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The main contribution of this paper is proposing an unsupervised learning-based pass/fail determination model for PCB image data. We constructed a vision inspection solution architecture that accepts image data as input, analyzes it using an artificial intelligence model, allows users to view results on a screen, and incorporates a distributed processing environment to accommodate increasing data volumes.
Two unsupervised learning models were selected and experimentally validated for quality inspection applicability, confirming that Dinomaly significantly outperforms PaDiM with an average F1-score of 98.8 percent.
However, we acknowledge that in actual manufacturing environments, there are limitations to relying solely on unsupervised learning for tasks such as defect type analysis and handling entirely new categories of defects. Therefore, even if unsupervised learning-based pass/fail determination is initially introduced, precise quality management will require advanced, supervised learning-based analysis using detailed data. Consequently, our future research will focus on technologies for autonomous application, including defect data augmentation and automated labeling. |
IEEE/ICACT20260190 Slide.19
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