A partial domain generalization method for modeling multiple multistage manufacturing processes
针对多阶段制造过程中多生产线并存且存在差异的问题，提出一种部分域泛化方法，结合历史过程信息与当前过程特征，提升参数优化与预测性能。
In complex manufacturing systems, materials are processed by machines sequentially before final products are obtained, forming a Multistage Manufacturing Process (MMP). In a modern massive production factory, it is common that multiple MMPs work simultaneously to fulfill productivity needs. Multiple MMPs may contain machines with different running times, processing the same types of products, but with different specifications. In such a Multiple MMP system, it is critical for machines to learn from each other to gain optimal parameter settings of each. However, traditional machine learning methods usually fail to consider both similarities and unique features among multiple processes at the same time. To address this problem, a partial domain generalization method is proposed based on the thought of transfer learning to combine useful information from historical processes while maintaining features from the in-production process. The proposed method also suits for problems with the assumption that only partial input variables are available. Studies reveal that the proposed method has superior prediction performance over traditional machine learning methods and some widely-used transfer learning methods.