In printed electronics, flawless printing quality is crucial for electronic device fabrication. While printing defects may reduce the performance or even cause a failure in the electronic device, there is a challenge in quality evaluation using conventional computer vision tools for printing defect recognition. This study proposed the computer vision approach based on artificial intelligence (AI) and deep convolutional neural networks. First, the data set with printed line images was collected and labeled. Second, the overall printing quality classification model was trained and evaluated using the Grad-CAM visualization technique. Third and last, the pretrained object detection model YOLOv3 was fine-tuned for local printing defect detection. Before fine-tuning, ground truth bounding boxes were analyzed, and anchor box sizes were chosen using the k-means clustering algorithm. The overall printing quality and local defect detection AI models were integrated with the roll-based gravure offset system. This AI approach is also expected to complement more accurate printing reliability analysis firmly.
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