Towards Knowledge Driven Adaptive Product Representations

  • László HorváthEmail author
Part of the Topics in Intelligent Engineering and Informatics book series (TIEI, volume 8)


Highly integrated engineering models are developed for lifecycle of industrial products towards increased self-development capabilities. Adaptive model has the capability to change itself in accordance with changed circumstances and events. Knowledge content in product model gives this capability for the modification of affected model entities as a result of changed parameters in the modeled product objects or the environment of the modeled product. This capability of the model is based among others on the well proven feature principle. According to this principle, product model is developed in the course of a series modification feature definitions. Modification by a feature is propagated through contextual connection chains of parameters of the modifying and modified features. Considering the recent development history of product modeling, product object feature driven models have been developed towards knowledge feature driven models where product object representations are generated and modified by active knowledge features. At the same time, lifecycle models of products are more and more interdisciplinary where product objects from mechanical, electrical, electronic, computer and other areas of engineering are included in a single model and handled by the same mechanism of modeling. However, interdisciplinary product model needs representations on higher abstraction levels than product object features. This chapter introduces some works and results from recent research activities contributing to the above sketched development history. It starts with scenario of current product modeling and the self-development capability of product models. Following this, the concept of product model affect zone and method to organize context definitions are proposed. The main contribution in the chapter includes introduction of different concepts for the abstraction levels in product model as well as abstraction levels and the related knowledge representations in the proposed coordinated request driven product model (CRPM). Finally, possible integration of the CRPM method into PLM systems is discussed.


Product lifecycle management (PLM) feature driven product model adaptive product representations abstraction levels in product model 


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  1. 1.
    Zadeh, L.A.: Soft computing and fuzzy logic. Software 11(6), 48–56 (1994)CrossRefGoogle Scholar
  2. 2.
    Horváth, L., Rudas, I.J.: Modeling and Problem Solving Methods for Engineers. Elsevier, Academic Press (2004)Google Scholar
  3. 3.
    Stark, J.: Product Lifecycle Management: 21st Century Paradigm for Product Realisation. Birkhäuser (2004)Google Scholar
  4. 4.
    Horváth, L., Rudas, I.J.: Knowledge Technology for Product Modeling. In: Knowledge in Context – Few Faces of the Knowledge Society, ch. 5, pp. 113–137. Walters Kluwer (2010)Google Scholar
  5. 5.
    Jardim-Goncalves, R., Figay, N., Steiger-Garcao, A.: Enabling interoperability of STEP Application Protocols at meta-data and knowledge level. International Journal of Technology Management 36(4), 402–421 (2006)CrossRefGoogle Scholar
  6. 6.
    Sy, M., Mascle, C.: Product design analysis based on life cycle features. Journal of Engineering Design 22(6), 387–406 (2011)CrossRefGoogle Scholar
  7. 7.
    Horváth, L.: A New Method for Enhanced Information Content in Product Model. WSEAS Transactions on Information Science and Applications 5(3), 277–285 (2008)Google Scholar
  8. 8.
    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
  9. 9.
    Saridakis, K.M., Dentsoras, A.J.: Soft computing in engineering design. A review. Advanced Engineering Informatics 22(2), 202–221 (2008)CrossRefGoogle Scholar
  10. 10.
    Clark, J.O.: System of Systems Engineering and Family of Systems Engineering From a Standards Perspective. In: Proc. of the Third IEEE SMC International Conference on System of Systems Engineering (SoSE), Monterey, California, USA, pp. 1–6 (2008)Google Scholar
  11. 11.
    Horváth, L., Rudas, I.J.: Requested Behavior Driven Control of Product Definition. In: Proc. of the 38th Annual Conference on IEEE Industrial Electronics Society, Montreal, Canada, pp. 2821–2826 (2012)Google Scholar
  12. 12.
    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, vol. 5, pp. 93–102. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  13. 13.
    Rudas, I.J., Fodor, J.: Information Aggregation in Intelligent Systems Using Generalized Operators. International Journal of Computers Communications & Control 1(1), 47–57 (2006)Google Scholar
  14. 14.
    Rudas, I.J., Fodor, J.: Non-conventional Interpretation of Fuzzy Connectives. In: Proc. of the 14th WSEAS International Conference on Applied Mathematics, Puerto de la Cruz, Spain, pp. 294–299 (2009)Google Scholar
  15. 15.
    Rudas, I.J., Pap, E., Fodor, J.: Information aggregation in intelligent systems: An application oriented approach. Knowledge-Based Systems 38, 3–13 (2013)CrossRefGoogle Scholar
  16. 16.
    Horváth, L., Rudas, I.J.: Processes for Improved Human Control over Definition of Product Models. In: Proc. of the 35th Annual Conference of the IEEE Industrial Electronics Society, Porto, Portugal, pp. 2491–2496 (2009)Google Scholar
  17. 17.
    Horváth, L., Rudas, I.J.: Towards interacting systems in product lifecycle management. In: Proc. of the 8th International Conference on System of Systems Engineering (SoSE), Maui, Hawaii, USA, pp. 267–272 (2013)Google Scholar
  18. 18.
    Horváth, L., Rudas, I.J.: A Machine Learning Based Approach to Manufacturing Process Planning. In: Proc. of the IEEE International Symposium on Industrial Electronics (ISIE 1993), Budapest, pp. 429–433 (1993)Google Scholar
  19. 19.
    Horváth, L., Rudas, I.J.: Human-Computer Interactions at Decision Making and Knowledge Acquisition in Computer Aided Process Planning Systems. In: Proc. of the 1994 IEEE International Conference on Systems, Man and Cybernetics, San Antonio, Texas, USA, pp. 1415–1419 (1994)Google Scholar
  20. 20.
    Horváth, L., Rudas, I.J.: Multilevel Modeling of Manufacturing Processes Using Object Oriented Petri Nets and Advanced Knowledge Representation. In: Proc. of the 1995 IEEE 21st International Conference on Industrial Electronics, Control, and Instrumentation (IECON 1995), Orlando, Florida, USA, pp. 133–137 (1995)Google Scholar
  21. 21.
    Horváth, L., Rudas, I.J.: Knowledge Based Generation of Petri Net Representation of Manufacturing Process Model Entities. In: Proc. of the 1996 IEEE International Conference on Systems, Man and Cybernetics, Beijing, China, pp. 2957–2962 (1996)Google Scholar
  22. 22.
    Rudas, I.J., Horváth, L.: Modeling of Manufacturing Processes Using Petri Net Representation. Engineering Applications of Artificial Intelligence 10(3), 243–255 (1997)CrossRefGoogle Scholar
  23. 23.
    Rudas, I.J., Horváth, L.: Intelligent Computer Methods for Modeling of Manufacturing Processes and Human Intent. Journal of Advanced Computational Intelligence and Intelligent Informatics 2(3), 111–119 (1998)Google Scholar
  24. 24.
    Horváth, L., Rudas, I.J.: Modeling of Manufacturing Processes in Simultaneous Engineering Using Collaborative Methods and Tools. In: Simultaneous Engineering: Methodologies and Applications (Automation and Production Systems), pp. 321–357. Gordon and Breach Science Publisher, New York (1999)Google Scholar
  25. 25.
    Horváth, L., Rudas, I.J., Hancke, G.: Associative Modeling of Machining Processes Using Feature Based Solid Part Models. In: Proc. of the 2006 26th Annual Conference of the IEEE Industrial Electronics Society, Nagoya, Japan, pp. 1267–1273 (2000)Google Scholar
  26. 26.
    Horváth, L., Rudas, I.J.: Virtual technology based associative integration of modeling of mechanical parts. Journal of Advanced Computational Intelligence and Intelligent Informatics 5(5), 269–278 (2001)Google Scholar
  27. 27.
    Horváth, L., Rudas, I.J., Bitó, J.F.: Form Feature Based Generation of Robot Assembly Paths for Product Variants. In: Proc. of the 202 IEEE Conference on Industrial Technology, Bangkok, Thailand, pp. 181–186 (2002)Google Scholar
  28. 28.
    Horváth, L., Rudas, I.J., Tzafestas, S.G.: Relating Shape and Robot Process Model Features. International Journal of Mechanics and Control 4(2), 27–31 (2003)Google Scholar
  29. 29.
    Horváth, L., Rudas, I.J., Bitó, J., Hancke, G.: Intelligent Computing for the Management of Changes in Industrial Engineering Modeling Processes. Computing and Informatics 24, 549–562 (2005)zbMATHGoogle Scholar
  30. 30.
    Horváth, L., Rudas, I.J.: Emphases on human intent and knowledge in management of changes at modeling of products. WSEAS Transactions on Information Science and Application 3(9), 1731–1738 (2006)Google Scholar
  31. 31.
    Horváth, L., Rudas, I.J.: New approach to knowledge intensive product modeling in PLM systems. In: Proc. of the IEEE International Conference on Systems, Man and Cybernetics, Montreal, Canada, pp. 668–673 (2007)Google Scholar
  32. 32.
    Horváth, L., Rudas, I.J.: New Product Model Representation for Decisions in Engineering Systems. In: Proc. of the 2011 International Conference on System Science and Engineering (ICSSE, Macau, China, pp. 546–551 (2011)Google Scholar

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© Springer International Publishing Switzerland 2014

Authors and Affiliations

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

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