Research on Detection Algorithm of PCB Welding Anomaly Based on Depth Learning

The soldering quality of solder joints determines the reliability of the board, and the rapid detection of abnormal board soldering is a prerequisite for mass production. In order to quickly realize the accurate detection of welding anomaly, a solder joint image recognition algorithm based on deep learning is proposed. The algorithm is implemented by adaptive moment estimation combined with accelerated convolutional neural network, which can quickly classify and detect a large number of welding pictures. The experiment selected 5,000 welding image training set tests and compared with the traditional K-means clustering algorithm and Canny edge detection algorithm. The experimental results show that the three methods have similar effects in the small ball and bridge defects, but the algorithm has obvious advantages in the case of virtual welding and less tin defects. In the 1000 sets of test set experiments, the comprehensive detection rate and recall rate were 97.92% and 98.21%, respectively, which were significantly better than the traditional method. It will have a good application prospect.