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舵类结构件几何量误差视觉检测方法及误差评定

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针对现有接触式测量方法装夹定位后只能测量一种或两种参数、检测效率低等问题,提出一种舵类结构件几何量误差和装配误差视觉检测方法.首先,利用计算机视觉系统获取舵轴和舵面区域图像,利用图像预处理技术去除图像畸变和噪声,为了更有效地提取舵类结构件边缘,分别采用Sobel算子、Scharr算子、Laplace算子和Canny算子对图像进行边缘检测以确定轮廓,实验对比发现Scharr算子处理后的舵轴图像边缘更清晰且无间断,Canny算子处理后的摇臂图像边缘比较清晰,因此选用Scharr算子提取舵轴图像边缘、Canny算子提取摇臂图像边缘.结合被测要素特点,采用霍夫直线和霍夫圆检测方法提取舵面边缘线特征、舵轴母线特征、摇臂圆轮廓特征;确定了舵芯对称度、舵轴垂直度、摇臂夹角的基准要素,构建了几何量误差检测目标函数,运用自适应遗传算法计算最优解;结合相机标定的内外参矩阵,得到舵芯对称度、舵轴垂直度、摇臂夹角的测量值.最后,研发了舵类结构件几何量误差和装配误差视觉检测软件,搭建了视觉检测实验平台,实现了几何量误差及装配角度误差快速检测功能.经过多次重复测量实验,对称度检测精度达到0.055 mm,垂直度检测精度达到0.225 mm,装配角度检测精度达到0.772°,完成单次检测耗时7 s.该方法不仅提高了几何量误差检测精度和检测效率,同时有助于提高舵类结构件成型-制造-在机检测的自动化和智能化水平.
Geometric errors vision inspection and error evaluation method of rudder structural parts
In view of the problems that existing contact measurement methods can only measure one or two parameters after clamping and positioning,and low inspection efficiency,and combining the machine vision inspection technology has outstanding advantages such as no contact,no damage,high degree of au-tomation and safety and reliability,a geometric errors vision inspection method of the air rudder was pro-posed.Firstly,the images of rudder shaft and rudder surface area were acquired by a computer vision sys-tem,and image distortion was removed using the camera parameters and distortion parameters obtained from the camera calibration.Image pre-processing techniques were used to remove noise and reduce the impact of non-target elements in the detection environment on the detected object.In order to extract the rudder-like structural member edges more efficiently,Sobel operator,Scharr operator,Laplace operator and Canny operator were used to detect the edges of the image to determine the contours.In order to more effectively extract the edge and determine the contours of the air rudder,the Sobel operator,Scharr opera-tor,Laplace operator,and Canny operator were used to detect the edge detection of images.The experi-mental results show that the edges of the rudder shaft image were sharper and more complete after the Scharr operator,and the edges of the rocker image were sharper after the Canny operator.Therefore,the Scharr operator was used to extract the edges of the rudder image and the Canny operator to extract the edges of the rocker image.Combining the characteristics of the testing elements,Hoff straight line and Hoff circle detection methods are used to extract rudder surface edge line features,rudder shaft bus fea-tures,and rocker arm circle contour features.And the reference elements of the rudder core symmetry,rudder shaft perpendicularity,and rocker arm pinch angle are determined.Construct an objective function for geometric quantity error detection and compute the optimal solution using an adaptive genetic algo-rithm.The measured values of rudder core symmetry,rudder axis perpendicularity and rocker angle are obtained from the internal and external reference matrix obtained from the camera calibration.Finally,the vision inspection software for geometric and assembly errors of air rudder parts has been developed,and the vision inspection experiment platform has been constructed to realize the rapid detection of geometric and assembly angle errors of air rudder parts.After several repetitive measurement experiments,the sym-metry inspection accuracy reaches 0.086 mm,the perpendicularity inspection accuracy reaches 0.233 mm and the assembly angle inspection accuracy reaches 0.373°,and the detection time is 7 s.The experimental results show that the method not only improves the accuracy and efficiency of geometric errors detection,but also helps to improve the automation and intelligence of forming-manufacturing-in-machine inspection of air rudder parts.

air ruddergeometric errorsvision inspectionhough transform

杨泽青、平恩旭、陈英姝、胡宁、张毅、金一、吕雅丽

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河北工业大学 机械工程学院,天津 300401

河北工业大学 电工装备可靠性与智能化国家重点实验室,天津 300401

天津爱思达航天科技股份有限公司,天津 300000

舵类结构件 几何量误差 视觉检测 霍夫变换

国家自然科学基金资助项目国家自然科学基金资助项目天津市智能制造专项资助项目国家重点研发计划资助项目

5217546112227801202011992019YFC0840709

2024

光学精密工程
中国科学院长春光学精密机械与物理研究所 中国仪器仪表学会

光学精密工程

CSTPCD北大核心
影响因子:2.059
ISSN:1004-924X
年,卷(期):2024.32(2)
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