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Acoustic resonance testing of glass IV bottles

: Kraljevski, Ivan; Duckhorn, Frank; Ju, Yong Chul; Tschöpe, Constanze; Wolff, Matthias


Maglogiannis, Ilias (Ed.) ; International Federation for Information Processing -IFIP-:
Artificial Intelligence Applications and Innovations. Proceedings. Pt.II : 16th IFIP WG 12.5 International Conference, AIAI 2020, Neos Marmaras, Greece, June 5-7, 2020
Cham: Springer International Publishing, 2020 (IFIP advances in information and communication technology 584)
ISBN: 978-3-030-49185-7 (Print)
ISBN: 978-3-030-49186-4 (Online)
ISBN: 978-3-030-49187-1
ISBN: 978-3-030-49188-8
International Conference on Artificial Intelligence Applications and Innovations (AIAI) <16, 2020, Neos Marmaras>
Conference Paper
Fraunhofer IKTS ()
acoustic resonance testing; machine learning; glass IV bottles; non-destructive testing

In this paper, acoustic resonance testing on glass intravenous (IV) bottles is presented. Different machine learning methods were applied to distinguish acoustic observations of bottles with defects from the intact ones. Due to the very limited amount of available specimens, the question arises whether the deep learning methods can achieve similar or even better detection performance compared with traditional methods. The results from the binary classification experiments are presented and compared in terms of Balanced Accuracy Rate, F1-score, Area Under the Receiver Operating Characteristic Curve and Matthews Correlation Coefficient metrics. The presented feature analysis and the employed classifiers achieved solid results, despite the rather small and imbalanced dataset with a highly inconsistent class population.