基于模糊C均值聚类算法的金刚石砂轮磨粒边缘检测

基于模糊C均值(FCM)聚类算法将金刚石砂轮表面检测数据划分成金刚石和结合剂两个类别,以数据的质心初始化聚类中心,用迭代的方法分别求出相应的最优聚类中心和隶属度矩阵,通过选取合适的隶属度阈值以及两个聚类中心的欧氏距离阈值来区分金刚石和结合剂,确定磨粒边缘。为验证方法的可行性,对多组数据进行检测,并用模拟的砂轮表面形貌对此方法进行了评定,评定结果与设定值误差不超过2.0%。

Grain Edge Detection of Diamond Grinding Wheel Based on a Fuzzy C-means Clustering Algorithm

Based on a fuzzy C-means (FCM) clustering algorithm, the height data set of a measured grinding wheel surface is classified into two fuzzy clusters, which are named as “grain” and “bond” respectively. The clusters centers are initialized with the centroids of the classified data first, and then the most optimal cluster centers and the membership matrix are obtained with an iterative strategy. By choosing appropriate thresholds of the membership degree and the Euclidean norm of the two cluster centers, the edge of grains is determined. The method is validated with experiment. For further analysis, an evaluation is carried out by applying this method to a simulated grinding wheel surface, the results of which show that the error of this method is less than 2.0%.