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Data‐driven variation source identification for manufacturing process using the eigenspace comparison method

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  • Nong Jin
  • Shiyu Zhou

Abstract

Variation reduction of manufacturing processes is an essential objective of process quality improvement. It is highly desirable to develop a methodology of variation source identification that helps quickly identify the variation sources, hence leading to quality improvement and cost reduction in manufacturing systems. This paper presents a variation source identification method based on the analysis of the covariance matrix of process quality measurements. The identification procedure utilizes the fact that the eigenspace of the quality measurement covariance matrix can be decomposed into a subspace due to variation sources and a subspace purely due to system noise. The former subspaces for different samples will be the same if the same variation sources dominate. A testing procedure is presented, which can determine the closeness of the subspaces under sampling uncertainty. A case study is conducted to illustrate the effectiveness of this methodology. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006.

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  • Nong Jin & Shiyu Zhou, 2006. "Data‐driven variation source identification for manufacturing process using the eigenspace comparison method," Naval Research Logistics (NRL), John Wiley & Sons, vol. 53(5), pages 383-396, August.
    • Handle: RePEc:wly:navres:v:53:y:2006:i:5:p:383-396
    DOI: 10.1002/nav.20150
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    References listed on IDEAS

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    1. Michael E. Tipping & Christopher M. Bishop, 1999. "Probabilistic Principal Component Analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(3), pages 611-622.
    2. Robert J. Boik, 2002. "Spectral models for covariance matrices," Biometrika, Biometrika Trust, vol. 89(1), pages 159-182, March.
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