Skip to main content
Springer Nature Link
Log in

Collaborative Design Optimization Based on Knowledge Discovery from Simulation

  • Conference paper
MICAI 2006: Advances in Artificial Intelligence (MICAI 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4293))

Included in the following conference series:

  • 1041 Accesses

Abstract

This paper presents a method of collaborative design optimization based on knowledge discovery. Firstly, a knowledge discovery approach based on simulation data is presented. Rules are extracted by knowledge discovery algorithm, and each rule is divided into several intervals. Secondly, a collaborative optimization model is established. In the model, the consistency intervals are derived from intervals of knowledge discovery. The model is resolved by genetic arithmetic. Finally, The method is demonstrated by a parameter design problem of piston-connecting mechanism of automotive engine. The proposed method can improve the robustness of collaborative design optimization.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Liau, J., Young, R.E., O’Grady, P.: An interactive constraint modeling system for concurrent engineering. Engineering Design and Automation 1(2), 105–119 (1996)

    Google Scholar 

  2. Oh, J.S., O’Grady, P., Young, R.E.: An artificial intelligence constraint network approach to design for assembly. IIE Transactions 27(1), 72–80 (1995)

    Article  Google Scholar 

  3. Young, R.E., Greef, A., O’Grady, P.: An artificial intelligence-based constraint network system for concurrent engineering. International Journal of Production Research 30(7), 1715–1735 (1992)

    Article  Google Scholar 

  4. Fohn, S., Greef, A., Young, R.E., O’Grady, P.: A constraint-system shell to support concurrent engineering approaches to design. Artificial Intelligence in Engineering 9, 1–17 (1994)

    Article  Google Scholar 

  5. Liau, J., Young, R.E., O’Grady, P.: Combining process planning and concurrent engineering to support printed circuit board assembly. Computers and Industrial Engineering 28(3), 615–629 (1995)

    Article  Google Scholar 

  6. Kong, S.H., Noh, S.D., Han, Y.G., Kim, G., Lee, K.I.: Internet-based collaboration system: Press-die design process for automobile manufacturer. International Journal of Advanced Manufacturing Technology 20(9), 701–708 (2002)

    Article  Google Scholar 

  7. Yin, G.F., Tian, G.Y., Taylor, D.: A web-based remote cooperative design for spatial cam mechanisms. International Journal of Advanced Manufacturing Technology 20(8), 557–563 (2002)

    Article  Google Scholar 

  8. Wang, Y.D., Shen, W.M., Ghenniwa, H.: A web/agent-based multidisciplinary design optimization environment. Computers in Industry 52(1), 17–28 (2003)

    Article  Google Scholar 

  9. Gantois, K., Morris, A.J.: The multi-disciplinary design of a large-scale civil aircraft wing taking account of manufacturing costs. Structural and Multidisciplinary Optimization 28(1), 31–46 (2004)

    Article  Google Scholar 

  10. Giassi, A., Bennis, F., Maisonneuve, J.J.: Multidisciplinary design optimisation and robust design approaches applied to concurrent design. Structural and Multidisciplinary Optimization 28(5), 356–371 (2004)

    Article  Google Scholar 

  11. Bai, Y.W., Chen, Z.N., Bin, H.Z., Hu, J.: Collaborative design in product development based on product layout model. Robotics and Computer-Integrated Manufacturing 21(1), 55–65 (2005)

    Article  Google Scholar 

  12. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R.: Advances in Knowledge Discovery in Databases. MIT Press, Cambridge (1996)

    Google Scholar 

  13. Braha, D.: Data Mining for Design and Manufacturing: Methods and Applications. Kluwer Academic Publishers, Dordrecht (2001)

    MATH  Google Scholar 

  14. Robert, L.G.: Data Mining for Scientific and Engineering Applications. Kluwer Academic Publishers, Dordrecht (2001)

    MATH  Google Scholar 

  15. Jiawei, H., Micheline, K.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2001)

    Google Scholar 

  16. Fayyad, U., Irani, K.: Multi-interval discretization of continuous-valued attributes for classification learning. In: Proceedings of IJCAI 1993, 13th International Joint Conference on Artificial Intelligence, Sidney, AU, pp. 1022–1027 (1993)

    Google Scholar 

  17. Nguyen, S.H., Nguyen, H.S.: Some efficient algorithms for rough set methods. In: Proceedings of the Sixth International Conference, Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 1996), Granada, Spain, July 1-5, 1996, pp. 1451–1456 (1996)

    Google Scholar 

  18. Zadeh, L.A.: Fuzzy Sets. Information and Control 8, 338–353 (1965)

    Article  MATH  MathSciNet  Google Scholar 

  19. Pawlak, Z.: Rough Set Rudiments. Bulletin of the International Rough Set Society 3(4), 181–186 (1999)

    MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Knowledge Based Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, 1954 Hua Shan Road, Shanghai, 200030, P.R. China

    Jie Hu & Yinghong Peng

Authors
  1. Jie Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yinghong Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Center for Computing Research, National Polytechnic Institute, 07738, Mexico City, México

    Alexander Gelbukh

  2. Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Luis Enrique Erro No. 1, Sta. Ma. Tonanzintla, 72840, Puebla, México

    Carlos Alberto Reyes-Garcia

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Hu, J., Peng, Y. (2006). Collaborative Design Optimization Based on Knowledge Discovery from Simulation. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_63

Download citation

  • DOI: https://doi.org/10.1007/11925231_63

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-49026-5

  • Online ISBN: 978-3-540-49058-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics