Deep multistage multi-task learning for quality prediction of multistage manufacturing systems

• Published in: Journal of quality technology Vol. 53; no. 5; pp. 526 - 544
• Main Authors: Yan, Hao, Sergin, Nurettin Dorukhan, Brenneman, William A., Lange, Stephen Joseph, Ba, Shan
• Format: Journal Article
• Language: English
• Published: Taylor & Francis 20.10.2021
• Subjects: deep neural network; multi-task learning; multistage manufacturing process; quality prediction
• ISSN: 0022-4065; 2575-6230
• DOI: 10.1080/00224065.2021.1903822

In multistage manufacturing systems, modeling multiple quality indices based on the process sensing variables is important. However, the classic modeling technique predicts each quality variable one at a time, which fails to consider the correlation within or between stages. We propose a deep multistage multi-task learning framework to jointly predict all output sensing variables in a unified end-to-end learning framework according to the sequential system architecture in the MMS. Our numerical studies and real case study have shown that the new model has a superior performance compared to many benchmark methods as well as great interpretability through developed variable selection techniques.