) 同济大学 机械与能源工程学院, 上海 201804
收稿日期:2020-03-30出版日期:2021-06-28发布日期:2021-06-30通讯作者:余建波E-mail:jbyu@tongji.edu.cn作者简介:周俊杰(1996-),男,浙江省金华市人,硕士生,主要研究方向为机器视觉、图像处理基金资助:国家自然科学基金(71777173);上海科委“科技创新行动计划”高新技术领域项目(19511106303);中央高校基本业务经费项目(22120180068);中央高校基本业务经费项目(22120190196)Online Measurement of Machining Tool Wear Based on Machine Vision
ZHOU Junjie, YU Jianbo() School of Mechanical Engineering, Tongji University, Shanghai 201804, China
Received:2020-03-30Online:2021-06-28Published:2021-06-30Contact:YU Jianbo E-mail:jbyu@tongji.edu.cn摘要/Abstract
摘要： 针对实际生产中测量刀具磨损需要人工操作、停机检测等问题,开发了一个基于机器视觉的加工刀具磨损测量系统.首先提出基于Laplacian算子边缘信息的Otsu分割算法将图像二值化,再通过基于形态学的Canny算子边缘检测粗定位及图像配准提取清晰的刀具磨损区域.最后,使用基于Zernike矩的亚像素边缘检测方法提高测量精度,并通过主曲线方法拟合亚像素边缘点得到光滑的边缘曲线,实现了刀具磨损量的在线测量.实际加工过程中的刀具磨损测试结果表明,该系统检测自动化程度高、运行速度快、测量精度可以达到微米级,可以有效地应用于工业上对加工刀具磨损的实时监控.
关键词: 加工刀具, 磨损测量, 机器视觉, 图像分割, 边缘检测
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.
Key words: machining tool, wear measurement, machine vision, image segmentation, edge detection
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