TY - CONF A1 - Hemmleb, M. A1 - Bettge, Dirk A1 - Berger, D. A1 - Driehorst, I. T1 - 3D surface reconstruction with segmented BSE detector: New improvements and application for fracture analysis in SEM N2 - Application of 4Q-BSE detector and dedicated software enables 3D surface measurements of fracture samples in SEM. 3D surface measurement is independent from magnification and material invariant. Quantitative height measurements require careful detector adjustment and system calibration. Live 3D view allows a fast assessment of topographical features. In addition to SE and BSE imaging, 3D surface data help to analyse and interpret fracture samples. T2 - European Microscopy Congress 2016 CY - Lyon, France DA - 28.08.2016 KW - Topography KW - SEM KW - 3D analysis KW - Fractography PY - 2016 AN - OPUS4-38456 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Sonntag, Nadja A1 - Skrotzki, Birgit A1 - Stegemann, Robert A1 - Löwe, Peter A1 - Kreutzbruck, M. T1 - The role of surface topography on deformation-induced magnetization under inhomogeneous elastic-plastic deformation N2 - It is widely accepted that the magnetic state of a ferromagnetic material may be irreversibly altered by mechanical loading due to magnetoelastic effects. A novel standardized nondestructive testing (NDT) technique uses weak magnetic stray fields, which are assumed to arise from inhomogeneous deformation, for structural health monitoring (i.e., for detection and assessment of damage). However, the mechanical and microstructural complexity of damage has hitherto only been insufficiently considered. The aim of this study is to discuss the phenomenon of inhomogeneous “self-magnetization” of a polycrystalline ferromagnetic material under inhomogeneous deformation experimentally and with stronger material-mechanical focus. To this end, notched specimens were elastically and plastically deformed. Surface magnetic states were measured by a three-axis giant magnetoresistant (GMR) sensor and were compared with strain field (digital image correlation) and optical topography measurements. It is demonstrated that the stray fields do not solely form due to magnetoelastic effects. Instead, inhomogeneous plastic deformation causes topography, which is one of the main origins for the magnetic stray field formation. Additionally, if not considered, topography may falsify the magnetic signals due to variable lift-off values. The correlation of magnetic vector components with mechanical tensors, particularly for multiaxial stress/strain states and inhomogeneous elastic-plastic deformations remains an issue. KW - Magnetic stray fields KW - Magnetomechanical effect KW - Damage KW - Topography KW - Multiaxial deformation KW - Notch KW - Plastic deformation KW - Metal magnetic memory KW - Digital image correlation KW - Structural steel PY - 2018 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-457878 SN - 1996-1944 VL - 11 IS - 9 SP - 1518, 1 EP - 26 PB - MDPI CY - Basel, Switzerland AN - OPUS4-45787 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - 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 -