Research on Defect Detection Method of Motor Control Board Based on Image Processing

Abstract: The motor control board has various defects such as inconsistent color differences, incorrect plug-in positions, solder short circuits, and more. These defects directly affect the performance and stability of the motor control board, thereby having a negative impact on product quality. Therefore, studying the defect detection technology of the motor control board is an important means to improve the quality control level of the motor control board. Firstly, the processing methods of digital images about the motor control board were studied, and the noise suppression methods that affect image feature extraction were analyzed. Secondly, a specific model for defect feature extraction and color difference recognition of the tested motor control board was established, and qualified or defective products were determined based on feature thresholds. Thirdly, the search algorithm for defective images was optimized. Finally, comparative experiments were conducted on the typical motor control board, and the experimental results demonstrate that the accuracy of the motor control board defect detection model-based on image processing established in this paper reached over 99%. It is suitable for timely image processing of large quantities of motor control boards on the production line, and achieved efficient defect detection. The defect detection method can not only be used for online detection of the motor control board defects, but also provide solutions for the integrated circuit board defect processing for the industry.