Cutting tool wear monitoring in milling processes by integrating deep residual convolution network and gated recurrent unit with an attention mechanism

The conditions of cutting tool wear in the machining process have a great influence on the dimension accuracy and surface integrity of a workpiece. Real-time tool condition monitoring can alert a worker to changes in a tool in time before tool failure occurs to reduce downtime and improve product quality. However, traditional monitoring methods are no longer able to meet the requirements of real-time monitoring in current intelligent manufacturing. Therefore, an online monitoring method based on deep learning to monitor the cutting tool conditions is proposed In this research. In this monitoring model, one-dimensional wide-kernel convolution is used to filter the high-frequency noise in the multi-dimensional raw signals, and then a deep residual convolution layer is adopted to adaptively extract the multi-dimensional spatial features of the signals, and a gated recurrent unit (GRU) layer with an attention mechanism is employed to adaptively extract the hidden temporal features of the sequence data and adaptively assign the corresponding weights, and finally, a regression layer is used to predict the wear value of the tool according to the extracted features. The PHM 2010 challenge dataset is used to illustrate and evaluate this proposed method. The experiment results show that the proposed method can accurately predict the future flank wear values in real time based on the acquired multi-dimensional signals and avoid the complexity of the manual feature extraction.