Online Measurement of Machining Tool Wear Based on Machine Vision

Abstract

In order to solve the problems of tool wear measurement in actual production, such as manual operation and shutdown detection, a machining tool wear measurement system based on machine vision is developed in this paper. First, the Otsu segmentation algorithm based on Laplacian edge information is proposed to binarize the images. Then, through rough positioning by morphology-based Canny operator edge detection and image registration, the tool wear area is extracted effectively. Finally, sub-pixel edge detection based on Zernike moment is used to improve the measurement accuracy while the principal curve method is used to fit sub-pixel edge points so as to obtain the smooth edge curve and realize the online measurement of tool wear. In real machining process, the tool wear test results show that the system has a high degree of automation and a rapid running speed. Moreover, its measurement accuracy can reach micron level. This system can be effectively applied to real-time monitoring of tool wear in industry.

Keywords

• machining tool
• wear measurement
• machine vision
• image segmentation
• edge detection