A Machine vision method for non-contact Tool Wear Inspection

Tool wear is usually the most relevant parameter of tool performance detection, which directly affects the product quality and tool life. Online tool condition monitoring can effectively avoid workpiece scrap and machine failure. To solve the problems of using electron microscopes to measure tool wear in real production, a machine vision system for tool wear detection is developed in this paper to reduce testing costs. Through rough positioning by morphology-based Canny operator edge detection and image registration, the tool wear area can be extracted effectively. Sub-pixel edge detection based on Zernike moment is used to improve the measurement accuracy and the principal curve method is used to fit sub-pixel edge points to obtain smooth edge curve. Finally, the tool wear can be detected and measured effectively based on these methods. In the real machining process, the test results show that this system features high response speed, high inspecting accuracy and high automation. This system can be effectively applied to the real-time monitoring of tool wear in industry.