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 - TY - CONF A1 - Hemmleb, M. A1 - Bettge, Dirk A1 - Schmies, Lennart A1 - Sonntag, U. A1 - Botsch, B. T1 - Integrated 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. Machine learning methods enables the quantitative determination of fracture characteristics and fracture mechanisms utilizing digitized expert knowledge [1]. Although the application of SE images provides promising results, additional information is required to obtain reliable solutions. As expected, BSE and 3D information helps to improve the classification (Fig. 1). But only a fast, widely integrated, and automated topography measurement can provide the required amount of referenced surface data for the application of machine learning methods. To fulfil these requirements, topographical data are obtained from a BSE detector with four symmetric segments (4Q-BSE) using shape-from-shading technology [2]. Surface height calculation is performed live during image acquisition and provides immediate feedback in three dimensions. All available signals (SE, BSE and more if applicable) are recorded simultaneously together with the surface topography and stored in a multichannel data file. This guaranties the same geometrical reference for all data, which is required for further analysis (Fig. 2). When applying machine learning methods to topographic data together with SEM images, topographic information must be provided as depth image. Consequently, a unique height scale is required for all applied data with different magnifications. This requires a calibrated height measurement, which is ensured with the integrated 3D calibration of the topographic acquisition and a dedicated calibration sample. Thus, a large number of data sets from different fracture samples was generated and used as training data for machine learning. T2 - 16th Multinational Congress on Microscopy CY - Brno, Czech Republic DA - 04.09.2022 KW - Fractography KW - Machine Learning KW - Topography PY - 2022 AN - OPUS4-55937 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Sonntag, U. A1 - Botsch, B. A1 - Bettge, Dirk A1 - Schmies, Lennart A1 - Yarysh, Anna A1 - Hemmleb, M. T1 - Softwaregestützte Bestimmung von Bruchmechanismen und Bruchflächenmerkmalen mittels Machine Learning und Topographie-Informationen N2 - Ziel von fraktographischen Untersuchungen ist die Auswertung von makroskopischen und mikroskopischen Bruchflächenmerkmalen und daraus resultierend die Bestimmung des Bruchmechanismus eines Bauteils aus einem Schadensfall. Die Grundlage für die Bewertung von Bruchmerkmalen stammt dabei aus zuvor durchgeführten eigenen Vergleichsversuchen und aus der Literatur. Eine fraktographische Analyse kann sehr aufwändig sein und setzt in jedem Fall erhebliches Erfahrungswissen voraus. Im IGF-Vorhaben „iFrakto“ wird Software entwickelt, die auf Basis von digitalisiertem Expertenwissen, Machine Learning und unter Zuhilfenahme von Topographie-Daten anhand von REM-Aufnahmen Bruchmerkmale und Bruchmechanismen quantitativ bestimmt, s. Abbildung. Solche Software soll mittelfristig direkt am REM oder bei der nachfolgenden Auswertung wissensbasierte Vorschläge für die Bewertung von Bruchflächen liefern. Als Grundlage hierfür wurden Ringversuche unter Fraktograph/inn/en durchgeführt, um die Wissensbasis zu schaffen, die praxisrelevanten Anforderungen an solche Software abzufragen und erste Praxistests durchzuführen. Die bisherigen Ergebnisse werden vorgestellt und die Relevanz der Auswertestrategie und unterschiedlicher Sensordaten bewertet. T2 - 56. Metallographie-Tagung CY - Saarbrücken, Germany DA - 21.9.2022 KW - Fraktographie KW - Machine Learning KW - Topographie PY - 2022 AN - OPUS4-55939 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -