A physical guided data-driven tool condition monitoring model for high-speed vertical milling of carbon fiber reinforced polymer

Carbon fiber-reinforced polymer (CFRP) is a multiphase material consisting of fibers, interfaces, and matrix. Due to their excellent mechanical properties, they are widely used in the energy, military, and aerospace sectors. However, due to the anisotropic and non-homogeneous nature of the material, tool wear inevitably occurs during machining. In order to ensure the quality of material machining and to control tool costs, tool condition monitoring has become an integral part of machining. By monitoring the tool condition in machining, predictive maintenance can be achieved, and early warning of tool failure can be achieved, thus drastically reducing downtime and saving costs in terms of time and labor. On this basis, this paper proposes a novel physics-guided neural network approach for tool wear prediction. Firstly, the fusion of physical and data information is achieved through cross-physical data modeling. Second, a multi-channel 1D-CNN convolutional neural network is utilized to reduce the complexity of local feature extraction. In addition, a loss function considering physical subject factors is proposed to quantify the physical inconsistency. Experiments of the proposed model are carried out on carbon fiber reinforced ceramic matrix composites to validate the performance of the model in terms of MAE and RMSE.