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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)

Abstract

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.

Keywords

Boss design Back-Propagation Network Genetic algorithm knowledge extraction Plastic 

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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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