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Automatisierte Analyse von Bruchflächen mittels künstlicher neuronaler Netzwerke (KNN) für kerntechnisch relevante ...

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Description

Für die sichere Bewertung von kerntechnisch relevanten Sicherheitskomponenten ist eine präzise und zuverlässige Analyse der Bruchfläche nach erfolgtem Prüfvorgang erforderlich. Die Anforderungen an die Analyse sind dabei von der jeweiligen Zielgröße abhängig und in entsprechenden Regelwerken (z.B. ASTM E399, E1820 und E1921) festgelegt. Die Klassifizierung der Bruchmechanismen stellt dabei einen zeitaufwendigen Arbeitsvorgang dar, der stark von der subjektiven Wahrnehmung des Technikers und den vorliegenden Arbeitsbedingungen abhängig ist. Das Ziel der automatisierten Analyse und Ausmessung von Bruchflächen mit Hilfe von künstlichen neuronalen Netzen besteht darin, eine hohe Qualität, Vergleichbarkeit und vor allem Wirtschaftlichkeit der Auswertung der Bruchflächen zu gewährleisten.
Nach der Identifikation einer geeigneten Netzwerkarchitektur und der aufwendigen Aufbereitung eines Datensatzes (Digitalkameraaufnahmen von 22 NiMoCr3-7 und dessen Schweißgut) konnten erfolgreich Modelle zur pixelweisen Klassifikation von Bruchflächen trainiert werden. Hierbei konnten auf einem Testdatensatz hohe mIoU-Werte erzielt werden. Die automatisierte Vermessung der Bruchflächen erweist sich als hochpräzise, so dass die ermittelten Anrisslängen zur Bestimmung von bruchmechanischen Kennwerten genutzt werden können.
Im Rahmen des Beitrags soll der Entwicklungsprozess, die Stärken und Schwächen des Modellansatzes, sowie weitere Arbeiten in diesem Zusammenhang vorgestellt und diskutiert werden. Darüber hinaus werden derzeit die bisher erarbeiteten Methoden für die Analyse von REM-Bruchflächenbildern weiterentwickelt, die aufgrund der höheren Vergrößerungsstufe andere Untersuchungsschwerpunkte aufweisen.

Reference: BR-2023-002

Title: Automatisierte Analyse von Bruchflächen mittels künstlicher neuronaler Netzwerke (KNN) für kerntechnisch relevante Sicherheitskomponenten
Author(s): J. Rosenberger, J. Tlatlik, S. Münstermann
DOI: 10.48447/BR-2023-002
Event: 55th Conference of the Working Group on Fracture Mechanics and Structural Safety
Year of publication: 2023
Publication type: conference paper (PDF)
Language: German
Keywords: KNN,Image Segmentation,Bruchmechanik,Automatisierte Bildverarbeitung,Versuchsauswertung

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Author(s): J. A. Schönherr, M. Klein, M. Oechsner

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Auswertung von Kraft-Weg Daten zur Detektion von Rissschließen im Schwellenwertbereich

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ERWEITERUNG DES FESTIGKEITSNACHWEISES NACH ÖRTLICHEM KONZEPT ZUR BERÜCKSICHTIGUNG VON RANDSCHICHTBEHAFTETEN BAUTEILEN

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Editor(s): M. Vormwald

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Bruchmechanische Lebensdauer-Prognose für additive gefertigte Aluminium Komponenten

Publication type: conference paper (PDF)

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1

German

€25.00

Online only

Author(s): I. Varfolomeev, C. Amann

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1

Einfluss von Constraint auf duktile Rissinitiierung und duktiles Risswachstum

Publication type: conference paper (PDF)

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1

German

€25.00

Online only

Author(s): A. Tsakmakis, M. Vormwald

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1

Rissspitzenfelder in Phasenfeldmodellierungen von duktilen Bruchprozessen

Publication type: conference paper (PDF)

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1

German

€25.00

Online only

Author(s): M. Neite, M. Dölz, M. Könemann, S. Münstermann

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1

Mikrostruktursensitive Beschreibung der Restbeanspruchbarkeit von Komponenten mit Hilfe virtueller Experimente

Publication type: conference paper (PDF)

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1

German

€25.00

Online only

Author(s): U. Hähnel, P. Hübner, B. Kranz

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Bruchmechanische Bewertung hochfester Schweißverbindungen für den Mobilkranbau unter Berücksichtigung innerer Unregelmäßigkeiten

Publication type: conference paper (PDF)

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1

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€25.00

Online only

Author(s): G. Kullmer, D. Weiß, B. Schramm

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1

German

€25.00

Online only

Author(s): P. Köster, C. Benz, M. Sander

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€25.00

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Author(s): C. Fällgren, H. T. Beier, M. Vormwald

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1

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€25.00

Online only

Author(s): J. Kraft, M. Vormwald

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1

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€25.00

Online only

Author(s): W. Radlof, C. Benz, M. Sander

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1

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€25.00

Online only

Author(s): V. Schöne, F. Paysan, D. Melching, T. Strohmann, E. Breitbarth

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1

German

€25.00

Online only