Physics-Informed Deep Learning for Tool Wear Monitoring

Tool condition monitoring is essential to maintain the final product quality and machining efficiency of the manufacturing process. However, traditional physics-based and data-driven approaches have limitations either on prediction efficiency or performance generalization, due to the nature of the respective approaches. To address these issues, in this article, a physics-informed deep learning approach is developed, which integrates the tool wear mechanism into the data-driven model. First, some representative physical information is selected for the task learning. Then, four practical physics-informed methods are proposed to integrate various physical information into the data-driven models. Based on these physical constraints, a physics-informed deep learning model is specially designed for tool wear monitoring. Compared with previous studies, more diverse physical information can be effectively utilized to guide the hypothesis space, thereby improving the generality of the model. The effectiveness and feasibility of this model under various working conditions are verified in high-speed milling experiments. The results show that the wear prediction of the proposed approach is more accurate and consistent under unknown machining conditions.