Study of Tool Wear Monitoring Using Machine Vision
Ruitao Peng, Haolin Pang, Haojian Jiang, Yunbo Hu
Abstract
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.