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dc.contributor.authorPierer, Alexander
dc.contributor.authorHauser, Markus
dc.contributor.authorHoffmann, Michael
dc.contributor.authorNaumann, Martin
dc.contributor.authorWiener, Thomas
dc.contributor.authorLara de Léon, Melvin Alexis
dc.contributor.authorMende, Mattias
dc.contributor.authorKoziorek, Jiří
dc.contributor.authorDix, Martin
dc.date.accessioned2023-03-03T08:21:18Z
dc.date.available2023-03-03T08:21:18Z
dc.date.issued2022
dc.identifier.citationSensors. 2022, vol. 22, issue 24, art. no. 9646.cs
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10084/149170
dc.description.abstractPerfectly coated surfaces are an essential quality feature in the automotive and consumer goods industries. They are the result of an optimized, controlled coating process. Because entire assemblies could be rejected if Out-of-Specification (OOS) parts are installed, this has a severe economic impact. This paper presents a novel, line-integrated multi-camera system with intelligent algorithms for anomaly detection on small KTL-coated aluminum parts. The system also aims to automatize the previously used human inspection to a sophisticated and automated vision system that efficiently detects defects and anomalies on coated parts.cs
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofseriesSensorscs
dc.relation.urihttps://doi.org/10.3390/s22249646cs
dc.rights© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectquality controlcs
dc.subjectcoatingcs
dc.subjectextrusioncs
dc.subjectfailurecs
dc.subjectneural networkscs
dc.titleInline quality monitoring of reverse extruded aluminum parts with cathodic dip-paint coating (KTL)cs
dc.typearticlecs
dc.identifier.doi10.3390/s22249646
dc.rights.accessopenAccesscs
dc.type.versionpublishedVersioncs
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume22cs
dc.description.issue24cs
dc.description.firstpageart. no. 9646cs
dc.identifier.wos000904308200001


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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Except where otherwise noted, this item's license is described as © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.