TY - CONF A1 - Botsch, B A1 - Sonntag, U A1 - Bettge, Dirk A1 - Le, Quynh Hoa A1 - Schmies, Lennart A1 - Yarysh, Anna T1 - Classification of fracture surface types based on SEM images N2 - 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. T2 - Materialsweek 2021 CY - Online meeting DA - 07.09.2021 KW - Fractography KW - Fracture surface KW - Deep learning KW - SEM PY - 2021 AN - OPUS4-53418 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -