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.

摘要

在多阶段制造系统中，基于过程传感变量对多个质量指标进行建模很重要。然而，经典建模技术一次预测每个质量变量，没有考虑阶段内或阶段之间的相关性。我们提出了一个深度多阶段多任务学习框架，根据 MMS 中的顺序系统架构，在统一的端到端学习框架中联合预测所有输出传感变量。我们的数值研究和实际案例研究表明，与许多基准方法相比，新模型具有优越的性能，并且通过开发的变量选择技术具有出色的可解释性。