On-machine Wear Measurement for Milling Cutter Based on Machine Vision

As a key factor in the milling process, the wear status of the milling cutter has a significant impact on the machining quality of the workpiece. To detect wear on a milling machine efficiently and precisely, this paper presents the development of a milling machine wear detection system based on machine vision and digital image processing. The system including link mechanisms and industrial camera is designed for auxiliary localization and collection of on-machine images of milling cutter status. The image preprocessing method based on automatic threshold segmentation and Canny edge detection operator is proposed to identify the edge of cutter wear. The Maximum connected domains algorithm is used to screen the wear area of the milling cutter and the amount of wear is obtained based on a calibrated scaling method. Experimental results show that the proposed system is suitable for industrial use due to its rapid detection speed and strong recognition accuracy, which are desirable for engineering applications.