基于机器视觉零件轴线直线度误差测量的研究

Research on straightness error measurement of part axis based on machine vision

轴类零件的直线度误差是判断其是否合格的一个重要标准。针对接触测量轴零件直线度误差效率低、精度不高等问题,设计一个针对小型轴类零件直线度误差测量的平台;采用一种基于自适应阈值的八邻域空心梯度加权的清晰度评价函数用于相机自动对焦,经图像预处理、形态学操作、亚像素级边缘坐标提取后,通过径向局部区域搜索的方法得到零件中心轴线;提出基于最小区域的大变异双切点交叉遗传算法来评定零件中心轴线的直线度误差;采用图像用户界面集成评定算法。结果表明文中方法评定误差优于最小二乘法、分割逼近法和最小区域法,与文献中算法的评定结果基本一致。最后与三坐标测量仪测量结果进行对比,其中94%以上的测量结果相差10μm以内,因而本检测系统能够用于小型轴类零件轴线的直线度误差的测量中。

Straightness of shaft parts is an important criterion to judge whether a part passes quality con⁃trol.To solve the problems of low efficiency and insufficient accuracy of traditional methods for measuring straightness,a platform for measuring the straightness of short shaft parts has been developed.A sharpness function based on eight neighborhood hollow gradient weighting is proposed for achieving autofocus.Using image preprocessing,morphological operations,and sub-pixel edge coordinate extraction,the central axis of each part is obtained using the radial local area search method.Next,a large-variation double-tangent cross genetic algorithm based on the minimum region method is proposed for measuring the straightness of the central axis.Four algorithms are integrated by the graphical user interface.Evaluation error using this algorithm is less than that obtained using the least squares method,the segmentation approximation method,or the minimum area method,consistent with the literature reports of results obtained using this algo⁃rithm.Finally more than 94%of the results are within 10μm of the results obtained using a 3-axis measur⁃ing machine.This system can thus be used to measure the straightness error of short shaft parts.