Physics-Guided Deep Learning Method for Tool Condition Monitoring in Smart Machining System

Accurate and fast prediction of tool conditions is fundamental to improve the machining accuracy and consistency in smart machining systems. The current tool condition monitoring methods, i.e., physics-based and data-driven approaches, have either low prediction accuracy or model generalization. To solve the shortcomings and utilize the benefits of both sides, a novel physics-guided deep learning model is developed in this study. The physics of cutting mechanics and tool wear model is applied to guide the model construction and regulate the network learning process. The model first generates large labeled simulation dataset for the pretraining of the deep network, and solves the problem of labeled-data insufficiency for network training in practice. It then fine-tunes the model through the monitored signal to optimize the network weight and alleviate the deviation between the physical model and the actual machining process, which improves the physical consistency and generalization of the model. Additionally, with the introduction of attention mechanism to the deep residual network, discriminant features can be extracted to distinguish wear values and working conditions. The experimental results show that by learning the physics, the physics-guided deep network can accurately predict the tool wear even under limited training sets and varied working conditions, and it outperforms the original data-driven models and the physical models.