Optimising solder joint inspection in printed circuit boards through X-ray imaging and machine learning integration

This study combines machine learning (ML) and X-ray imaging to evaluate the health of solder joints in printed circuit boards (PCBs). A convolutional neural network (CNN) served as the base framework, with CNN-LSTM and CNN-CapsNet models added to enhance performance. Pre-training with the CNN facilitated feature extraction, boosting the subsequent performance of LSTM and CapsNet models. The research focused on three objectives: identifying the best ML model for limited datasets, addressing class imbalance in defective solder samples with data augmentation, and using image manipulation to assess model strengths and limitations. Data augmentation significantly improved model accuracies, with CNN, LSTM, and CapsNet achieving 87.05%, 91.29%, and 94.65%, respectively, compared to 76.23%, 83.32%, and 88.05% without augmentation. CapsNet outperformed other models, leveraging its dynamic routing mechanism to preserve feature hierarchies and maintain stable performance. LSTM demonstrated rapid learning through memory cells, while CNNs were prone to overfitting. CapsNet also excelled in balancing classification across solder types, highlighting its ability to handle complex feature relationships. Robustness tests showed CapsNet’s resilience to image transformations like rotation, scaling, and flipping, though extreme deformations remained challenging. These results underscore CapsNet’s potential for accurate and reliable solder joint classification in diverse scenarios.