|
Vol: 59(73) No: 2 / December 2014 Method for Knowledge Driven Element Generation on Levels of RFLP Structure László Horváth Institute of Applied Mathematics, Óbuda University, John von Neumann Faculty of Informatics, H-1034, Bécsi u. 96/b, Budapest, Hungary, phone: +(36-1)666-5524, e-mail: horvath.laszlo@nik.uni-obuda.hu, web: http://users.nik.uni-obuda.hu/lhorvath/lhpag27.htm Imre J. Rudas Institute of Applied Mathematics, Óbuda University, John von Neumann Faculty of Informatics, H-1034, Bécsi u. 96/b, Budapest, Hungary, phone: +(36-1) 666-5731, e-mail: rudas@uni-obuda.hu, web: http://www.uni-obuda.hu/rudas/ Keywords: Product lifecycle management, abstraction in product model, knowledge based engineering, RFLP structure, RBAC structure Abstract Lifecycle management of complex product information (PLM) at engineering for complex structures as aircrafts and cars is increasingly relied upon modeling methods from requirements engineering (RE), knowledge engineering (KE) and systems engineering (SE). Recently, very complex product models are organized by the requirement, functional, logical, and physical (RFLP) structure. At the same time, knowledge property (KP) of company is active in generic product models at making model instances. RFLP structure offers suitably high level abstraction for emerging multidisciplinary products. Early recognition of the above trend motivated the authors of this paper to make research in model representation on high level abstraction of product objects. The published results of this research grounded the research for the request, behavior, action, and context (RBAC) structure at the Laboratory of Intelligent Engineering Systems (LIES, Óbuda University. The RBAC is model representation of knowledge based content for RFLP structure elements. This paper introduces state-of-the-art in relevant PLM modeling and explains actual problems and solutions at modeling of complex products. Following this, paper characterizes the RBAC structure and its connections in product model. The next section discusses the knowledge engineering in RBAC structure, as well as the potential of soft computing (SC), and role system of systems engineering (SoSE) in PLM modeling. Finally, implementation of the proposed model is discussed. References [1] J. Stark, Product Lifecycle Management: 21st Century Paradigm for Product Realisation, Birkhäuser, 2004, p.441. [2] S. Kleiner, C. Kramer, “Model Based Design with Systems Engineering Based on RFLP Using V6,” in Smart Product Engineering, Springer, 2013, pp. 93-102. [3] L. A. Zadeh, “Soft computing and fuzzy logic,” Software, Vol.11, No. 6, pp. 48-56, 1994. [4] L. Horváth, I. J. Rudas, “Human Intent Description in Environment Adaptive Product Model Objects,” Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 9, No. 4, pp. 415-422, 2005. [5] L. Horváth, I. J. Rudas, “A New Method for Enhanced Information Content in Product Model,” Transactions on Information Science & Applications, Vol. 5, No. 3, pp. 277-285, 2008. [6] L. Horváth, I. J. Rudas, “Requested Behavior Driven Control of Product Definition,” in Proc. 38th Annual Conference on IEEE Industrial Electronics Society, Montreal, Canada, 2012, pp. 2821-2826. [7] L. Horváth, I. J. Rudas, “Virtual intelligent space for engineers,” ion Proc. 31st Annual Conference of IEEE Industrial Electronics Society, Raleigh, USA, 2005, 400-405. [8] R. Jardim-Goncalves, N. Figay and A. Steiger-Garcao, “Enabling interoperability of STEP Application Protocols at meta-data and knowledge level,” International Journal of Technology Management, Volume 36, Number 4, pp. 402 – 421, 2006. [9] M. Sya, C. Masclea, “Product design analysis based on life cycle features,” Journal of Engineering Design, Vol. 22, No. 6, pp. 387-406, 2011. [10] A. Brière-Côté, L. Rivest, A. Desrochers, Adaptive generic product structure modelling for design reuse in engineer-to-order products,” Computers in Industry, Vol. 61, No. 1, pp. 53–65, 2010. [11] M. Richardson, P. Domingos, “Learning with Knowledge from Multiple Experts,” in proc. of the Twentieth International Conference on Machine Learning, Washington, DC, Morgan Kaufmann, 2003, pp. 624-631. [12] L. Horváth and I. J. Rudas, “Human Intent Representation in Knowledge Intensive Product Model,” Journal of Computers, Vol. 4, No. 9, pp. 954-961, 2009. [13] R. L. Ackoff, “From Data to Wisdom,” Journal of Applied Systems Analysis, Vol. 16, No. 1, pp. 3-9, 1989. [14] L. Horváth, I. J. Rudas, “Adaptive modeling for robot systems based on behaviors and feature driven shape descriptions,” Transactions on Information Science and Application Vol. 11, No 2, pp. 1761-1771, 2005. [15] L. Horváth, I. J. Rudas, “Coordinated and Recorded Human Interactions for Enhanced Intelligence in Product Model,” in Recent Advances in Intelligent Engineering Systems, Berlin, Heidelberg, Springer, 2012, pp. 59-78. [16] L. Horváth, I. J. Rudas, “Adaptive modeling for robot systems based on behaviors and feature driven shape descriptions,” Transactions on Information Science and Application, Vol. 11, No 2, pp. 1761-1771, 2005. [17] L. Horváth, I. J. Rudas, “Towards Interacting Systems in Product Lifecycle Management,” in Proc. 8th International Conference on System of Systems Engineering, Maui, HI, USA, 2013, pp. 267 – 272. [18] L. A. Wojcik, K. C. Hoffman, “Emergent Enterprise Dynamics in Optimal Control Models of the System of Systems Engineering Process,” in Proc. 2007 IEEE International Conference on System of Systems Engineering, San Antonio, TX, USA, 2007, pp. 1-7. [19] V. Ireland, Y. O. Sanches, “Recognising further concepts from complex systems research in SoSE,” in Proc. 8th International Conference on System of Systems Engineering, Maui, HI, USA, 2013, pp. 93-98. [20] K. M. Saridakis, A.J. Dentsoras, “Soft computing in engineering design – A review,” Advanced Engineering Informatics, Vol. 22, No. 2, pp. 202–221, 2008. |