Detection of Cutting Tools Wear Status Based on Machine Vision
With the development of the manufacturing industry,the wear state of cutting tools has a significant impact on machining quality and efficiency.The use of sensor detection methods has the problems of limited detection environment and difficult accuracy guarantee,while machine vision based wear state detection methods can achieve automated detection and situation analysis of tool wear state through image processing technology.This paper proposes a method of using machine vision to detect tool wear status.Firstly,Gaussian filtering is used to remove interference and noise from the tool grayscale image.Then,further reduce interference through operations such as binarization and threshold segmentation.Finally,the wear status of the tool was successfully identified through contour filling and other operations.This method has high stability and robustness,which is of great significance for improving the automation level of tool detection and optimizing production efficiency in the manufacturing industry.
万方数据
维普
