光学技术, 2016, 42 (6): 491, 网络出版: 2016-12-23
基于机器视觉的表面粗糙度测量与三维评定
Surface roughness measurement and three-dimensional assessment based on machine vision
摘要
表面粗糙度的测量与评定一直是机械行业的重要课题。提出了一种基于机器视觉检测工件表面粗糙度的方法。首先利用显微镜获取端铣、刨、车不同等级下工件表面的序列图像, 采用方差聚焦测度算子对序列图像中的每一个点进行高度计算; 然后再利用高斯插值法计算出微观物体表面的准确高度, 重构其表面微观形貌; 最后计算出各个工件表面的三维粗糙度。通过对实验数据的分析和讨论, 可以确定出表面均方根偏差Sq、表面偏斜度Ssk和表面峰密度Sds这三个参数, 它们是常用地对工件表面粗糙度进行评价的可靠参数, 可为以后三维粗糙度体系的科学建立提供依据。
Abstract
Surface roughness measurement is a important topic of machine-building industry. A method of the surface roughness inspection of workpieces by machine vision technique is introduced. Firstly, the sufface sequence images of workpieces are obtained by optical microscope, and then the variance focus measure operator is used to calculate the height of every bit for the sequence images. At last, the exact information for height of workpieces surface is calculated by Gauss interpolation and surface reconstruction for the surface microtopography is obtained. Moreover, based on the analysis of the numeric and physical significance of the different 3D parameters for the workpieces after milling, planing and turning, three parameters including surface root mean square deviation (Sq), surface skewness (Ssk) and surface peak density(Sds) are believed to be the dependable for the purpose to characterize the surface roughness.
杨洁, 李乐. 基于机器视觉的表面粗糙度测量与三维评定[J]. 光学技术, 2016, 42(6): 491. YANG Jie, LI Le. Surface roughness measurement and three-dimensional assessment based on machine vision[J]. Optical Technique, 2016, 42(6): 491.