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Unüberwachtes Maschinelles Lernen an Bruchflächen im Kontext des Kerbschlagbiegeversuchs

Instrumentierte Pendelschlagversuche liefern wertvolle Erkenntnisse über das Bruchverhalten unter schlagartigen Beanspruchungsbedingungen. Jedoch erfordert diese Versuchstechnik zusätzliche Prüfgeräte, Kalibrierungsverfahren und Auswerteverfahren. Ziel dieser Studie ist die Korrelation des Kraft-Weg-Verhaltens mit Bildinformationen in Form von Bruchflächen aus instrumentierten Charpy-Kerbschlagbiegeversuchen mittels Methoden des maschinellen Lernens. Durch den Einsatz von unüberwachten Trainingsmethoden werden die Bruchflächen von vier Stählen klassifiziert und anschließend auf Zusammenhänge zu den Eigenschaften der Kraft-Weg-Kurven hin untersucht. Zusätzlicher Fokus wird dabei auf eine Interpretierbarkeit der Modellentscheidungen gelegt. Die dargestellten Methoden erlauben…

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Reference: BR-2025-488

Title: Unüberwachtes Maschinelles Lernen an Bruchflächen im Kontext des Kerbschlagbiegeversuchs
Author(s): J. Rosenberger, J. Tlatlik, S. Münstermann
DOI: 10.48447/BR-2025-488
Event: 57. Tagung des DVM-Arbeitskreises Bruchmechanik und Bauteilsicherheit – Tagung 2025
Year of publication: 2025
Publication type: conference paper (PDF)
Language: German
Keywords: Machine Learning,Kerbschlagbiegeversuch,Fraktographie

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Berücksichtigung umforminduzierter Schädigung in der Bauteilauslegung

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€25.00

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Crack path prediction on integrated raceways under RCF

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Integration von digitalen und experimentellen Methoden zur Analyse des Ermüdungsfortschrittshaltens in komplexen dreidimensionalen Blechbauteilen am Beispiel von L-Profilen

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