Research on multi-step ahead prediction method for tool wear based on MSTCN-SBiGRU-MHA

Abstract

Tool wear monitoring (TWM), as an important component of modern intelligent processing, faces significant challenges related to accuracy and long-term predictability. This research proposes a method for the precise and reliable multi-step prediction of tool wear. First, a dual-indicator feature screening scheme is proposed. The constructed sensitive features can describe the tool wear condition from multiple perspectives. Further, the MSTCN-SBiGRU-MHA model is developed to effectively analyze time series data by incorporating three key modules. The synergistic interaction among these three modules contributes to the model’s superior performance in complex time series prediction tasks. Finally, the multi-step prediction approach is integrated with interval prediction, and the validity of the resultant predictions is substantiated through milling experiments. Ablation experiments and the SHAP method are used to analyze the contribution of different modules and features to the model’s performance. Comparative experiments show that the model’s R2 for predicting the next 1, 5, and 10 steps across various datasets exceeded 0.86, significantly outperforming the SLSTM, SGRU, SBiLSTM-AT, and CNN-LSTM models. Accurate advance prediction of tool wear is crucial for developing an intelligent early warning system, ensuring high-quality production, reducing operational and maintenance costs, and enhancing machining safety.