Tool Wear Condition Prediction Method Based on Sparse Identification of Nonlinear Dynamics (SINDy)

Current deep learning methods for tool wear monitoring suffer from poor interpretability and struggle to reveal the intrinsic relationship between signals and wear states. To address this issue, this paper presents an interpretable tool wear monitoring method based on Sparse Identification of Nonlinear Dynamics (SINDy). Multi-domain features are extracted from cutting force and acoustic emission signals to construct a time series. The SINDy algorithm is used to identify ordinary differential equations that describe the evolution of tool wear. An iterative “predict-validate-correct” mechanism is applied to optimize model parameters. Experimental results show that the mean absolute percentage error (MAPE) between the predicted and actual values is below 6%. Moreover, the optimal model demonstrates an average MAPE as low as 0.067% in cross-condition tests. This study provides an effective solution for online tool wear monitoring that achieves high precision, strong generalization, and physical interpretability.