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

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.

Keywords:

• dynamic quality control
• principal component analysis
• dominant factors
• incremental weight
• quality prediction

Acknowledgements

The research work presented in this paper is supported by grant number 51275399 from the National Natural Science Foundation of China (NSFC). The authors are grateful to the editor and reviewers for their thoughtful suggestions that improve the quality of the paper.

Additional information

Funding

The research work presented in this paper is supported by the National Natural Science Foundation of China (NSFC) [grant number 51275399].