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Intelligent Content for Product Definition in RFLP Structure

  • László HorváthEmail author
  • Imre J. Rudas
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 513)

Abstract

This paper introduces a new contribution to high level abstraction assisted product definition methodology. The aim is enhanced knowledge representation for high level concept driven definition of multidisciplinary industrial products. The background of the proposed method is product definition in the requirement, functional, logical, and physical (RFLP) structure. This is the basis of four level abstraction based new generation of product lifecycle modeling. The problem to be solved by the proposed method is definition of content, control, and connections of R, F, L, and P elements. Usual dialogues at user surfaces require too complex thinking process which motivated research in intelligent assistance of RFLP element generation at the Laboratory of Intelligent Engineering Systems (LIES), Óbuda University. As preliminary result, abstraction on five levels was conceptualized and published at the LIES for product definition six years ago. The emergence of RFLP structures in leading PLM systems motivated refurbishing this abstraction for the new requirements. The result is the initiative, behavior, context, and action (IBCA) structure which organizes multiple human influence request originated content for the generation of RFLP structure elements and connects request definition with RFLP structure element and conventional feature generation through its four levels. Self adaptive product model concept was extended. Consequently, the IBCA structure driven model reconfigures and updates itself for new situations and events. This paper introduces recent relevant results in human controlled product model development. Following this, changes caused by RFLP structure in PLM model, the IBCA structure and its driving connections, and embedding IBCA structure in PLM model are discussed. Integration of IBCA structure in typical PLM model structure and implementation are issues in the rest of the paper.

Keywords

Product lifecycle management (PLM) Multidisciplinary product definition Adaptive product model Generation of RFLP structure and its elements IBCA structure 

Notes

Acknowledgment

The authors gratefully acknowledge the financial support by the Óbuda University research fund.

References

  1. 1.
    Cross, N.: Expertise in design: an overview. Des Stud 25(5), 427–441 (2004)CrossRefGoogle Scholar
  2. 2.
    Stark, J.: Product Lifecycle Management: 21st Century Paradigm for Product Realisation. Birkhäuser, Heidelberg (2004)Google Scholar
  3. 3.
    Jardim-Goncalves, R., Figay, N., Steiger-Garcao, A.: Enabling interoperability of STEP Application Protocols at meta-data and knowledge level. Int J Technol Manage 36(4), 402–421 (2006)CrossRefGoogle Scholar
  4. 4.
    Kleiner, S., Kramer, C.: Model based design with systems engineering based on RFLP Using V6. In: Abramovici, M., Stark, R. (eds.) Smart Product Engineering. LNPE, pp. 93–102. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  5. 5.
    Horváth, L., Rudas, I.J.: Human intent description in environment adaptive product model objects. J. Adv. Comput. Intell. Intell. Inform. Tokyo 9(4), 415–422 (2005)Google Scholar
  6. 6.
    Horváth, L., Rudas, I.J.: A new method for enhanced information content in product model. WSEAS Trans. Inf. Sci. Appl. 5(3), 277–285 (2008)Google Scholar
  7. 7.
    Horváth, L., Rudas, I.J.: Human intent representation in knowledge intensive product model. J. Comput. 4(10), 954–961 (2009)CrossRefGoogle Scholar
  8. 8.
    Vosgien, T., Nguyen Van, T., Jankovic, M., Eynard, B., Bocquet, J.-C.: Towards simulation-based design in product data management systems. In: Rivest, L., Bouras, A., Louhichi, B. (eds.) PLM 2012. IFIP AICT, vol. 388, pp. 612–622. Springer, Heidelberg (2012)Google Scholar
  9. 9.
    Ambroisine, T.: Mastering increasing product complexity with Collaborative Systems Engineering and PLM. In: Proceedings of the Embedded World Conference, Nürnberg, Germany, pp. 1–8 (2013)Google Scholar
  10. 10.
    Horváth, L., Rudas, I.J.: New approach to knowledge intensive product modeling in PLM systems. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Montreal, Canada, pp. 668–673 (2007)Google Scholar
  11. 11.
    Horváth, L., Rudas, I.J.: Modeling and Problem Solving Methods for Engineers, p. 330. Elsevier, Academic Press, New York (2004)Google Scholar
  12. 12.
    Horváth, L., Rudas, I.J.: Virtual intelligent space for engineers. In: Proceedings of the 31st Annual Conference of IEEE Industrial Electronics Society, pp. 400–405, Raleigh, USA (2005)Google Scholar
  13. 13.
    Horváth, L., Rudas, I.J.: Requested behavior driven control of product definition. In: Proceedings of the 38th Annual Conference on IEEE Industrial Electronics Society, pp. 2821–2826, Montreal, Canada (2012)Google Scholar
  14. 14.
    Horváth, L., Rudas, I.J.: Decision support at a new global level definition of products in PLM systems. In: Precup, R.-E., Kovács, S., Preitl, S., Petriu, E.M. (eds.) Applied Computational Intelligence in Engineering. TIEI, vol. 1, pp. 301–320. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  15. 15.
    Brière-Côté, A., Rivest, L., Desrochers, A.: Adaptive generic product structure modelling for design reuse in engineer-to-order products. Comput. Ind. 61(1), 53–65 (2010)CrossRefGoogle Scholar
  16. 16.
    Choiand, S.H., Cheunga, H.H.: A versatile virtual prototyping system for rapid product development. Comput. Ind. 59(5), 477–488 (2008)CrossRefGoogle Scholar
  17. 17.
    Zadeh, L.A.: Soft computing and fuzzy logic. Software 11(6), 48–56 (1994)CrossRefGoogle Scholar
  18. 18.
    Horváth, L., Rudas., I.J.: Knowledge technology for product modeling. In: Knowledge in Context – Few Faces of thess Knowledge Society, Chapter 5, pp. 113–137. Walters Kluwer (2010)Google Scholar
  19. 19.
    Sy, M., Mascle, C.: Product design analysis based on life cycle features. J. Eng. Des. 22(6), 387–406 (2011)CrossRefGoogle Scholar
  20. 20.
    Horváth, L., Rudas, I.J.: Active knowledge for the situation-driven control of product definition. Acta Polytechnica Hungarica 10(2), 217–234 (2013)Google Scholar
  21. 21.
    Saridakis, K.M., Dentsoras, A.J.: Soft computing in engineering design. A review. Adv. Eng. Inf. 22(2), 202–221 (2008)CrossRefGoogle Scholar
  22. 22.
    Rudas, I.J., Pap, E., Fodor, J.: Information aggregation in intelligent systems: An application oriented approach. Knowl. Based Syst. 38, 3–13 (2013)CrossRefGoogle Scholar
  23. 23.
    Horváth, L., Rudas, I.J.: Towards interacting systems in product lifecycle management. In: Proceedings of the 8th International Conference on System of Systems Engineering (SoSE), pp. 267–272, Maui, Hawaii, USA (2013)Google Scholar
  24. 24.
    Horváth, L., Rudas, I.J., Bitó, J., Hancke, G.: Intelligent computing for the management of changes in industrial engineering modeling processes. Comput. Inform. 24, 549–562 (2005)zbMATHGoogle Scholar
  25. 25.
    Horváth, L., Rudas, I.J.: Emphases on human intent and knowledge in management of changes at modeling of products. WSEAS Trans. Inf. Sci. Appl. 3(9), 1731–1738 (2006)Google Scholar
  26. 26.
    Horváth, L., Rudas, I.J.: New product model representation for decisions in engineering systems. In: Proceedings of 2011 International Conference on System Science and Engineering (ICSSE 2011), pp. 546–551, Macau, China (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.John von Neumann Faculty of Informatics, Institute of Applied MathematicsÓbuda UniversityBudapestHungary

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