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

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.

Keywords:

• deep neural network
• multistage manufacturing process
• multi-task learning
• quality prediction

Additional information

Funding

This work is partially funded by the grants from NSF DMS 1830363 and NSF CMMI 1922739. The support is gratefully acknowledged.

Notes on contributors

Hao Yan

Hao Yan received his BS degree in Physics from the Peking University, Beijing, China, in 2011. He also received a MS degree in Statistics, a MS degree in Computational Science and Engineering, and a PhD degree in Industrial Engineering from Georgia Institute of Technology, Atlanta, in 2015, 2016, 2017, respectively. Currently, he is an Assistant Professor in the School of Computing, Informatics, and Decision Systems Engineering at ASU. His research interests focus on developing scalable statistical learning algorithms for large-scale high-dimensional data with complex heterogeneous structures to extract useful information for the purpose of system performance assessment, anomaly detection, intelligent sampling and decision making. Dr. Yan was also the recipient of multiple awards including best paper award in IEEE TASE, IISE Transaction and ASQ Brumbaugh Award. Dr. Yan is a member of IEEE, INFORMS and IIE.

Nurettin Dorukhan Sergin

Nurettin Dorukhan Sergin is a doctoral candidate at the Industrial Engineer program at Arizona State University. His current research is focused on out-of-distribution behaviors of deep neural networks and spatiotemporal modeling of urban mobility. During his master's, he did research on agent-based modeling and its application to computational social simulation problems.

William A. Brenneman

William A. Brenneman is a Research Fellow and the Global Statistics Discipline Leader at Procter & Gamble in the Data and Modeling Sciences Department and an Adjunct Professor of Practice at Georgia Tech in the Stewart School of Industrial and Systems Engineering. Since joining P&G, he has worked on a wide range of projects that deal with statistics applications in his areas of expertise: design and analysis of experiments, robust parameter design, reliability engineering, statistical process control, computer experiments, machine learning and statistical thinking. He was also instrumental in the development of an in-house statistics curriculum. He received a Ph.D. in Statistics from the University of Michigan, an MS in Mathematics from the University of Iowa and a BA in Mathematics and Secondary Education from Tabor College. He is a Fellow in both the American Statistical Association (ASA) and the American Society for Quality (ASQ). He has served as ASQ Statistics Division Chair, ASA Quality and Productivity Section Chair and as Associate Editor for Technometrics. William also has seven years of experience as an educator at the high school and college level.

Stephen Joseph Lange

Stephen Joseph Lange is Managing Member of ProcessDev, LLC, a manufacturing process consultancy, and retired as a Research Fellow from the Procter & Gamble Company, where he had a 35-year career in Research and Development, developing processes and materials for new products and product improvements.

Shan Ba

Shan Ba is a data science applied researcher at LinkedIn. He had previously worked as a group data scientist at the Procter & Gamble Company and an assistant professor of statistics at the Fariborz Maseeh Department of Mathematics and Statistics, Portland State University. He received his Ph.D. in Industrial Engineering from Georgia Institute of Technology.