Segmentation and evaluation of diamond abrasive grains based on K-Means clustering and convex hull detection

Abstract

Diamond tools are widely used in grinding, wire sawing and other fields. The characteristics of abrasive particles on the surface are an important factor affecting the machining results and tool performance. To process abrasive grain images with complex background information, this paper proposed an abrasive grain segmentation method based on K-means clustering and convex hull detection, which combines binarization, morphological processing, main contour extraction and other related operations to achieve abrasive grain extraction. Finally, three related indicators, including abrasive grain contour area accuracy ηCAA, abrasive grain position error θPE, and abrasive grain quantity recall rate σQR, were proposed to evaluate the segmentation effect. The results show that the average contour area precision is 98.30%, the average position error is only 2.93%, and the average number recall rate is 95.91%, which proves the accuracy of the method.

Keywords

• diamond tools
• abrasive grain characteristics
• k-means clustering
• convex hull detection ; abrasive grain segmentation