A multisource domain adaptation method for quality prediction in small-batch production systems

Abstract

Quality prediction for small-batch production processes is a complex problem due to limitations in available training samples. In this study, a multisource domain adaptation joint-Y partial least square (PLS) method is proposed to learn the similarities between domains and use them to construct a quality prediction model. Without constraints on the number of source and target domains, the proposed method can transfer more historical information for the in-operation process than traditional methods. Numerical experiments and a real-world case study of quality prediction in computer wafer production are performed to verify the effectiveness of the proposed method. The results show that the prediction accuracy of the proposed method is high in cases with few training samples in the target domain compared to the accuracies of the joint-Y PLS model and the traditional PLS model.

KEYWORDS:

• Quality prediction
• small-batch process
• domain adaptation
• partial least square
• multitask learning

Acknowledgements

The authors thank the editor and referees for helping to improve this article.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data is not available due to commercial restrictions. Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.

Additional information

Funding

This work was supported by National Natural Science Foundation of China [grant numbers 71932006 and 71731008 ].

Notes on contributors

Dengyu Li

Dengyu Li is currently a PhD student at Department of Industrial Engineering, Tsinghua University. He received his B.S. degree in Industrial Engineering from Tsinghua University in 2020. His research focuses on data-driven methods for quality prediction and control.

Kaibo Wang

Kaibo Wang is a professor in the Department of Industrial Engineering, Tsinghua University, Beijing, China. He received his BS and MS degrees in Mechatronics from Xi'an Jiaotong University, Xi'an, China, and his PhD in Industrial Engineering and Engineering Management from the Hong Kong University of Science and Technology, Hong Kong. His research focuses on statistical quality control and data-driven system modelling, monitoring, diagnosis, and control, with a special emphasis on the integration of engineering knowledge and statistical theories for solving problems from the real industry.