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The Study of a Knowledge-Based Constraints Network System (KCNS) for Concurrent Engineering

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Advances in Machine Learning and Cybernetics

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3930))

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Abstract

This research article demonstrates the use of a constraints network for modeling the knowledge which is necessary for concurrent product design. A Knowledge-based Constraints Network System (KCNS) has been developed to maintain design consistency and to support the selection of appropriate design parameter intervals. A data-mining algorithm named fuzzy-rough algorithm is developed to acquire the knowledge level constraints from the numerical simulation. The method integrated Case Based Reasoning (CBR) and Rule Based Reasoning (RBR) with interval consistency algorithm is adopted to predict the potential conflicts and to specify the interval of design parameters. The design example of a crank connecting link in a V6 engine shows the validity of the system.

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Authors and Affiliations

  1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P.R. China

    Wei-ming Wang, Jie Hu, Fei Zhou, Da-yong Li, Xiang-jun Fu & Ying-hong Peng

Authors
  1. Wei-ming Wang
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  2. Jie Hu
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  3. Fei Zhou
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  4. Da-yong Li
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  5. Xiang-jun Fu
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  6. Ying-hong Peng
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Editor information

Editors and Affiliations

  1. Department of Computing, Hong Kong Polytechnic University, P.O. Box, Hong Kong, China

    Daniel S. Yeung

  2. School of Creative Media, City University of Hong Kong,, China

    Zhi-Qiang Liu

  3. Department of Mathematics and Computer Science, Hebei University, 071002, Baoding, Hebei, P.R. China

    Xi-Zhao Wang

  4. School of Electrical and Information Engineering, University of Sydney, 2006, NSW, Australia

    Hong Yan

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© 2006 Springer-Verlag Berlin Heidelberg

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Wang, Wm., Hu, J., Zhou, F., Li, Dy., Fu, Xj., Peng, Yh. (2006). The Study of a Knowledge-Based Constraints Network System (KCNS) for Concurrent Engineering. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_28

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  • DOI: https://doi.org/10.1007/11739685_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33584-9

  • Online ISBN: 978-3-540-33585-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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