A hybrid-driven probabilistic state space model for tool wear monitoring

Tool wear monitoring (TWM) is essential to improve product quality and maintain machining safety. Within recent years, numerous techniques have been developed for TWM, including data-driven methods and physics-based models. However, traditional data-driven methods are highly dependent on measured data. The physics-based models are difficult to model complex machining processes. To solve these problems, a hybrid-driven probabilistic state space model is proposed to improve the accuracy and robustness of TWM. In this work, a sensitive feature extraction scheme is first constructed to eliminate interferences and redundancies for improving monitoring efficiency. Subsequently, the Gaussian process is innovatively developed to integrate the data mining and physical model from a probabilistic perspective. On this basis, the particle filter is then utilized to estimate the model parameters and estimate tool wear conditions. Finally, an adaptive wear state recognition method is further established for the predictive maintenance of cutting tools. The practical data obtained from milling experiments are used to validate the effectiveness of the proposed methodology. The results show that the proposed hybrid-driven method effectively improves tool wear prediction accuracy to over 97.7%, while the constructed probabilistic model successfully evaluates the prediction results with over 95% confidence. Therefore, the developed methodology may provide a promising way for tool health management to improve economic efficiency and maintain machining safety.