An IG-LSTM-Based Tool Wear Prediction Method for Cross-Operating-Condition Forecasting

Tool wear directly affects the accuracy and reliability of machined workpieces; accurately predicting wear trends is crucial for developing reasonable tool-change strategies and avoiding sudden tool failure. Traditional parametric Inverse Gaussian (IG) processes can effectively characterize the non-negativity and stochastic fluctuations of incremental tool wear; however, the mean functions in such models often adopt an analytically defined power-law form that is arbitrarily preset, making it difficult to adapt to the nonlinear evolution patterns of tools at different stages. Consequently, their generalization capabilities are limited in cross-tool prediction scenarios. To address this, this paper proposes an IG-LSTM tool wear prediction method designed for cross-tool forecasting. First, Variational Mode Decomposition (VMD) is employed to extract the degradation trend of wear, and a Long Short-Term Memory (LSTM) neural network is trained based on this. Next, the trend predicted by the LSTM is used as the cumulative degradation function of the IG process to construct a degradation model for wear increment; simultaneously, the model parameters of the IG process are estimated via maximum likelihood estimation based on the training data. Finally, two sets of experiments—single-tool prediction and cross-tool prediction—were designed using the PHM2010 dataset. The experimental results show that, in both scenarios, the proposed method significantly outperforms traditional parametric inverse Gaussian models and purely data-driven LSTM models, achieving an organic integration of data-driven adaptive trend extraction and physics-driven stochastic degradation modeling.