A novel approach of combined edge detection and segmentation for tool wear measurement in machining

Product quality in machined parts is governed by many factors, out of which the state of wear of the tool is one of the most critical factors. Knowing the condition of the tool wear makes it possible to optimize the tool life and simultaneously maintain the surface quality. There are methods of online wear measurement proposed in the literature, like correlating some physical parameters to the wear state of the tool. As the processes are indirect, they do not provide exact values of the tool wear, but only aids in classifying the wear into different states from mild to severe. This work is focused on developing direct tool wear measurement by applying image processing techniques, which is more accurate, and precise. It has a very negligible interruption in production, and helps in automation of the task of tool wear monitoring and replacing it. In this paper, a novel online tool wear measuring algorithm is proposed using combined techniques of edge detection and segmentation. A complementary metal–oxide–semiconductor (CMOS) sensor camera is utilized to capture the wear zone images. The tool’s wear value is extracted by establishing wear boundaries through image processing, threshold segmentation, edge detection, and morphological operation. The machining tests are performed on a CNC lathe. The tool wear measured by the proposed technique is compared with the measurements obtained by an optical microscope. The results demonstrated high detection accuracy of the proposed approach enabling online tool wear monitoring during the turning process.