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Product Quality Improvement Analysis Using Data Mining: A Case Study in Ultra-Precision Manufacturing Industry

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Fuzzy Systems and Knowledge Discovery (FSKD 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3614))

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Abstract

This paper presents an analysis of product quality improvement in ultra-precision manufacturing industry using data mining for developing quality improvement strategies. Based on 11320 ultra-precision optical products that were produced from the study factory during the period of June 1 and August 31, 2004, important factors impacting the product quality were identified via the decision tree method for data mining. Findings showed that the important factors for the percentage of defectives were type of processing chain, precision requirement, product classes, and raw material. The optimum range of target group in production quality indicators was identified from the gains chart.

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References

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Author information

Authors and Affiliations

  1. School of Information Management, Shanghai University of Finance & Economics, Guoding Road 777, 200433, Shanghai, China

    Hailiang Huang

  2. School of Mechanical Engineering, Shanghai Jiaotong University, 200030, Shanghai, P.R. China

    Dianliang Wu

Authors
  1. Hailiang Huang
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  2. Dianliang Wu
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. Honda Research Institute Europe GmbH, Offenbach/Main, Germany

    Yaochu Jin

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© 2005 Springer-Verlag Berlin Heidelberg

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Huang, H., Wu, D. (2005). Product Quality Improvement Analysis Using Data Mining: A Case Study in Ultra-Precision Manufacturing Industry. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_70

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  • DOI: https://doi.org/10.1007/11540007_70

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28331-7

  • Online ISBN: 978-3-540-31828-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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