Tool Fault Diagnosis Based on Improved Multiscale Network and Feature Fusion

Prognostic and health management is a key issue in the field of machine tool manufacturing. As the “teeth” of CNC machine tools, their health status directly affects the machining efficiency and the quality of products. Accurate monitoring of tool wear can help to avoid product quality problems caused by tool failure and improve productivity. In this paper, we investigate the deep learning-based tool fault diagnosis approach. First, a new data-driven tool fault diagnosis method based on improved multiscale network and feature fusion (IMSNet-F) is proposed to recognize and classify the tool wear condition. It can increase the efficiency of the process and make it possible to replace the tool before catastrophic wear occurs. And then, a tool wear experimental system is designed to verify the performance of the proposed tool fault diagnosis method in a real production scenario. Besides, based on the tool wear experimental system, a data set of vibration signals used to detect tool wear conditions is constructed and publicly released. Experimental results show the proposed approach can improve the tool fault diagnosis accuracy by 2.2% compared to existing methods.