Knowledge Acquisition from Simulation Data to Product Configuration Rules

Abstract:

Product configuration design and simulation analysis are of critical importance in the rapid response to performance-driven product design. Product configuration design is the transition from customer specifications into physical solutions, while simulation analysis compared with testing result explains that physical solutions are correct and applicable to engineering. Configuration rules play an important role in the product configuration design. Finite element analysis (FEA) has been used widely in the simulation analysis. We could regard the simulation data of FEA as the important knowledge source of configuration rules. Then, a methodology of integration between product configuration design and simulation analysis is proposed to realize the dynamic update of configuration rules. An application of a clamping unit of injection molding machine that illustrates the proposed approach is developed at the end of the paper.

• Periodical: Advanced Materials Research (Volumes 308-310)
• Pages: 77-82
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.308-310.77
• Online since: August 2011
• Authors: Bin Ren, Shu You Zhang
• Keywords: Configuration Rules, Fuzzy Rough Set, Knowledge Acquisition, Product Configuration Design, Simulation Data

References

[1] M.M. Tseng, J.X. Jiao: Computers&Industrial Engineering Vol. 33 (1997), p.319

[2] Z. Chen, L.Y. Wang: Internation Journal of Product Research Vol. 47 (2009), p.5037

[3] Z. Chen, L.Y. Wang: Expert Systems with Applications Vol. 37 (2010), p.383

[4] N. Franke, F. Piller: Journal of Prodct Innovation Management Vol. 21 (2004), p.401

[5] J.L. Yin, D.Y. Li and Y.H. Peng: International Advanced Manufacture Technology Vol. 29 (2006), p.279

[6] U. Fayyad, G. Piatetsky-Shapiro, P. Smyth and R. Uthurusamy, in: Advances in knowledge discovery in databases, MIT Press, Cambridge (1996).

[7] D. Braha, in: Data mining for design and manufacutring: methods and applications, Kluwer, Boston (2001).

[8] R.L. Grossman, in: Data mining for scientific and engineering applications, Kluwer, Bonston (2001).

[9] C. Kamath, in: An Introduction to Scientific Data Mining, edtied by Institute for Pure&Appl. Math., UCLA (2002), in press.

[10] Z. Pawlak, in: Rough Sets: Theoretical aspects of Reasoning about Data, edited by Norwell, MA: Kluwer Academic (1992), in press.

[11] Z. Pawlak: Fuzzy sets and systems Vol 17 (1981), p.99

[12] J.S. Yu, J.P. Gonzalez-Zugasti and K.N. Otto: ASME Journal of Mechanical Design Vol. 121 (1999), p.329

[13] M.M. Tseng, X. Du: CIRP Annals Vol. 47 (1998), p.103

[14] H. Tsai, S. Hsiao:Information sciences Vol. 158 (2004), p.232

[15] Z. Pawlak, A. Skowron: Bull Int Rough set Soc Vol. 3 (2000), p.43