A Tool Wear Prediction Model Based on Attention Mechanism and IndRNN

Machining equipment often faces health monitoring problems during long-term use. As an essential piece of equipment for industrial processing, the state of the cutter tool is directly related to the quality of machined parts. Therefore, tool wear prediction plays an important role in improving the quality of parts and achieving intelligent management of equipment health. In real industrial scenarios, some complex tool milling needs to change the machining parameters, and the changes in these machining parameters will directly affect the tool wear status. In addition, some redundant data collected by sensors can also affect the model's training speed and prediction accuracy. To solve the above problems, a CNN-AIndRNN dual-input model is proposed in this paper. The method empowers the IndRNN attention regulation by introducing the attention mechanism EleAttG to reduce the influence of redundant information on the model. Meanwhile, CNN and AIndRNN are used to extract local and temporal features of the data to avoid information loss. For the problem of multiple working conditions, both sensor signals and machining parameters are used as model inputs in this paper to emphasize the influence of machining parameters on tool wear. Finally, the hyperparameters of the model are optimally selected using the whale optimization algorithm. The proposed model is validated on the NASA milling dataset. The experimental results show that the proposed model has a smaller RMSE and MAE than other baseline models. Machining parameters as a model input can effectively improve the performance of the model, and WOA can also optimize the model for model prediction accuracy.