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2020 | OriginalPaper | Buchkapitel

Acoustic Resonance Testing of Glass IV Bottles

verfasst von : Ivan Kraljevski, Frank Duckhorn, Yong Chul Ju, Constanze Tschoepe, Matthias Wolff

Erschienen in: Artificial Intelligence Applications and Innovations

Verlag: Springer International Publishing

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Abstract

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.

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Titel: Acoustic Resonance Testing of Glass IV Bottles
verfasst von: Ivan Kraljevski
Frank Duckhorn
Yong Chul Ju
Constanze Tschoepe
Matthias Wolff
Verlag: Springer International Publishing
Buch: Artificial Intelligence Applications and Innovations
Print ISBN: 978-3-030-49185-7

Electronic ISBN: 978-3-030-49186-4

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-49186-4_17

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