Plastic Boss Design Using Knowledge Extraction Method

  • Min-Chie ChiuEmail author
  • Tian-Syung Lan
  • Ho-Chih Cheng
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)


Complex product design often relies on design experience and numerous repeated equipment operations. In order to speed up the design process on a complicated product, a methodology of knowledge extraction (KE) is proposed. Moreover, a case study in designing a shaped boss using the KE technique in conjunction with a Back-Propagation Network (BPN) method as well as a genetic algorithm (GA) will be introduced. The results indicate that the prediction error between learned and examined data is found to be within 8%. Moreover, the error between the GA’s solution and the specific target is also found to be within 5%. Therefore, the bi-directional prediction scheme constructed in this project is deemed to be reliable. Consequently, knowledge extraction can provide a rapid and economical way to design and shape a complicated product.


Boss design Back-Propagation Network Genetic algorithm knowledge extraction Plastic 


  1. 1.
    Chuang, S.C.: A research on a corresponding mode between product form and engineering design. Master thesis. National Yunlin University of Science and Technology (2003)Google Scholar
  2. 2.
    GE Plastics Group: GE Plastic Design Guide. GE Plastics, Pittsfield, MA (1997)Google Scholar
  3. 3.
    Carter, S., Kazmer, D.: Studies of Plastic Boss Design and Methodology. University of Massachusetts, Boston (1999)Google Scholar
  4. 4.
    Chen, C.T.: The process parameters optimization and process control for plastic injection molding, Ph.D. thesis. Chung Hua University (2005)Google Scholar
  5. 5.
    Chen, P.C.: Design of uniaxially loaded RC columns using back propagation neural network. Master thesis. National Chung Hsing University (2007)Google Scholar
  6. 6.
    Werbos, P.J.: Back propagation through time: what it does and how to do it. Proc. IEEE 78(10), 1550–1560 (1990)CrossRefGoogle Scholar
  7. 7.
    Parker, D.C.: Separable helper factors support B Cell proliferation and maturation to Ig secretion. J. Immunol. 129(2), 469–474 (1982)Google Scholar
  8. 8.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning Internal Representations by Error Propagation. Bradford Books, Cambridge (1986)zbMATHGoogle Scholar
  9. 9.
    Kondo, T.: The learning algorithms of the GMDH neural network and their application to medical image recognition. In: SICE (1988)Google Scholar
  10. 10.
    Patrikar, A., Provence, J.: Nonlinear system identification and adaptive control using polynomial networks. Math. Comput. Model. 23(1/2), 159–173 (1996)CrossRefGoogle Scholar
  11. 11.
    Wang, C.D., Shiao, D.C.: The Introduction of Neural Network and Fuzzy Control Theory. Open Tech., Taiwan (2002)Google Scholar
  12. 12.
    Yeh, I.C.: The Application and Practice of Neural Network. Zu-Lin, Taiwan (2000)Google Scholar
  13. 13.
    Holland, J.: Adaptation in Natural and Artificial System. University of Michigan Press, Ann Arbor (1975)Google Scholar
  14. 14.
    Jong, D.: An Analysis of the Behavior of a Class of Genetic Adaptive Systems. University of Michigan, Ann Arbor (1975)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Chung Chou University of Science and TechnologyYuanlinTaiwan, R.O.C.
  2. 2.Yu Da University of Science and TechnologyZaoqiao TownshipTaiwan, R.O.C.

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