Study of Surface Defect Detection Techniques in Grinding of SiCp/Al Composites

Abstract

SiCp/Al composites are used in the aerospace, automotive, and electronics fields, among others, due to their excellent physical and mechanical properties. However, as they are hard-to-machine materials, poor surface quality has become a major limitation to their wider applications. To effectively control the quality of machined surfaces, it is necessary to accurately detect and characterize defects. Based on the YOLOv4 object detection algorithm, a SiCp/Al composite machined surface defect detection model has been developed for the accurate and fast detection of machined surface defects. OpenCV is used to process images of detected defects and extract defect feature parameters. The number of defects and the total defect area in the same machining area are used as evaluation criteria to assess the quality of the machined surface, and the effect of the machining parameters on the quality of the machined surface is analyzed. The results show that the number and total area of surface defects that occur when grinding SiCp/Al composites are positively correlated with the feed rate, tool diameter, and size of the abrasive, while they are negatively correlated with the spindle speed and ultrasonic vibration amplitude. When the grinding depth is greater than 20 microns, the quality of the machined surface is greatly affected.