Study of Tool Wear Monitoring Using Machine Vision

• Published in: Automatic control and computer sciences Vol. 54; no. 3; pp. 259 - 270
• Main Authors: Ruitao Peng, Pang, Haolin, Jiang, Haojian, Hu, Yunbo
• Format: Journal Article
• Language: English
• Published: Moscow Pleiades Publishing 01.05.2020; Springer Nature B.V
• Subjects: Charge coupled devices; Computer Science; Control Structures and Microprogramming; Edge detection; Image detection; Image segmentation; Machine vision; Milling (machining); Monitoring; Monitoring systems; Nickel base alloys; Superalloys; Tool wear; Vision systems; image processing; milling process; tool wear monitoring; on-line measurement; machine vision
• ISSN: 0146-4116; 1558-108X
• DOI: 10.3103/S0146411620030062

In order to improve tool utilization and reduce tool costs in milling processing, this paper presented a new approach to monitor tool wear status and replace tool in time by machine vision technology. A tool wear monitoring system was established. The wear images of the tool were obtained by a charge coupled device (CCD) camera, and the wear boundaries were established by image preprocessing, threshold segmentation and edge detection based on Canny operator and sub-pixel, then wear value of the tool was extracted. Milling experiments of GH4169 nickel-based superalloy were carried out. The wear values detected by the monitoring system were compared with that obtained by ultra-depth microscope. The results showed that the wear monitoring system had high detection accuracy and enabled on-machine monitoring of tool wear during milling process.