Product quality improvement analysis using data mining: a case study in ultra-precision manufacturing industry

Authors:
Hailiang Huang;Dianliang Wu
Affiliations:
School of Information Management, Shanghai University of Finance & Economics, Shanghai, China;School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai, P.R. China
Venue:
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Year:
2005

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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.