Deep Multistage Multi-Task Learning for Quality Prediction of Multistage Manufacturing Systems

• URL: http://arxiv.org/abs/2105.08180v1
• Date: Mon, 17 May 2021 22:09:36 GMT
• Title: Deep Multistage Multi-Task Learning for Quality Prediction of Multistage Manufacturing Systems
• Authors: Hao Yan, Nurretin Dorukhan Sergin, William A. Brenneman, Stephen Joseph Lange, Shan Ba
• Abstract summary: We propose a deep multistage multi-task learning framework to jointly predict all output sensing variables in a unified end-to-end learning framework. Our numerical studies and real case study have shown that the new model has a superior performance compared to many benchmark methods.
• Score: 7.619217846525994
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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