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Machine Learning in Glass Bottle Printing Quality Control: A Collaboration with a Medium-Sized Industrial Partner
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Datum
2024
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Gesellschaft für Informatik e.V.
Zusammenfassung
In cooperation with a medium-sized industrial partner, we developed and evaluated two ML-based approaches for quality control in glass bottle printing. Our first approach utilized various filters to suppress reflections, image quality metrics for image comparison, and supervised classification models, resulting in an accuracy of 84%. We used the ORB algorithm for image alignment and to estimate print rotations, which may indicate manufacturing anomalies. In our second approach, we fine-tuned pre-trained CNN models, which resulted in an accuracy of 87%. Utilizing Grad-CAM, an Explainable AI method, we localized and visualized frequently defective bottle print regions without explicitly training our models for this use case. These insights can be used to optimize the actual manufacturing process beyond classification. This paper also describes our general approach and the challenges we encountered in practice with data collection during ongoing production, unsupervised preselection, and labeling.