基于形位特征的机械零件加工位置识别

不同形状零件具有个性特征,为了对具体形状零件的准确定位进行进一步研究,首先通过霍夫变换提取给定零件轮廓中发直线和圆特征,进而利用峰值检测和K-means聚类分析方法准确识别有效的直线和圆特征;依次建立零件轮廓的绝对坐标系和零件标准位置模板坐标系;分别采用基于零件全特征和基于零件主要特征的方式提取零件轮廓的有效像素点,并通过模拟退火算法求解了不同迭代次数下采用这两种方式识别零件位置的速度和精度。为提高识别速度,建立基于零件几何特征的快速识别模型,即将目标函数简化为被测零件的圆心与标准位置模板圆心的最短距离和为最小,通过模拟退火算法进行求解。结果表明,零件的识别时间为0. 321 s,最低识别精度为98. 7%,可见该方法识别精度高,识别速度快。

Recognition of Machining Position of Mechanical Parts Based on Shape and Position Feature

Different shapes of parts have individual characteristics. In order to further study the accurate positioning of specific shape parts, this paper firstly extracts the straight line and circle features in the contour of a given part by Hough transform, and then uses the peak detection and K-means cluster analysis method to be accurate. Identify effective line and circle features; establish the absolute coordinate system of the part contour and the part standard position template coordinate system in turn; extract the effective pixel points of the part contour based on the full feature of the part and the main features of the part, respectively, and pass the simulated annealing algorithm The speed and accuracy of identifying the position of the part in these two ways are solved for different iterations. In order to improve the recognition speed, a fast recognition model based on the geometric features of the part is established, which is to simplify the objective function to the shortest distance and minimum of the center of the tested part and the center of the standard position template, and solve it by simulated annealing algorithm. The results show that the recognition time of part is 0.321s, the lowest recognition accuracy is 98.7%. It is included that the method has high recognition accuracy and fast recognition speed.