基于机器视觉的大尺寸薄壁机械零件微裂纹检测研究  

Research on Micro-crack Detection of Large-size and Thin-walled Mechanical Parts Based on Machine Vision

作　　者：张娟飞[1] ZHANG Juanfei(Shaanxi Institute of Technology,Xi'an 710300,China)

机构地区：[1]陕西国防工业职业技术学院,陕西西安710300

出　　处：《机械制造与自动化》2022年第3期225-228,共4页Machine Building & Automation

基　　金：陕西国防工业职业技术学院2021年度科研计划项目(Gfy21-22)。

摘　　要：当前的微裂纹检测方法不能对微裂纹图像进行平滑处理,导致无法有效检测到微裂纹的长度、面积以及圆度。为此,设计一种基于机器视觉的大尺寸薄壁机械零件微裂纹检测方法,对大尺寸薄壁机械零件微裂纹图像进行灰度拉伸,利用邻域均值算法对拉伸后的图像进行平滑处理,进而利用机器视觉理论提取微裂纹图像缺陷特征,通过计算对微裂纹的缺陷特征进行约束处理,完成大尺寸薄壁机械零件的微裂纹检测。测试结果表明:该方法具有较好的检测效果和精准度。To overcome the poor effective detection of the length,area and roundness of micro-crack due to its unsmooth image worked by current detection methods,a micro-crack detection method for large scale thin-walled mechanical parts based on mechanical vision is designed,with which the micro-crack image of large scale thin-walled mechanical parts is gray stretched,the stretched image is smoothed by the neighborhood mean algorithm,the defect characteristics of micro-crack image are extracted by machine vision theory and the defect characteristics of micro-cracks are constrained by calculation so as to complete the micro-crack detection of large size thin-walled mechanical parts.The test results show that the designed method has good detection effect and accuracy.

关 键 词：机器视觉 薄壁机械 微裂纹 邻域均值滤波 灰度拉伸 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]