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Ermüdungs- und Kerbwirkungsbewertung additiv gefertigter AISI 316L-Proben mittels physikalisch informierten maschinellen Lernverfahren

Die additive Fertigung, insbesondere das Laserstrahlschmelzen (L-PBF), bietet große Vorteile, verursacht jedoch prozessbedingte Defekte wie Poren, Bindefehler und Oberflächenunregelmäßigkeiten, die die Ermüdungsfestigkeit verringern. Zur Bewertung dieser Einflüsse werden Methoden des maschinellen Lernens (ML) und physikalisch informierte ML-Modelle (PIML) eingesetzt. PIML kombiniert Gradient Boosted Trees mit den physikalischen Modellen nach Paris und Murakami, um die Lebensdauer von L-PBF-gefertigten AISI 316L-Proben vorherzusagen. Die Modelle werden mit beobachteten Defektgrößen trainiert und können künftig mit CT-Daten zur Lebensdauervorhersage genutzt werden.

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Reference: ADD-2025-680

Title: Ermüdungs- und Kerbwirkungsbewertung additiv gefertigter AISI 316L-Proben mittels physikalisch informierten maschinellen Lernverfahren
Author(s): X. Wang, M. Braun, J. Schubnell
DOI: 10.48447/ADD-2025-680
Event: DVM-Arbeitskreis Arbeitskreis Additiv gefertigte Bauteile und Strukturen - Tagung 2025
Year of publication: 2025
Publication type: conference paper (PDF)
Language: German
Keywords: Ermüdungsfestigkeit,Additive Fertigung,physikalisch informiertes maschinelles Lernen,Gradient Boosted Trees

3 other products in the same category:

Author(s): F. Keil, J. Schubnell, J. Radners, A. Heiß, S. Fliegener, O. Maiß

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Steigerung der Ermüdungsfestigkeit von AM-Bauteilen durch mechanische Oberflächenbehandlung

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€25.00

Online only New

Author(s): M. A. Hoffmann, B. Blinn, T. Beck, M. Faßhauer, T. Hassel

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