Wear Status Recognition for Milling Cutter Based on Compressed Sensing and Noise Stacking Sparse Auto-encoder

In the machining process of CNC machine tools, the state of tool wear has a great influence on the surface quality and dimensional accuracy of the parts being machined. However, the traditional method of relying on cutting forces to analyze tool wear requires the installation of additional force measuring devices on the workbench. This will interfere with the normal machining of the machine tool, limit the size of the part being machined, cause the processing quality to be reduced and other issues, and limit its application in the actual industrial environment. Aiming at the above problems, a monitoring method for identifying the wear state of the milling cutter by using the spindle current combined with the deep learning network is proposed. Firstly, the feasibility of using spindle current instead of cutting force to identify tool wear is demonstrated. Then, the frequency domain data of the current signal is compressed by compressed sensing, and Gaussian white noise is added to the observation signal to improve the robustness of the network. Finally, the compressed data is input into a stack sparse auto-encoder, which uses a combination of supervised learning and unsupervised learning to extract feature information caused by tool wear and to characterize tool wear. The experimental results show that this method can effectively monitor the wear state of the milling cutter.