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

Hao Yan, Nurettin Dorukhan Sergin, William A. Brenneman, Stephen Joseph Lange, Shan Ba

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
JournalJournal of Quality Technology
DOIs
StateAccepted/In press - 2021

Keywords

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

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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