TY - CONF A1 - Schmies, Lennart A1 - Bettge, Dirk A1 - Yarysh, Anna A1 - Sonntag, U. A1 - Botsch, B. A1 - Hemmleb, M. T1 - Using Machine Learning and Topographic SEM Imaging for Software Assisted Fractography N2 - 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, 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. T2 - Material Science and Engineering 2022 CY - Darmstadt, Germany DA - 27.9.2022 KW - Fractography KW - Machine Learning KW - Topography PY - 2022 AN - OPUS4-55938 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -