Journal of Engineering Research

With the increasing complexity of electronic products, the quality control of printed circuit boards (PCBs) has become more and more important. Traditional manual detection methods are not only time-consuming and labor-intensive, but also prone to errors. The automatic PCB defect detection algorithm based on machine learning has gradually become an effective way to solve this problem. Aiming at the difficulties in PCB defect detection, this paper proposes an automated detection algorithm based on deep learning. By extracting the deep features of PCB image data and optimizing the classification model, the accuracy and efficiency of detection are significantly improved. The experimental results show that the algorithm has strong robustness in practical applications and can effectively reduce manual intervention. The research of this paper not only provides a feasible solution for the automatic detection of PCBs, but also provides reference for other industrial automated detection problems.

PCB defect detection; Machine learning; Deep learning; Image recognition; Automation