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Machine Learning in Glass Bottle Printing Quality Control: A Collaboration with a Medium-Sized Industrial Partner

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dc.contributor.authorBundscherer, Maximilian
dc.contributor.authorSchmitt, Thomas H.
dc.contributor.authorBocklet, Tobias
dc.contributor.editorKlein, Maike
dc.contributor.editorKrupka, Daniel
dc.contributor.editorWinter, Cornelia
dc.contributor.editorGergeleit, Martin
dc.contributor.editorMartin, Ludger
dc.date.accessioned2024-10-21T18:24:15Z
dc.date.available2024-10-21T18:24:15Z
dc.date.issued2024
dc.description.abstractIn 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.en
dc.identifier.doi10.18420/inf2024_147
dc.identifier.eissn2944-7682
dc.identifier.isbn978-3-88579-746-3
dc.identifier.issn2944-7682
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/45122
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofINFORMATIK 2024
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-352
dc.subjectML
dc.subjectQuality Control
dc.subjectIndustrial Manufacturing Optimization
dc.subjectGlass Printing
dc.titleMachine Learning in Glass Bottle Printing Quality Control: A Collaboration with a Medium-Sized Industrial Partneren
dc.typeText/Conference Paper
gi.citation.endPage1704
gi.citation.publisherPlaceBonn
gi.citation.startPage1691
gi.conference.date24.-26. September 2024
gi.conference.locationWiesbaden
gi.conference.sessiontitleKünstliche Intelligenz im Mittelstand / KI-KMU2024

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