Improved Interpretable-based Physically Guided Spatial Attention for Cross-process Parameters End Milling Cutter Wear Identification

The insufficient interpretability of deep learning has become a critical issue restraining its industrial applications. Intelligent assessment methods for tool wear state exhibit high levels of speed, automation, and intelligence; however, the end-to-end patterns and extracted features are challenging to be understood. Especially when dealing with cross-process parameters, their interpretability is poor, and their reliability is insufficient. Cross-process parameters end milling cutter wear state identification model is proposed to address this based on an improved interpretable-based physically guided spatial attention mechanism. Firstly, a physically guided spatial attention module is constructed based on the periodic discontinuity characteristics of the signals, enabling the adaptive capture of key signal fragments under cross-process parameters such as feed rate and cutting depth. Secondly, constraints are applied to the features using maximum mean discrepancy and variance, reducing the distribution discr