Tool Fault Diagnosis Method Based on Multiscale-Channel Attention Network

Abstract

In the modern machine tool manufacturing scene, as the milling tool of CNC machine tool, the health of the tool directly affects the processing efficiency and product quality. Effective prognostic and health management of tool is critical. Precise monitoring of tool wear helps to avoid product quality problems caused by tool fault and improve production efficiency. Therefore, this paper constructs a tool fault diagnosis method based on deep learning. In order to effectively fuse the vibration signals features from different directions of machine tool spindle, we apply a variety of Channel Attention (CA) mechanisms to Multiscale Network (MSNet) to construct Multiscale-Channel Attention Network (MS-CA Net), and explore the performance gains of these modules in tool wear classification tasks. Among these modules, the CA blocks include Channel Attention Block (CAB), Squeeze and Excitation Block (SEB), and Efficient Channel Attention Block (ECAB). We use the three proposed networks to classify the tool wear status by identifying the vibration signal of the machine tool spindle. At the same time, in order to verify the performance of the proposed tool fault diagnosis method in the actual milling scene, this paper designs a tool wear test platform to collect sample data that meets actual industrial production scenarios, which uses a three-axis accelerometer to collect tool life cycle vibration signals, and a digital universal tool microscope to measure tool wear values. The experimental results show that, compared with the MSNet method, the tool fault diagnosis accuracy rate of the improved method is increased by 4.47%-7.38%.