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 discrepancies of wear features under different process parameters while enhancing the aggregation of similar wear features.Through practical cutting experiments on the tool,the effectiveness of the proposed method in improving the identification accuracy under cross-process parameters is validated.Visual analysis of the spatial attention weight distribution indicates that the proposed method's extracted features possess obvious physical interpretability.The proposed method can provide a reference for the interpretability optimization of the deep learning-based identification model of tool wear monitoring.
interpretability deep learningtool wearspatial attentionphysical characteristics
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