Request Driven Generation of RFLP Elements at Product Definition

  • László HorváthEmail author
  • Imre J. Rudas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9520)


Recent achievements in product modeling emphasize self adaptive instancing of generic models, active intelligent property (IP) representations, and multidisciplinary product concept definitions. Although current leading product lifecycle management models (PLM) are in possession of these advanced capabilities, new complexity related problems arise at definition of active knowledge and higher level abstraction model entities. As contribution to solution for the above problems, this paper introduces new request driven behavior centered method for the generation of requirements, functional, logical, and physical (RFLP) structure elements and active knowledge features in PLM model. The requirements, functional, and logical elements provide high level abstraction for representation of multidisciplinary product concept design while knowledge features assist generation of product features on the physical level. The proposed method is aimed to be suitable for intelligent application purposed extension of PLM modeling systems.


Product Model Product Lifecycle Management High Level Abstraction Product Object Product Behavior 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Horváth, L., Rudas, I.J.: Human intent representation in knowledge intensive product model. J. Comput. 4(10), 954–961 (2009)CrossRefGoogle Scholar
  2. 2.
    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
  3. 3.
    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
  4. 4.
    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
  5. 5.
    Horváth, L., Rudas, I.J.: Associativity, adaptivity and behavior aspects in modeling for manufacturing related robot systems. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 3006–3011, Barcelona, Spain (2005)Google Scholar
  6. 6.
    Stark, J.: Product Lifecycle Management: 21st Century Paradigm for Product Realisation. Birkhäuser, Heidelberg (2004)Google Scholar
  7. 7.
    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
  8. 8.
    Horváth, L., Rudas, I.J.: Active knowledge for the situation-driven control of product definition. Acta Polytech. Hung. 10(2), 217–234 (2013)Google Scholar
  9. 9.
    Sy, M., Mascle, C.: Product design analysis based on life cycle features. J. Eng. Des. 22(6), 387–406 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Institute of Applied Mathematics, John Von Neumann Faculty of InformaticsÓbuda UniversityBudapestHungary

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