Tool wear state monitoring for smart machining: A deep learning method with embedded physical knowledge

Accurate tool wear state monitoring is essential for enhancing machining efficiency, prolonging tool life, and ensuring precision in smart machining systems. However, existing methods face significant challenges: physics-based methods often struggle to adapt to nonlinear and dynamic machining environments, while data-driven methods lack physical consistency and require extensive labeled data. To address these limitations, this study proposes a deep learning method with embedded physical knowledge for tool wear state monitoring. By integrating physical domain knowledge into a data-driven framework, the proposed method balances prediction accuracy with physical consistency. The model incorporates several innovations: a physics-guided loss function embeds the physical mechanisms of tool wear into the learning process; periodic and segmented attention mechanisms improve temporal feature extraction from cutting force signals; a data augmentation strategy leverages physics-based simulations to generate diverse and physically consistent datasets, enriching the model’s training data; and architectural improvements, such as monotonicity constraints, further enhance the model’s robustness and alignment with real-world tool wear behavior. Experimental results demonstrate that the proposed method significantly outperforms baseline approaches. It achieves a 74.1% improvement in prediction accuracy and maintains strong robustness, as further validated through experiments conducted under mildly varying machining conditions. This work presents a scalable and efficient solution for tool wear state monitoring in smart machining systems, bridging the gap between physics-based and data-driven paradigms.