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A dynamic quality control approach by improving dominant factors based on improved principal component analysis

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  • Guangzhou Diao
  • Liping Zhao
  • Yiyong Yao

Abstract

Process variables in manufacturing process are critical to the final quality of product, especially in continuous process. Their abnormal fluctuations may cause many quality problems and lead to poor product quality. Against this background, this paper proposes a dynamic quality control approach by improving dominant factors (DFs) based on improved principal component analysis (iPCA). Firstly, the generation of iPCA is illustrated to identify the DFs which lead to quality problems. Then, a quality prediction model for improving DFs is proposed based on modified support vector machine (SVM). An incremental weight is introduced in SVM to improve its sparsity and increase the accuracy of quality prediction. Thus, the product quality can be guaranteed by controlling the DFs dynamically. Finally, a case study is provided to verify the feasibility and applicability of proposed method. The research is expected to provide some guidance for continuous process.

Suggested Citation

  • Guangzhou Diao & Liping Zhao & Yiyong Yao, 2015. "A dynamic quality control approach by improving dominant factors based on improved principal component analysis," International Journal of Production Research, Taylor & Francis Journals, vol. 53(14), pages 4287-4303, July.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:14:p:4287-4303
    DOI: 10.1080/00207543.2014.997400
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    File URL: http://hdl.handle.net/10.1080/00207543.2014.997400
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    Cited by:

    1. Yuehjen E. Shao & Yu-Ting Hu, 2020. "Using Machine Learning Classifiers to Recognize the Mixture Control Chart Patterns for a Multiple-Input Multiple-Output Process," Mathematics, MDPI, vol. 8(1), pages 1-14, January.
    2. Sheng Hu & Liping Zhao & Yiyong Yao & Rushan Dou, 2016. "A variance change point estimation method based on intelligent ensemble model for quality fluctuation analysis," International Journal of Production Research, Taylor & Francis Journals, vol. 54(19), pages 5783-5797, October.

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