Physics-informed meta learning for machining tool wear prediction

Tool wear prediction plays an important role in ensuring the reliability of machining operation due to their wide-ranging application in smart manufacturing. Massive effort has been devoted into exploring the methods of tool wear prediction. However, it remains a challenge to improve the accuracy of tool wear prediction under varying tool wear rates. To address this issue, this paper presents a new physics-informed meta-learning framework for tool wear prediction under varying wear rates. First, a physics-informed data-driven modeling strategy is proposed by employing the empirical equations’ parameters to improve the interpretability of the modeling and optimization of the data-driven models. The piecewise fitting is adopted to ensure the accuracy of the parameters. Second, the physics-informed model input is investigated to help the data-driven models explore the hidden information about the tool wear under varying tool wear rates. Third, the physics-informed loss term is presented to constrain the optimization of the meta-learning model. An experimental study on a milling machine is performed to validate the effectiveness of the presented method.