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Deep-Learning Basierte Bestimmung Lokaler Mikrostrukturparameter an Duktilem Gusseisen

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Description

Die Bewertung von nuklearen Sicherheitsbauteilen erfordert ein hohes Maß an Genauigkeit und Zuverlässigkeit. Transportbehälter für abgebrannte Brennelemente bestehen überwiegend aus duktilem Gusseisen mit Kugelgraphit. Bei dieser Werkstoffgruppe muss dem mikrostrukturellen Einfluss auf die Rissbeständigkeit und damit auf die Bauteilzuverlässigkeit besondere Aufmerksamkeit geschenkt werden. Die manuelle Gefügebeurteilung anhand von Schliffbildern ist ein mühsamer und zeitaufwändiger Prozess, der viel Erfahrung erfordert. Außerdem enthalten die Schliffbilder im Gegensatz zur Bruchfläche nicht die lokalen Informationen über das Gefüge an der Bruchstelle, sondern an der Entnahmestelle. Lokale Gefügeparameter können in diesem Fall von besonderem Interesse sein, da diese innerhalb des Behälters aufgrund seiner großen Wandstärke stark variieren können. Im Rahmen dieses Beitrags wurde daher ein Framework zur automatisierten Analyse von Bruchflächen aus duktilem Gusseisen auf ihre Gefügeeigenschaften auf Basis von Deep Learning Algorithmen entwickelt. Ziel des Ansatzes ist es, die lokalen Eigenschaften der Bruchfläche, wie den Graphitvolumenanteil und die Partikelgröße, den Abstand, den Formfaktor und die Partikelanzahl über REM-Aufnahmen aus der Bruchfläche zu extrahieren, ohne dass eine Schliffbildpräparation erforderlich ist.

Reference: BR-2024-352

Title: Deep-Learning Basierte Bestimmung Lokaler Mikrostrukturparameter an Duktilem Gusseisen
Author(s): J. Rosenberger, J. Tlatlik, S. Münstermann
DOI: 10.48447/BR-2024-352
Event: 56. Tagung des DVM-Arbeitskreises Bruchmechanik und Bauteilsicherheit – Tagung 2024
Year of publication: 2024
Publication type: conference paper (PDF)
Language: German
Keywords: Deep Learning,Bruchmechanik,Duktiles Gusseisen,Segmentierung

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Mechanismen der Bruchflächenverdrehung bei dünnwandigen AA2024-T3 Aluminiumblechen

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€25.00

Online only

Author(s): M. Holzwarth, W. Baer, U. Mayer, S. Weihe

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