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dc.contributor.authorPajić, Nemanja-
dc.contributor.authorAleksić, Jovana-
dc.contributor.authorZivic, Fatima-
dc.contributor.authorDjordjevic, Aleksandar-
dc.date.accessioned2023-06-14T12:17:08Z-
dc.date.available2023-06-14T12:17:08Z-
dc.date.issued2023-
dc.identifier.isbn978-86-6335-104-2en_US
dc.identifier.urihttps://scidar.kg.ac.rs/handle/123456789/18409-
dc.description.abstractThe purpose of this paper is to explore conventional laser welding quality control methods and compare them with modern AI-based (Artificial Inteligence) testing solutions, highlighting the potential of AI in laser welding quality assurance. AI can effectively monitor various laser welding process signals and parameters to determine weld quality. Furthermore, AI image recognition can enhance weld error detection precision when monitoring laser welding with vision systems. In cases where conventional quality control methods, such as X-ray, are utilized, AI can be employed to process and interpret test results, reducing the time and effort required for a human operator. This paper presents and briefly discusses several successful AI application examples in laser welding quality assurance, as well as application possibilities, demonstrating the latest state-of-the-art non destructive laser welding test solutions.en_US
dc.language.isoenen_US
dc.publisherFaculty of Engineering, University of Kragujevac, Serbiaen_US
dc.rightsinfo:eu-repo/semantics/openAccess-
dc.source14th International Quality Conference, Quality Festival 2023en_US
dc.subjectArtificial Intelligenceen_US
dc.subjectLaser Weldingen_US
dc.subjectQuality Assuranceen_US
dc.subjectNon-Destructive Testingen_US
dc.titleAI APPLICATION IN QUALITY ASSURANCE OF INDUSTRIAL LASER WELDING PROCESSESen_US
dc.typeconferenceObjecten_US
dc.description.versionPublisheden_US
dc.type.versionPublishedVersionen_US
Налази се у колекцијама:Faculty of Engineering, Kragujevac

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