Journal of Quality Technology ( IF 2.6 ) Pub Date : 2021-04-20 , DOI: 10.1080/00224065.2021.1903822 Hao Yan 1 , Nurettin Dorukhan Sergin 1 , William A. Brenneman 2 , Stephen Joseph Lange 2 , Shan Ba 2
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.
中文翻译:
用于多级制造系统质量预测的深度多级多任务学习
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。