A U-Net-Based Approach for Tool Wear Area Detection and Identification

Abstract

The tool wear condition monitoring is key to ensuring product quality. This article develops a direct technique dealing with cutting tool images to automate the tool wear detection and identification. The constructed U-Net-based network can realize an effective and reliable extraction of the tool wear area. The introduction of deep supervision with a Matthews correlation coefficient (MCC)-based surrogate loss function helps to address the few-shot and data imbalance issues. Experiments on the images with wear on the flank face of cutting tools from a computer numerical control (CNC) turning machine show the effectiveness, competitiveness, and reliability of the proposed method under different types of loss functions.