Intelligent recognition of tool wear in milling based on a single sensor signal

A major problem in the high-speed cutting process of machine tools is tool wear. Tool wear directly affects the surface quality and machining accuracy of the workpiece. However, the limits of fusing multiple sensing signals to indirectly monitor tool wear are rarely concerned in real manufacturing environments. In this paper, a tool wear identification method based on a single sensor signal is proposed. To solve the limits of less obtained information and poor anti-interference ability of single sensor, multi-domain feature fusion strategy is established. By establishing a hybrid model of deep convolutional neural network and stacked long short-term memory network, the complex mapping relationship between fusion features and tool wear is constructed. Specifically, the spatial features of the input data set are extracted by the convolution kernel of the deep convolutional neural network. Then, a stacked double-layer long short-term memory neural network is established to capture sequence features with long-term dependence, thereby identifying tool wear. Finally, the superiority of the developed method is verified by tool wear experiments. The results show that the method can be effectively applied to tool wear identification from single sensor signals, and the mean RMSE and MAE of the identification results are 9.43 and 7.15, respectively. Compared with four other traditional multiple regression methods, RMSE and MAE are reduced by 73.0% and 78.7% on average. This study provides a reference value for the industrial implementation of tool wear monitoring system.