Tool Wear Prediction Combining Global Feature Attention and Long Short-Term Memory Network

This study aims to accurately predict tool flank wear in milling and simplify the complexity of feature selection. A hybrid approach is proposed to eclectically integrate the advantages between the long short-term memory (LSTM) network and the global feature attention (GFA) module. First, the feature matrix is calculated using the multi-domain feature extraction method. Subsequently, a parallel network is employed to achieve feature fusion. The stacked LSTM network extracts the temporal dependencies between features and the GFA module is used to adaptively complement key features representing global information of samples. Finally, the output features are concatenated, and tool wear prediction is achieved through a fully connected layer. Experiments demonstrate the optimal performance in predicting tool flank wear. Specifically, using the proposed GFA-LSTM framework reduces the mean absolute error (MAE) by 36.9%, 17.7%, and 25.2% in three experiments compared to the simple LSTM, validating the effectiveness of the proposed method.