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Using Machine Learning and Topographic SEM Imaging for Software Assisted Fractography

  • The aim of a fractographic investigation is the evaluation of macroscopic and microscopic fracture surface characteristics and, as a result, the determination of the fracture mechanism of a component from a failure case. The basis for such evaluations of fracture characteristics comes from actual comparative mechanical testing and from the literature. A fractographic analysis can be very complex and, in any case, requires considerable experience. In the IGF project "iFrakto", software is being developed that quantitatively determines fracture characteristics and fracture mechanisms utilizing digitized expert knowledge, machine learning, and standard 2D and topographical data from SEM imaging. Topographical data are obtained from 4QBSE detector using shape-from-shading technology. In the medium term, a software tool should provide knowledge-based suggestions for the evaluation of fracture surfaces in real time during SEM work or at subsequent evaluation. As a basis for this, roundThe aim of a fractographic investigation is the evaluation of macroscopic and microscopic fracture surface characteristics and, as a result, the determination of the fracture mechanism of a component from a failure case. The basis for such evaluations of fracture characteristics comes from actual comparative mechanical testing and from the literature. A fractographic analysis can be very complex and, in any case, requires considerable experience. In the IGF project "iFrakto", software is being developed that quantitatively determines fracture characteristics and fracture mechanisms utilizing digitized expert knowledge, machine learning, and standard 2D and topographical data from SEM imaging. Topographical data are obtained from 4QBSE detector using shape-from-shading technology. In the medium term, a software tool should provide knowledge-based suggestions for the evaluation of fracture surfaces in real time during SEM work or at subsequent evaluation. As a basis for this, round robins were carried out among fractographers in order to create a knowledge base, to query the practice-relevant requirements for such tools and to carry out first practical tests. Actual results are presented and the relevance of the evaluation strategy is evaluated.zeige mehrzeige weniger

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Metadaten
Autor*innen:Lennart Schmies
Koautor*innen:Dirk Bettge, Anna Yarysh, U. Sonntag, B. Botsch, M. Hemmleb
Dokumenttyp:Vortrag
Veröffentlichungsform:Präsentation
Sprache:Englisch
Jahr der Erstveröffentlichung:2022
Organisationseinheit der BAM:5 Werkstofftechnik
5 Werkstofftechnik / 5.1 Mikrostruktur Design und Degradation
DDC-Klassifikation:Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurwissenschaften und zugeordnete Tätigkeiten
Freie Schlagwörter:Fractography; Machine Learning; Topography
Themenfelder/Aktivitätsfelder der BAM:Material
Veranstaltung:Material Science and Engineering 2022
Veranstaltungsort:Darmstadt, Germany
Beginndatum der Veranstaltung:27.9.2022
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:10.10.2022
Referierte Publikation:Nein
Eingeladener Vortrag:Nein
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