Surface Defect Detection of High Precision Cylindrical Metal Parts Based on Machine Vision

The surface quality of high precision cylindrical metal parts is an important index to measure its quality. Most of the existing detection methods still use manual visual inspection. Manual detection is inefficient and difficult to ensure the standard of detection. It is difficult to make an effective judgment for the defects in the critical index, and it is more prone to miss detection and misjudgment. In this paper, the seamless steel pipe used for the shock absorber of bike is taken as the main research object, and machine vision is used for its surface defecting. Combined with the characteristics of arc and high reflection on the surface of steel pipe, an image acquisition and processing system composed of linear light source, linear array camera, encoder and rotation system is proposed. Refer to the national standard GB/T9797-2005, the defects mainly include pit, spalling, pitting, speckle, which is determined by Fourier transform, gradient threshold, and line detection by their four different characteristics. Finally, a complete experimental platform with clamping, blowing, detection, and classification functions is built to test. The experimental results show that the stability, accuracy and detection efficiency of the steel pipe detection system based on machine vision is high, which can meet the needs of daily production detection.