In-Process Tool Wear Prediction Enabled by Physics-Informed Liquid Neural Networks Under Varying Cutting Conditions

Tool wear is a critical factor affecting the quality and efficiency of machining. In-process tool wear prediction is of great importance for unmanned factories. However, its industrial application faces many challenges, especially in terms of complicated cutting conditions and limited accuracy. In this article, a novel approach of physics-informed liquid neural networks (PILNNs) is proposed for tool wear prediction under varying cutting conditions. The model extracts multidimensional features related to tool wear from the acceleration signals and inputs them into a multilayer perceptual machine. Physical constraints are constructed through a tool wear model to provide physical priori knowledge for the neural networks. Liquid time-constant neurons are constructed by the neural circuit policies method to build the liquid neural networks (LNNs). In this case, the PILNNs merge both physical laws and LNN structures, which significantly enhances the generalization performance towards tool wear prediction. The performance of the proposed model is evaluated by the data obtained under varying cutting conditions, as well as the comparison against traditional physics-informed neural networks and other machine learning models through the same dataset. The results illustrate that the proposed model shows high accuracy and stability in terms of tool wear prediction under varying cutting conditions, which is also superior to the traditional machine learning models.