Research on Surface Defect Detection Method of Metal Workpiece Based on Machine Learning

Abstract

Some uncontrollable defects will occur on the surface of metal workpieces during processing. The existence of surface defects not only affects the appearance of the finished product, but also affects the quality to a certain extent. Surface defect detection of metal workpieces can effectively improve product quality and production efficiency, and is an important link in the process of product quality control. Although there are many different types of surface defect detection methods, in the actual production process, due to the characteristics of multiple types and irregular distribution of the surface defects of metal workpieces, in most cases, manual inspection or simple machine inspection is still used to detect the surface of metal workpieces. Defect inspections often lead to missed inspections and false inspections. The defect detection efficiency, accuracy and precision of metal workpieces still need to be further improved. This paper studies the method of detecting the surface defects of metal workpieces based on deep learning, provides the surface defect recognition accuracy and defect detection rate of metal workpieces, and provides references for the staff and scientific researchers engaged in metal workpiece defect detection.