摘要
针对小型刀具在复杂加工环境中磨损难以测量的问题,提出了基于机器视觉的刀具磨损量在机检测方法.首先,设计了刀刃图像自适应分割计算方法,利用改进的高斯混合模型分割算法对原始图像进行分割;然后,利用改进的非线性滤波进行降噪处理,使刀具磨损区域图像在进行模糊降噪的同时保留边缘、降低误差,从而实现刀具图像自动分割、降噪和测量;最后,设计了单相机刀具图像采集机构并进行了切削实验.结果表明,刀具侧面、底面磨损面积和最大磨损带宽与传统电子显微镜测量结果相比误差分别为3.88%、5.41%和6.26%,精度可以达到微米级.相比传统磨损测量方法,该方法图像降噪更快且边缘清晰.不规则磨损区域以像素点为单位进行计算,有效解决了小型刀具刀刃微小不易观察和测量的问题,能够满足小型刀具在机检测的要求.
In order to solve the problem that it is difficult to measure the wear of small tools in complex machining environments,an on-machine tool wear detection method based on machine vision is proposed.First,a method of adaptive segmentation calculation for knife edge images is designed.The original image is segmented by using the improved Gaussian mixture model segmentation algorithm.Subsequently,the noise is reduced by using the improved nonlinear filter,effectively retaining edge details while reducing error during fuzzy noise reduction process,thus achieving automatic segmentation,denoising,and measurement of tool image.The single camera tool image acquisition mechanism is further designed,and cutting experiments are carried out.The results show that the errors in the tool side,bottom wear area,and the maximum wear bandwidth are 3.88%,5.41%,and 6.26%,respectively,compared with the traditional electron microscope measurements,achieving micron-level accuracy.Compared with traditional wear measurement methods,this system and algorithm enables faster image denoising with clearer edges.Irregular wear areas are calculated by pixel points,which effectively solves the problem that small tool blades are small and difficult to observe and measure,and can meet the requirements of on-machine detection of small tools.
作者
郭润兰
张昊
支晓波
尉卫卫
GUO Run-lan;ZHANG Hao;ZHI Xiao-bo;YU Wei-wei(School of Mechanical and Electrical Engineering,Lanzhou Univ.of Tech.,Lanzhou 730050,China)
出处
《兰州理工大学学报》
CAS
北大核心
2024年第6期33-41,共9页
Journal of Lanzhou University of Technology
基金
国家自然科学基金(51962037)。
关键词
刀具磨损
机器视觉
图像降噪
图像分割
磨损测量
machining tool
machine vision
image denoising
image segmentation
wear measurement
作者简介
通讯作者:郭润兰(1963-),女,山西山阴人,教授.Email:llggrl@126.com。
