Physics-informed hidden markov model for tool wear monitoring

Tool wear monitoring (TWM) is an important part of modern intelligent machining. An efficient and accurate monitoring system will effectively improve machining accuracy and reduce processing costs. At present, the methods for TWM can generally be divided into three categories: physics-based models, data-driven models, and hybrid models. These current approaches are suffered from either low prediction accuracy, insufficient model training, or poor result interpretability due to the nature of the model construction approaches. To overcome the shortcomings of these methods, a physics-informed hidden Markov model (PI-HMM) proposed in this study fully integrates the advantages of the above two methods. Firstly, the model constrains the division of hidden states through the tool wear physical model to improve the physical consistency of the model. Secondly, according to the process parameters and the labels generated by the tool wear physical model, the training set is expanded to solve the problem of insufficient training data of the data model. Finally, the influence of real-time features is added to the established wear state output model and the physical data fusion training strategy is used to train the model to realize the classification of tool wear state and the regression of wear value. The experimental results show that the proposed model in this study achieves an average recognition rate of 0.995 for wear classification and an average coefficient of determination (R2) of 0.968 for wear regression.