Advertisement

Recent Contribution to Computer Representation of Cyber Physical System for Changed Style of Engineer Cooperation

  • László HorváthEmail author
Chapter
Part of the Topics in Intelligent Engineering and Informatics book series (TIEI, volume 14)

Abstract

This chapter is about recent contribution to model of industrial engineering products which are operated by cooperating systems. Advanced systems operated products use organized cyber units and intelligent sensor networks to control physical units and are referred as cyber physical systems (CPSs). Modeling of CPS is challenging especially because a single model system serves all engineering activities for innovation cycle and lifecycle of a generic product. Purpose of engineering structure can be arbitrary in the advanced industrial practice. In this context, product may serve planned experiments and engineering solution development beyond its production and application. Product model is a system itself and fulfill requirements of theoretically and methodically backgrounded and at the same time experience validated achievements. This model system represents CPS and related production activities, system and resources. Beyond representation and verification of CPS in virtual, new area of research is contextual connections between product model and two physically existing CPSs. These CPSs are the installed product and the system for its production. This chapter introduces recent research results in CPS related engineering model system considering its above advanced features. Main issues are way to system representation in engineering model, extended model of systems operated industrial product, structured driving content model (DCM), integration of engineering related activities around common model system, and outside contexts of DCM.

References

  1. 1.
    A. Brière-Côté, L. Rivest, A. Desrochers, Adaptive generic product structure modelling for design reuse in engineer-to-order products. Comput. Ind. 61(1), 53–65 (2010)CrossRefGoogle Scholar
  2. 2.
    L. Horváth, Towards knowledge driven adaptive product representations, in Advances in Book Soft Computing, Intelligent Robotics and Control (Springer, Heidelberg, London, New York, 2014), pp. 191–209Google Scholar
  3. 3.
    L. Horváth, I.J. Rudas, Information content driven model for virtual engineering space. Acta Polytech. Hung. 15(2), 7–32 (2018)Google Scholar
  4. 4.
    M. Sy, C. Mascle, Product design analysis based on life cycle features. J. Eng. Des. 22(6), 387–406 (2011)CrossRefGoogle Scholar
  5. 5.
    R. Jardim-Goncalves, N. Figay, A. Steiger-Garcao, Enabling interoperability of STEP Application Protocols at meta-data and knowledge level. Int. J. Technol. Manag. 36(4), 402–421 (2006)CrossRefGoogle Scholar
  6. 6.
    L. Horváth, I.J. Rudas, Procedures for generating and evaluation of generic manufacturing process model entities, in Proceedings of the 1997 IEEE International Conference on Systems, Man and Cybernetics, Orlando, Florida, USA (1997), pp. 565–570Google Scholar
  7. 7.
    L. Horváth, I.J. Rudas, Modeling of manufacturing processes in simultaneous engineering using collaborative methods and tools, in Simultaneous Engineering: Methodologies and Applications (Automation and Production Systems) (Gordon and Breach Science Publisher, New York, 1999), pp. 321–357Google Scholar
  8. 8.
    M. Maksimovic, A. Al-Ashaab, E. Shehab, M. Flores, P. Ewers, B. Haque, R. Furian, F. von Lacroix, R. Sulowski, Industrial challenges in managing product development knowledge. Knowl. Based Syst. 71, 101–113 (2014)CrossRefGoogle Scholar
  9. 9.
    L. Horváth, I.J. Rudas, Modeling and Problem Solving Methods for Engineers (Elsevier, Academic Press, New York, 2004)Google Scholar
  10. 10.
    L. Horváth, I. J. Rudas, Possibilities for application of associative objects with built-in intelligence in engineering modeling. J. Adv. Comput. Intell. Intell. Inform. 8(5), 544–551 (2004) (Tokyo)Google Scholar
  11. 11.
    J. Stark, Product Lifecycle Management: 21st Century Paradigm for Product Realisation (Birkhäuser, 2004)Google Scholar
  12. 12.
    J. Gubbia, R. Buyyab, S. Marusica, M. Palaniswamia, Internet of Things (IoT): a vision, architectural elements, and future directions. Future Gener. Comput. Syst. 29(7), 1645–1660 (2013)CrossRefGoogle Scholar
  13. 13.
    S. Kleiner, C. Kramer, Model based design with systems engineering based on RFLP using V6, in Smart Product Engineering (Springer, 2013), pp. 93–102Google Scholar
  14. 14.
    A Canedo, E Schwarzenbach, E.M.A. Al Faruque, Context-sensitive synthesis of executable functional models of cyber-physical systems, in Proceedings of the 2013 ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS), Philadelphia, PA, USA (2013), pp. 99–108Google Scholar
  15. 15.
    L. Horváth, I.J. Rudas, Processes in virtual engineering spaces, in Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics, San Antonio, Texas, USA (2009), pp. 2179–2184Google Scholar
  16. 16.
    L. Horváth, I.J. Rudas, Virtual intelligent space for engineers, in Proceedings of the 31st Annual Conference of IEEE Industrial Electronics Society, Raleigh, USA (2005), pp. 400–405Google Scholar
  17. 17.
    L. Horváth, I.J Rudas, Intelligent content for product definition in RFLP structure, in Intelligent Software Methodologies, Tools and Techniques (Springer, Amsterdam, 2015), pp. 55–70Google Scholar
  18. 18.
    G. Beier, A. Figge, R. Müller, U. Rothenburg, R. Stark, Supporting product development through cross-information content dependency-modeling—novel approaches for traceability-usage. Lect. Notes Inf. Theory 1(1), 21–28 (2013)CrossRefGoogle Scholar
  19. 19.
    P. Leitaoa, A.W. Colomboc, S. Karnouskose, Industrial automation based on cyber-physical systems technologies: prototype implementations and challenges. Comput. Ind. 81, 11–25 (2016)CrossRefGoogle Scholar
  20. 20.
    L. Horváth, Intelligent property support for cyber-physical product system modeling, in Proceedings of the 43rd Annual Conference of the IEEE Industrial Electronics Society (IECON 2017), Beijing, China (2017), pp. 3474–3479Google Scholar
  21. 21.
    L. Horváth, Model mediated university course in engineering, in Proceedings of the 10th International Conference on Computer Supported Education, vol. 1 (Scitepress, Funchal, Portugal, 2018), pp. 481–488Google Scholar
  22. 22.
    K. Baughey, Functional and logical structures: a systems engineering approach, in SAE 2011 World Congress, SAE Technical Paper 2011-01-0517 (2011)Google Scholar
  23. 23.
    T. Ambroisine, Mastering increasing product complexity with Collaborative Systems Engineering and PLM, in Proceedings of the Embedded World Conference, Nürnberg, Germany (2013), pp. 1–8Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Doctoral School of Applied Informatics and Applied MathematicsInstitute of Applied Mathematics, Óbuda UniversityBudapestHungary

Personalised recommendations