ConvLSTM-Att: An Attention-Based Composite Deep Neural Network for Tool Wear Prediction

In order to improve the accuracy of tool wear prediction, an attention-based composite neural network, referred to as the ConvLSTM-Att model (1DCNN-LSTM-Attention), is proposed. Firstly, local multidimensional feature vectors are extracted with the help of a one-dimensional convolutional neural network (1D-CNN), which avoids the loss of wear features caused by manual feature extraction. Then the temporal relationship learning between multidimensional feature vectors is performed by introducing a long short-term memory (LSTM) network to make up for the lack of long-short distance dependence of the captured sequence of the CNN network. Finally, an attention mechanism is applied to strengthen the ability to extract key information from tool-wearing temporal features. The proposed ConvLSTM-Att model is trained with the measured tool wear data and then performs as a tool wear predictor. The model is compared with several state-of-the-art models on the PHM tool wear data sets. It significantly outperforms the other models in terms of prediction accuracy, but with similar computational complexity.

Automated summary

In order to improve the accuracy of tool wear prediction, a new attention-based composite neural network model called ConvLSTM-Att (1DCNN-LSTM-Attention) is proposed. The model uses a one-dimensional convolutional neural network (1D-CNN) to extract local multidimensional feature vectors and a long short-term memory (LSTM) network to learn the temporal relationship between the feature vectors. An attention mechanism is applied to enhance the extraction of key information from the tool-wearing temporal features. The proposed model outperforms other state-of-the-art models in terms of prediction accuracy while maintaining similar computational complexity.