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doi:10.22028/D291-39416
Titel: | Learned Anomaly Detection with Terahertz Radiation in Inline Process Monitoring |
VerfasserIn: | Meiser, Clemens Wald, Anne Schuster, Thomas |
Sprache: | Englisch |
Titel: | Sensing and Imaging |
Bandnummer: | 23 |
Heft: | 1 |
Verlag/Plattform: | Springer Nature |
Erscheinungsjahr: | 2022 |
Freie Schlagwörter: | Terahertz radiation Terahertz tomography Inline monitoring Anomaly detection Learned defect detection Machine learning Nondestructive testing Supervised learning Gaussian distribution Eikonal equation |
DDC-Sachgruppe: | 510 Mathematik |
Dokumenttyp: | Journalartikel / Zeitschriftenartikel |
Abstract: | Terahertz tomographic imaging as well as machine learning tasks represent two emerging felds in the area of nondestructive testing. Detecting outliers in measurements that are caused by defects is the main challenge in inline process monitoring. An efcient inline control enables to intervene directly during the manufacturing process and, consequently, to reduce product discard. We focus on plastics and ceramics, for which terahertz radiation is perfectly suited because of its characteristics, and propose a density based technique to automatically detect anomalies in the measured radiation data. The algorithm relies on a classifcation method based on machine learning. For a verifcation, supervised data are generated by a measuring system that approximates an inline process. The experimental results show that the use of terahertz radiation, combined with the classifcation algorithm, has great potential for a real inline manufacturing process. In a further investigation additional data are simulated to enlarge the data set, especially the variety of defects. We model the propagation of terahertz radiation by means of the Eikonal equation. |
DOI der Erstveröffentlichung: | 10.1007/s11220-022-00402-5 |
URL der Erstveröffentlichung: | https://link.springer.com/article/10.1007/s11220-022-00402-5 |
Link zu diesem Datensatz: | urn:nbn:de:bsz:291--ds-394165 hdl:20.500.11880/35537 http://dx.doi.org/10.22028/D291-39416 |
ISSN: | 1557-2072 1557-2064 |
Datum des Eintrags: | 30-Mär-2023 |
Fakultät: | MI - Fakultät für Mathematik und Informatik |
Fachrichtung: | MI - Mathematik |
Professur: | MI - Prof. Dr. Thomas Schuster |
Sammlung: | SciDok - Der Wissenschaftsserver der Universität des Saarlandes |
Dateien zu diesem Datensatz:
Datei | Beschreibung | Größe | Format | |
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s11220-022-00402-5.pdf | 2,22 MB | Adobe PDF | Öffnen/Anzeigen |
Diese Ressource wurde unter folgender Copyright-Bestimmung veröffentlicht: Lizenz von Creative Commons