Optimizing the quality control of multivariate processes under an improved Mahalanobis–Taguchi system

Abstract

Quality characteristics in manufacturing are correlated and do not follow a normal distribution. This study proposes a quality control method for multivariate manufacturing processes that are based on an improved Mahalanobis–Taguchi System (IMTS). The MTS has no data distribution assumptions and identifies anomalies through the Mahalanobis distance (MD). However, a covariance distance can consider the correlation between variables. Further, to address the shortcomings of the MTS in feature selection and threshold determination. A joint optimization model is proposed in this paper. Under this approach, the IMTS is employed to perform composite analyses on multiple quality characteristics and reduce dimensionality to identify abnormalities and the key quality characteristics that lead to anomalies. Further, various models are compared to construct the optimal non-parametric prediction models for each key quality characteristic. Finally, a conceptual model of process parameter optimization is proposed, which improves the Taguchi method to obtain the optimal combination of process parameters and their importance ranking, as the basis for process adjustment. By applying the proposed method, results show that the IMTS has an abnormality identification rate of 99.5%, which is higher than other methods such as MTS, support vector machine (SVM), back propagation neural network (BPNN), fast correlation-based filter solution SVM (FCBF-SVM) and sequential backward selection BPNN (SBS-BPNN). The dimensionality reduction rate is 0.5, which is higher than MTS, SVM, BPNN, and SBS-BPNN methods. The random forest (RF) algorithm is used for accurate predictions of all five key quality characteristics, the improved Taguchi method guided adjustments to manufacturing processes objectively, effectively, and economically.

Keywords:

• improved Mahalanobis–Taguchi system
• improved Taguchi method
• multivariate manufacturing process
• quality control
• random forest

Additional information

Funding

This work was supported by the National Social Science Foundation of China under Grant 18BJY033.

Notes on contributors

Yefang Sun

Yefang Sun is currently pursuing a master’s degree at China Jiliang University. Her main research interests are Mahalanobis-Taguchi system, quality management, and machine learning.

Ijaz Younis

Ijaz Younis joined the School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, PR China, in 2018 as a Research Scientist (academic). In April 2020, he was awarded Elite NMG Scholar Foreign Young Scientist by the Jiangsu Education Department. He is serving as visiting lecturer in Nanjing University of Science and Technology, Nanjing 210094, PR China. He has an academic background in Mahalanobis-Taguchi system, quality management, combined with practical work experience in machine learning.

Yueyi Zhang

Yueyi Zhang received a PhD degree from Nanjing University of Science and Technology in 2010. He is currently a professor and supervisor of postgraduate in the School of Economics and Management of China Jiliang University. His main research fields are quality management and Mahalanobis-Taguchi system.

Hui Zhou

Hui Zhou is currently pursuing a master’s degree at China Jiliang University. Her main research interests are quality management and performance evaluation.