Classification of fracture surface types based on SEM images
- The following work deals with the quantitative fracture surface evaluation in damage analysis. So far, fracture surfaces have almost exclusively been evaluated qualitatively, i.e. the presence of fracture features is documented and their surface proportions are estimated, if necessary. Many years of experience are required, as well as an intensive comparison with defined comparative images from the literature. The aim of this work is the development of classifiers which can recognize fracture mechanisms or fracture features in scanning electron microscope images (SEM). The basis is 46 SEM images, which have been evaluated by fractography experts with regard to fracture features. The existing data set of images is expanded using augmentation methods in order to increase the variability of the data and counteract overfitting. Only convolutional neural networks (CNN) are used to create the classifiers. Various network configurations are tested, with the SegNet achieving the best results.
Autor*innen: | B Botsch |
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Koautor*innen: | U Sonntag, Dirk BettgeORCiD, Quynh Hoa Le, Lennart Schmies, Anna Yarysh |
Dokumenttyp: | Vortrag |
Veröffentlichungsform: | Präsentation |
Sprache: | Englisch |
Jahr der Erstveröffentlichung: | 2021 |
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: | Deep learning; Fractography; Fracture surface; SEM |
Themenfelder/Aktivitätsfelder der BAM: | Material |
Material / Degradation von Werkstoffen | |
Veranstaltung: | Materialsweek 2021 |
Veranstaltungsort: | Online meeting |
Beginndatum der Veranstaltung: | 07.09.2021 |
Enddatum der Veranstaltung: | 09.09.2021 |
Verfügbarkeit des Dokuments: | Datei im Netzwerk der BAM verfügbar ("Closed Access") |
Datum der Freischaltung: | 29.09.2021 |
Referierte Publikation: | Nein |
Eingeladener Vortrag: | Nein |