart Product Engineering. Proceedings of the 23rd CIRP Design Conference, Bochum, Germany, March 11th–13th, 2013, pp. 1–10. Springer, Berlin, Heidelberg (2013)

    Google Scholar 

  9. Radziwon A, Bilberg A, Bogers M, Madsen ES (2014) The Smart Factory. Exploring adaptive and flexible manufacturing solutions. Procedia Engineering 69:1184–1190

    Article  Google Scholar 

  10. Strang D (2016) Kommunikationsgesteuerte cyber-physische Montagemodelle. Shaker, Aachen

    Google Scholar 

  11. VDI 3633-1: Simulation of systems in materials handling, logistics and production. Fundamentals. VDI, Düsseldorf (2010)

    Google Scholar 

  12. Strang D, Anderl R (2014) Assembly Process driven Component Data Model in Cyber-Physical Production Systems. Lecture Notes in Engineering and Computer Science: Proceedings of The World Congress on Engineering and Computer Science 2014, WCECS 2014, 22–24 Oct 2014, San Francisco, USA, pp 947–952 (2014)

    Google Scholar 

  13. Object Management Group (OMG): Unified Modeling Language (OMG UML), Superstructure. Version 2.4.1 (2011)

    Google Scholar 

  14. Weilkiens T (2007) Systems engineering with SysML/UML. Modeling, analysis, design. Morgan Kaufmann, Burlington, Mass

    Google Scholar 

  15. Miles R, Hamilton K (2006) Learning UML 2.0. O’Reilly, Sebastopol, CA

    Google Scholar 

  16. Picard A (2015) Integriertes Werkstückinformationsmodell zur Ausprägung werkstückindividueller Fertigungszustände. Shaker, Aachen

    Google Scholar 

  17. Picard A, Anderl R (2014) Integrated component data model for smart production planning. In: Schützer K (ed) Proceedings of the 19th International Seminar on High Technology. Piracicaba, Sao Paulo, Brazil

    Google Scholar 

  18. Mattern F, Mehl H (1989) Diskrete Simulation - Prinzipien und Probleme der Effizienzsteigerung durch Parallelisierung. Informatik Spektrum 12:198–210

    Google Scholar 

  19. Galaske N, Anderl R (2016) Disruption management for resilient processes in cyber-physical production systems. Procedia CIRP 50:442–447

    Google Scholar 

  20. REFA: Methodenlehre der Betriebsorganisation. Hanser, München (1991)

    Google Scholar 

  21. Heil M (1995) Entstörung betrieblicher Abläufe. Deutscher Universitätsverlag, Wiesbaden

    Book  Google Scholar 

  22. Ingemansson A, Bolmsjö GS (2004) Improved efficiency with production disturbance reduction in manufacturing systems based on discrete-event simulation. J Manuf Technol Manage 15:267–279

    Article  Google Scholar 

  23. Knüppel K, Meyer G, Nyhuis P (2014) A universal approach to categorize failures in production. waset.org (eds) Int J Mech Aerosp Ind Mechatron Eng 8:24–28

    Google Scholar 

  24. Meyer G, Knüppel K, Busch J, Jakob M, Nyhuis P (2013) Effizientes Störgrößenmanagement. Ansatz zur Kategorisierung von Störgrößen in der Produktion. Prod Manag 18:49–52

    Google Scholar 

  25. Kamiske GF, Brauer J-P (2008) Qualitätsmanagement von A bis Z. Erläuterungen moderner Begriffe des Qualitätsmanagements. Hanser, München

    Book  Google Scholar 

  26. Hoover SV, Perry RF (1989) Simulation. A problem-solving approach, Addison-Wesley, Reading, Mass

    Google Scholar 

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Correspondence to Nadia Galaske .

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Galaske, N., Strang, D., Anderl, R. (2017). Response Behavior Model for Process Deviations in Cyber-Physical Production Systems. In: Ao, SI., Kim, H., Amouzegar, M. (eds) Transactions on Engineering Technologies. WCECS 2015. Springer, Singapore. https://doi.org/10.1007/978-981-10-2717-8_31

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  • DOI: https://doi.org/10.1007/978-981-10-2717-8_31

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