Tool health monitoring and prediction via attention-based encoder-decoder with a multi-step mechanism

In metal cutting processing, prediction of the tool wear or VB value can help early warning and timely tool replacement before the tool reaches the service life. Although the deep neural network is an effective method to predict the tool wear, the existing research predicts the tool wear only at its next time moment without considering the tool states at different time points. It ignores important information of the tool wear. In this paper, we propose a comprehensive model, which consists of a monitoring module and a prediction module, to monitor and predict the tool wear for the first time. In the monitoring module, a DenseNet model is constructed to monitor the tool wear via sensor signals. In addition, the prediction module based on attention mechanism is developed by simulating the human brain attention to selectively focus on the important part of processing sequence information. An encoder-decoder structure is introduced for a multi-step prediction of the tool wear. Several VB values in the nearest future are predicted by using sequential VB values monitored in the latest past. Experimental studies show that the short-term information has more influence on the tool wear prediction than the long-term information. The proposed method has been used to predict multi-step VB values in milling operations.