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

TL;DR

This paper introduces a deep multistage multi-task learning framework for predicting multiple quality indices in multistage manufacturing systems, capturing inter-stage correlations and improving prediction accuracy.

Contribution

It presents a novel end-to-end deep learning model that jointly predicts all quality variables, enhancing performance and interpretability over traditional methods.

Findings

Outperforms benchmark methods in numerical studies

Demonstrates superior prediction accuracy in real case study

Provides interpretable variable selection techniques

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.