Toward Optimal Defect Detection in Assembled Printed Circuit Boards Under Adverse Conditions

Abstract

Defects in the printed circuit board assembly significantly impact product functionality and quality. Automated optical inspection (AOI) systems, employed by manufacturing quality control teams, are designed to accurately detect these defects in a timely manner, thereby reducing the underkill (false negatives) and overkill (false positives) rates. An AOI system requires optimal settings for resolution, brightness, camera angle, and data variety to ensure effective defect detection. However, consistently achieving these ideal conditions in a manufacturing environment presents challenges. Our proposed framework enhances defect detection through data preparation and detection modules, effectively addressing these manufacturing challenges. We developed one- and two-stage object detectors and assessed their performance using precision, recall, and intersection over union metrics. Our framework employs a diverse range of augmentation techniques to effectively train the defect detectors, enabling the expansion of a limited data set. The trained detectors are evaluated using real-world data. We assessed quality control plans across various confidence thresholds. At a 65% confidence threshold, one-stage detector models did not exhibit any false negatives and had minimal false positives. The You Only Learn One Representation (YOLOR) model outperformed both one-stage and two-stage detectors, achieving 100% precision and recall, a 96% mIoU, and an impressive inference time of 11 ms, making it an ideal choice for high-production printed circuit board assembly lines.

Keywords

• Computer vision
• faster-R-CNN model
• image augmentation techniques
• one-stage and two-stage defect detectors
• PCBA defect detection framework
• printed circuit board defects