TY - JOUR A1 - Jablonka, Kevin Maik A1 - Ai, Qianxiang A1 - Al-Feghali, Alexander A1 - Badhwar, Shruti A1 - Bocarsly, Joshua D. A1 - Bran, Andres M. A1 - Bringuier, Stefan A1 - Brinson, L. Catherine A1 - Choudhary, Kamal A1 - Circi, Defne A1 - Cox, Sam A1 - de Jong, Wibe A. A1 - Evans, Matthew L. A1 - Gastellu, Nicolas A1 - Genzling, Jerome A1 - Gil, María Victoria A1 - Gupta, Ankur K. A1 - Hong, Zhi A1 - Imran, Alishba A1 - Kruschwitz, Sabine A1 - Labarre, Anne A1 - Lála, Jakub A1 - Liu, Tao A1 - Ma, Steven A1 - Majumdar, Sauradeep A1 - Merz, Garrett W. A1 - Moitessier, Nicolas A1 - Moubarak, Elias A1 - Mouriño, Beatriz A1 - Pelkie, Brenden A1 - Pieler, Michael A1 - Ramos, Mayk Caldas A1 - Ranković, Bojana A1 - Rodriques, Samuel G. A1 - Sanders, Jacob N. A1 - Schwaller, Philippe A1 - Schwarting, Marcus A1 - Shi, Jiale A1 - Smit, Berend A1 - Smith, Ben E. A1 - Van Herck, Joren A1 - Völker, Christoph A1 - Ward, Logan A1 - Warren, Sean A1 - Weiser, Benjamin A1 - Zhang, Sylvester A1 - Zhang, Xiaoqi A1 - Zia, Ghezal Ahmad Jan A1 - Scourtas, Aristana A1 - Schmidt, K. J. A1 - Foster, Ian A1 - White, Andrew D. A1 - Blaiszik, Ben T1 - 14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon N2 - Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines. KW - Large Language model KW - Hackathon KW - Concrete KW - Prediction KW - Inverse Design KW - Orchestration PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-589961 DO - https://doi.org/10.1039/d3dd00113j VL - 2 IS - 5 SP - 1233 EP - 1250 PB - Royal Society of Chemistry (RSC) AN - OPUS4-58996 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Munsch, Sarah Mandy T1 - 1H NMR Relaxometry: A Reliable Tool for Predicting Durability Properties? N2 - Resistance to carbonation and chloride migration are critical durability properties in cementitious construction materials. The ingress of CO2 and chlorides can lead to concrete deterioration and reinforcement corrosion, underscoring the importance of ensuring high resistance to these durability-reducing factors. Traditional methods, such as determinating the carbonation coefficient using standard procedures, are time-consuming and resource-intensive. These tests typically require 42 days of preconditioning, followed by 70 days before the final results can be determined using a phenolphthalein test, for a total duration of 112 days. In addition, some test chambers can only accommodate four samples at a time. In this study, we investigate whether 1H NMR relaxometry can effectively predict not only the carbonation resistance, KAC,3%, but also the chloride migration coefficient, DRCM, and the compressive strength, fc, of concrete, providing a method that could potentially streamline and accelerate the material development process by directing us quickly to potentially well-suited formulations. We performed measurements on non-carbonated fully saturated concrete and mortar samples at 28, 56, and 92 days using the NMR tomograph at BAM (8.9 MHz). Notably, the NMR measurements taken at 28 days showed no significant differences, suggesting that this timeframe is sufficient for meaningful results. The samples analyzed were drill cores, each 70 mm in diameter and approximately 120 mm in height. The NMR features we focused on included the initial amplitude E0, the x and y values of the dominant T2 relaxation time, and the logarithmic mean of the T2 relaxation time distribution. Another key feature was the surface relaxivity, which was determined by comparing the NMR curves with results from mercury intrusion porosimetry. We examined the correlation of these NMR features with the aforementioned durability properties, which were determined in a laboratory on sister samples using standard procedures. No chloride migration tests were performed on the mortar samples, and the carbonation procedure was carried out unter atmospheric conditions. The preliminary results show that the correlation factors for the NMR features with the carbonation coefficients of concrete were particularly remarkable. The strongest correlations were observed for the dominant relaxation time and the logarithmic mean, with values approaching 1. The preliminary results also indicate that there are high correlations between the compressive strength and amplitude-related features. In contrast, no strong correlations are observed for the DRCM. Although the results related to the mortar samples are still being analyzed, these initial correlations suggest that 1H NMR relaxometry could be a valuable tool for early assessment of material properties. Furthermore, the rapid and non-destructive nature of NMR measurements, requiring only a few minutes per sample, suggests that this technique has the potential to significantly accelerate the process of evaluating durability properties in cementitious materials. This capability also paves the way for the use of NMR features as input for AI-driven predictive models. T2 - Magnetic Resonance in Porous Media 2024 CY - Tromso, Norway DA - 26.08.2024 KW - Nuclear magnetic resonance KW - SLAMD KW - Carbonation KW - Durability PY - 2024 AN - OPUS4-61058 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Lecompagnon, Julien A1 - Ahmadi, Samim A1 - Hirsch, Philipp Daniel A1 - Ziegler, Mathias T1 - 2D-Photothermal Super Resolution with Sparse Matrix Stacking N2 - Thermographic super resolution techniques allow the spatial resolution of defects/inhomogeneities below the classical limit, which is governed by the diffusion properties of thermal wave propagation. In this work, we report on the extension of this approach towards a full frame 2D super resolution technique. The approach is based on a repeated spatially structured heating using high power lasers. In a second post-processing step, several measurements are coherently combined using mathematical optimization and taking advantage of the (joint) sparsity of the defects in the sample T2 - Sensor and Measurement Science International Conference SMSI 2021 CY - Online meeting DA - 03.05.2021 KW - Thermography KW - Super-resolution KW - NDT KW - Inspection KW - Image resolution PY - 2021 SN - 978-3-9819376-4-0 DO - https://doi.org/10.5162/SMSI2021/C2.2 VL - SMSI 2021 - Sensors and Instrumentation SP - 183 EP - 184 AN - OPUS4-52589 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Lecompagnon, Julien T1 - 2D-Photothermal super-resolution with sparse matrix stacking N2 - Thermographic super resolution techniques allow the spatial resolution of defects/inhomogeneities below the classical limit, which is governed by the diffusion properties of thermal wave propagation. In this work, we re-port on the extension of this approach towards a full frame 2D super resolution technique. The approach is based on a repeated spatially structured heating using high power lasers. In a second post-processing step, several measurements are coherently combined using mathematical optimization and taking advantage of the (joint) sparsity of the defects in the sample. T2 - Sensor and Measurement Science International Conference SMSI 2021 CY - Online meeting DA - 03.05.2021 KW - Thermography KW - Super-resolution KW - NDT KW - Inspection KW - Image resolution PY - 2021 AN - OPUS4-52579 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Karapanagiotis, Christos T1 - 2. FOSsure-Projekttreffen: Ergebnisse aus den Verkehrsmonitoring-Messkampagnen T1 - 2nd FOSsure Project meeting: Results from the bridge traffic monitoring campaign N2 - We present distributed fiber optic results from the traffic monitoring measurement campaign on a newly constructed bridge in Brandenburg, which took place on 01.10.2025. Specifically, we employed a distributed acoustic sensing (DAS) system patented and developed by BAM and made use of optical fibers, which were embedded in the girders of the bridge. We show that our DAS system can capture vehicle flows along the bridge, including a) the number of vehicles, b) the number of axles, and c) estimates of vehicles' weight. T2 - 2. FOSsure Projekttreffen CY - Vienna, Germany DA - 16.10.2025 KW - Fiber optic sensors KW - Distributed acoustic sensing KW - Traffic load monitoring KW - Bridge monitoring PY - 2025 AN - OPUS4-64950 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Lay, Vera A1 - Buske, S. A1 - Townend, J. A1 - Kellett, R. A1 - Savage, M. A1 - Schmitt, D. R. A1 - Constantinou, A. A1 - Eccles, J. D. A1 - Gorman, A. A1 - Bertram, M. A1 - Hall, K. A1 - Lawton, D. A1 - Kofman, R. T1 - 3D Active Source Seismic Imaging of the Alpine Fault Zone and the Whataroa Glacial Valley in New Zealand N2 - The Alpine Fault zone in New Zealand marks a major transpressional plate boundary that is late in its typical earthquake cycle. Understanding the subsurface structures is crucial to understand the tectonic processes taking place. A unique seismic survey including 2D lines, a 3D array, and borehole recordings, has been performed in the Whataroa Valley and provides new insights into the Alpine Fault zone down to ∼2 km depth at the location of the Deep Fault Drilling Project (DFDP)-2 drill site. Seismic images are obtained by focusing prestack depth migration approaches. Despite the challenging conditions for seismic imaging within a sediment filled glacial valley and steeply dipping valley flanks, several structures related to the valley itself as well as the tectonic fault system are imaged. A set of several reflectors dipping 40°–56° to the southeast are identified in a ∼600 m wide zone that is interpreted to be the minimum extent of the damage zone. Different approaches image one distinct reflector dipping at ∼40°, which is interpreted to be the main Alpine Fault reflector located only ∼100 m beneath the maximum drilled depth of the DFDP-2B borehole. At shallower depths (z < 0.5 km), additional reflectors are identified as fault segments with generally steeper dips up to 56°. Additionally, a glacially over-deepened trough with nearly horizontally layered sediments and a major fault (z < 0.5 km) are identified 0.5–1 km south of the DFDP-2B borehole. Thus, a complex structural environment is seismically imaged and shows the complexity of the Alpine Fault at Whataroa. KW - Imaging KW - Signal processing KW - Seismic KW - Borehole KW - DAS PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-539163 DO - https://doi.org/10.1029/2021JB023013 VL - 126 IS - 12 SP - 1 EP - 21 PB - American Geophysical Union AN - OPUS4-53916 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Fritsch, Tobias T1 - 3D Analysis of Powder for Laser Beam Melting by Synchrotron X-ray CT N2 - Additive Manufacturing (AM) in terms of laser powder-bed fusion (L-PBF) offers new prospects regarding the design of parts and enables therefore the production of complex structures. The quality of the feedstock material receives increasing attention, as it depicts the first part of the L-PBF process chain. The powder quality control in terms of flowability and powder bed packing density is therefore mandatory. In this work, a workflow for quantitative 3D powder analysis in terms of particle size, particle shape, particle porosity, inter-particle distance and packing density was established. Synchrotron computed tomography (CT) was used to correlate the packing density with the particle size and particle shape for three different powder batches. The polydisperse particle size distribution (PSD) was transformed into a statistically equivalent bidisperse PSD. The ratio of the small and large particles helped to understand the powder particle packing density. While the particle shape had a neglectable influence, the particle size distribution was identified as major contributor for the packing density. T2 - AM- Workshop BAM CY - Online meeting DA - 20.04.2021 KW - Additive manufacturing KW - Laser powder bed fusion KW - Powder KW - Particle size distribution KW - Packing density PY - 2021 AN - OPUS4-53477 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Léonard, Fabien A1 - Zhang, Zhen A1 - Malow, Marcus A1 - Bruno, Giovanni T1 - 3D characterisation of ammonium nitrate powders by X-ray computed tomography N2 - The mixture of ammonium nitrate (AN) prills and fuel oil (FO), usually called ANFO, is extensively used in the mining industry as a bulk industrial explosive. One of the major performance predictors of ANFO mixtures is the fuel oil retention, which is itself governed by the porosity of the AN prills. Standardised tests routinely used to assess oil retention face several important limitations; the first being the difficulty to cover the wide range of porosity contents and morphologies from different types of ammonium nitrate prills; the second being the inability to evaluate the closed porosity, which is an important factor regarding the sensitivity of the explosive to detonation. In this study, we present how X-ray computed tomography (XCT), and the associated advanced data processing workflow, can be used to fully characterise the structure and morphology of AN prills. We show that structural parameters such as volume fraction of the different phases and morphological parameters such as specific surface area and shape factor can be reliably extracted from the XCT data, and that there is a good agreement with the measured oil retention values. XCT can therefore be employed to non-destructively and accurately evaluate and characterise porosity in ammonium nitrate prills. T2 - 23rd Seminar on New Trends in Research of Energetic Materials CY - Meeting was canceled DA - 01.04.2020 KW - Ammonium nitrate KW - Prill KW - Non-destructive characterisation KW - Porosity KW - Specific surface area PY - 2020 SP - 163 EP - 171 AN - OPUS4-51272 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Fritsch, Tobias A1 - Farahbod-Sternahl, L. A1 - Serrano-Munoz, Itziar A1 - Léonard, F. A1 - Haberland, C. A1 - Bruno, Giovanni T1 - 3D Computed Tomography Quantifies the Dependence of Bulk Porosity, Surface Roughness, and Re-Entrant Features on Build Angle in Additively Manufactured IN625 Lattice Struts N2 - Layer-by-layer additive manufacturing (AM) by means of laser-powder bed Fusion (L-PBF) offers many prospects regarding the design of lattice structures used, for example, in gas turbines. However, defects such as bulk porosity, Surface roughness, and re-entrant features are exacerbated in nonvertical structures, such as tilted struts. The characterization and quantification of these kinds of defects are essential for the correct estimation of fracture and fatigue properties. Herein, cylindrical struts fabricated by L-PBF are investigated by means of X-ray computed tomography (XCT), with the aim of casting light on the dependence of the three kinds of defects (bulk porosity, surface roughness, and re-entrant features) on the build angle. Innovative analysis methods are proposed to correlate shape and position of pores, to determine the angular-resolved Surface roughness, and to quantify the amount of re-entrant surface features, q. A meshing of the XCT surface enables the correlation of q with the classical Surface roughness Pa. This analysis leads to the conclusion that there is a linear correlation between q and Pa. However, it is conjectured that there must be a threshold of surface roughness, below which no re-entrant features can be build. KW - Additive manufacturing KW - Laser powder bed fusion KW - Computed tomography KW - Surface roughness analysis KW - Re-entrant surface feature PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-534728 DO - https://doi.org/10.1002/adem.202100689 IS - 2100689 SP - 1 EP - 8 PB - Wiley-VCH Verlag AN - OPUS4-53472 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Mishurova, Tatiana T1 - 3D imaging and residual stress analysis for AM Materials N2 - The overview of the activity of Federal Institute for Material Research and Testing (BAM, Belin, Germany) in the field of additively manufacturing material characterization will be presented. The research of our group is focused on the 3D imaging of AM materials by means of X-ray Computed Tomography at the lab and at synchrotron, and the residual stress characterization by diffraction (nondestructive technique). T2 - Seminar at Integrated Additive Manufacturing center, Politecnico Torino CY - Turin, Italy DA - 14.02.2023 KW - Additive manufacturing KW - Laser powder bed fusion KW - Residual stress KW - Computed tomography KW - Synchrotron X-ray diffraction PY - 2023 AN - OPUS4-57047 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Serrano-Munoz, Itziar T1 - 3D imaging and residual stress analysis for AM Materials N2 - Metal Additive Manufacturing (AM) technologies such as Laser Powder Bed Fusion (LPBF) are characterized by layer wise construction, which enable advancements of component design, leading to potential efficiency and performance improvements. However, the rapid cooling rates associated with the process consequently leads to the generation of high magnitude residual stresses (RS). Therefore, a deep understanding of the formation of RS, the influence of process parameters on their magnitude and the impact on mechanical performance is crucial for widespread application. The experimental characterization of these RS is essential for safety related engineering application and supporting the development of reliable numerical models. Diffraction-based methods for RS analysis using high energy synchrotron X-rays and neutrons enable non-destructive spatially resolved characterization of both surface and bulk residual stresses in complex components. This presentation will provide an overview of recent research conducted by the BAM at large scale facilities for the characterization of residual stresses in LPBF metallic alloys. Special focus will be given to the challenges posed by textured LPBF materials for the reliable choice of the diffraction elastic constants (DECs), which is crucial to the accurate calculation of the level of RS. T2 - Seminar at LTDS, Ecole Centrale de Lyon CY - Lyon, France DA - 15.06.2023 KW - Residual stress KW - Additive manufacturing KW - Diffraction methods PY - 2023 AN - OPUS4-57808 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Mishurova, Tatiana T1 - 3D Imaging and residual stress analysis of additively manufactured materials N2 - The focus of the presentation focus will be on 3D imaging by means of X-ray Computed Tomography (XCT) at the lab and at synchrotron, and the non-destructive residual stress (RS) characterization by diffraction of additively manufactured (AM) materials in BAM (Berlin, Germany). The manufacturing defects and high RS are inherent of AM techniques and affect structural integrity of the components. Using XCT the defects size and shape distribution as well as geometrical deviations can be characterized, allowing the further optimization of the manufacturing process. Diffraction-based RS analysis methods using neutron and synchrotron X-rays at large scale facilities offer the possibility to non-destructively spatially resolve both surface and bulk RS in complex components and track their changes following applied thermal or mechanical loads. T2 - The International Symposium on Nondestructive Characterization of Materials 2023 CY - Zurich, Switzerland DA - 15.08.2023 KW - Additive manufacturing KW - Residual stress KW - X-ray computed tomography PY - 2023 AN - OPUS4-58113 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bruno, Giovanni T1 - 3D imaging and residual stress analysis of AM Materials N2 - In this seminar, the capabilities for materials characterization at Division 8.5, BAM will be shon. Particular focus will be given to residual stress analysis and defect imaging in additively manufactured materials and components T2 - Skoltech - The 3rd International Workshop of Advanced Manufacturing Technologies CY - Online meeting DA - 18.04.2023 KW - Neutron Diffraction KW - X-ray diffraction KW - X-ray Computed Tomography KW - X-ray refraction radiography KW - Residual stress KW - Additive manufacturing PY - 2023 AN - OPUS4-57360 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Mishurova, Tatiana T1 - 3D imaging and residual stress analysis of AM materials at BAM N2 - The overview of the activity of group 8.5 Micro-NDT (BAM, Belin, Germany) in the field of additively manufacturing material characterization will be presented. The research of our group is focused on the 3D imaging of AM materials by means of X-ray Computed Tomography at the lab and at synchrotron, and the residual stress characterization by diffraction (nondestructive technique). T2 - Seminar at Chalmers University and Centre for Additive Manufacture (CAM2) CY - Gothenburg, Sweden DA - 19.05.2022 KW - Additive manufacturing KW - Laser powder bed fusion KW - Residual stress KW - Computed tomography KW - Synchrotron X-ray diffraction KW - X-ray refraction PY - 2022 AN - OPUS4-55019 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Lay, Vera T1 - 3D seismic imaging of the Alpine Fault and the glacial valley at Whataroa, New Zealand N2 - The Alpine Fault at the West Coast of the South Island (New Zealand) is a major plate boundary that is expected to rupture in the next 50 years, likely as a magnitude 8 earthquake. The Deep Fault Drilling Project (DFDP) aimed to deliver insight into the geological structure of this fault zone and its evolution by drilling and sampling the Alpine Fault at depth. Here we present results from a seismic survey around the DFDP-2 drill site in the Whataroa Valley where the drillhole almost reached the fault plane. This unique 3D seismic survey includes several 2D lines and a 3D array at the surface as well as borehole recordings. Within the borehole, the unique option to compare two measurement systems is used: conventional three-component borehole geophones and a fibre optic cable (heterodyne Distributed Vibration Sensing system (hDVS)). Both systems show coherent signals but only the hDVS system allowed a recording along the complete length of the borehole. Despite the challenging conditions for seismic imaging within a glacial valley filled with sediments and steeply dipping valley flanks, several structures related to the valley itself as well as the tectonic fault system are imaged. The pre-processing of the seismic data also includes wavefield separation for the zero-offset borehole data. Seismic images are obtained by prestack depth migration approaches. Within the glacial valley, particularly steep valley flanks are imaged directly and correlate well with results from the P-wave velocity model obtained by first arrival travel-time tomography. Additionally, a glacially over-deepened trough with nearly horizontally layered sediments is identified about 0.5 km south of the DFDP-2B borehole. With regard to the expected Alpine fault zone, a set of several reflectors dipping 40-56° to the southeast are identified in a ~600 m wide zone between depths of 0.2 and 1.2 km that is interpreted to be the minimum extent of the damage zone. Different approaches image one distinct reflector dipping at 40°, which is interpreted to be the main Alpine Fault reflector. This reflector is only ~100 m ahead from the lower end of the borehole. At shallower depths (z<0.5 km), additional reflectors are identified as fault segments and generally have steeper dips up to 56°. About 1 km south of the drill site, a major fault is identified at a depth of 0.1-0.5 km that might be caused by the regional tectonics interacting with local valley structures. A good correlation is observed among the separate seismic data sets and with geological results such as the borehole stratigraphy and the expected surface trace of the fault. In conclusion, several structural details of the fault zone and its environment are seismically imaged and show the complexity of the Alpine Fault at the Whataroa Valley. Thus, a detailed seismic characterization clarifies the subsurface structures, which is crucial to understand the transpressive fault’s tectonic processes. T2 - EGU 2021 General Assembly CY - Online meeting DA - 19.04.2021 KW - Imaging KW - Signal processing KW - Seismic KW - Borehole KW - DAS PY - 2021 AN - OPUS4-52614 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Lozano, Daniel A1 - Bulling, Jannis A1 - Asokkumar, A. A1 - Gravenkamp, H. A1 - Birk, C. T1 - 3D simulations of ultrasonic waves in plates using the scaled boundary finite element method and high-order transition elements N2 - It can be difficult to efficiently model ultrasonic waves in 3D structures, especially when the computational model needs to account for complex geometries. This contribution presents a solution based on the Scaled Boundary Finite Element Method (SBFEM). It is a numerical tool suitable for elastodynamic problems. A space-tree discretisation, namely quad-trees, is used. This technique allows the decomposition of an image into quadrilaterals or quads, which are extruded to generate the 3D plate geometry. In particular, small quads resolve regions with discontinuities, allowing them to represent fine details in the structure. Moreover, this meshing technique allows for exploiting cell similarities, making the calculation procedure more efficient. The space-tree discretisations are generated from a high-resolution image containing all the information about damaged regions or boundary conditions. The resulting SBFEM polyhedral domains employ transition elements to ensure correct coupling between cells of different sizes. The analytical solution of a cylindrical scatterer serves as a reference to validate the proposed approach. Other examples also demonstrate the validity of the methodology and its flexibility. KW - High-order transition elements KW - Image-based models KW - Wave propagation KW - Scaled boundary finite element method PY - 2023 DO - https://doi.org/10.1016/j.wavemoti.2023.103158 VL - 120 SP - 1 EP - 20 PB - Elsevier B.V. AN - OPUS4-57768 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Mishurova, Tatiana T1 - 3D X-ray Imaging and residual stress analysis of materials N2 - The focus of the presentation focus will be on 3D imaging by means of X-ray Computed Tomography (XCT) at the lab and at synchrotron, and the non-destructive residual stress (RS) characterization by diffraction of different kind of materials in FB8.5 Micro-NDT BAM. For instance, the manufacturing defects and high RS are inherent of additively manufacturing techniques and affect structural integrity of the components. Using XCT the defects size and shape distribution as well as geometrical deviations can be characterized, allowing the further optimization of the manufacturing process. Diffraction-based RS analysis methods using neutron and synchrotron X-rays at large scale facilities offer the possibility to non-destructively spatially resolve both surface and bulk RS in complex components and track their changes following applied thermal or mechanical loads. T2 - Seminar at Applied Materials Group at PSI CY - Villigen, Switzerland DA - 13.09.2023 KW - Additive manufacturing KW - Residual stress KW - X-ray computed tomography PY - 2023 AN - OPUS4-58310 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Dayani, Shahabeddin T1 - 4D imaging of abuse mechanisms in Li-ion batteries N2 - Higher energy density materials are being pushed by the research community to make lithium ion batteries a better competitor of chemical fossil fuels for transport applications. This increases potential risk of lithium ion batteries and therefore safety investigations are highly important for application purposes. Operando Computer Tomography provides a non-destructive investigation method of different abuse mechanisms. Application of X-ray computed tomography (XCT) for studying lithium-ion batteries has gained interest among the research community especially in the past decade [1]. This technique is widely used for ex-situ samples to measure porosity and tortuosity [2], particle size and volume distribution [3] in the graphite anode as well as different cathode materials such as LiCoOx and NiMnCoOx. [4]. In situ measurements of commercial batteries are also often carried out to detect defects induced in a cell by a safety abuse test or manufacturing process [5]. Operando CT of large cells (for example 18650 form factor) is conducted at synchrotron facilities with high flux of high energy photons, however at a cost of details due to the large field of view [6]. Methodik Thanks to their high brilliance, synchrotron beam facilitates us to do a full Computed Tomography in a short time. This enables us to measure batteries while being cycled with a reasonable time resolution to record morphological changes. In this poster we illustrate how one can utilize this ability to investigate abuse mechanisms on an actual commercially available lithium ion battery as well as a home made micro cell. Ergebnisse In this work, lab-based and synchrotron X-ray computed tomography is applied to commercial Li-ion batteries. It is shown how to find most suitable imaging settings to study available lithium-ion batteries on different size scales, from cell level to particle level. We also demonstrate how to optimize contrast as well as both temporal and spatial resolutions to study in-situ and operando processes in a commercial battery using attenuation and phase contrast SXCT. Manufacturing defects and inconsistencies on cell level as well as the electrode and microstructure on material level are shown in our study. Moreover, some abuse conditions are imaged in operando in a commercially available li-ion battery. Diskussion This work has demonstrated various imaging settings using lab and synchrotron based X-ray computed tomography to study in-situ as well as under operando condition, some abuse mechanisms in commercial lithium-ion batteries from cell level to electrode and particle level. T2 - Batterie Forum Deutschland CY - Berlin, Germany DA - 18.01.2023 KW - X-ray Computed tomography KW - Li ion Battery KW - Imaging PY - 2023 AN - OPUS4-57156 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Strangfeld, Christoph T1 - A brief introduction to wind turbine aerodynamics N2 - In general, wind turbines transform the kinetic energy of the wind into electric power. Thereby, the wind turbine blades are facing unsteady loads which are transferred to the hub to generate a rotation of the turbine’s axis. This brief introduction focuses on the aerodynamics of the blades and the corresponding loads. Starting with the basic flow field and loads of an airfoil, terms like stagnation point, boundary layer, Reynolds number, transition, and separation are introduced. For different geometries, lift and drag coefficient curves are discussed. Then, full wings will be considered, including their three-dimensional flow field due to wing tip vortices and crossflows. As a main source of increased loads, unsteady effects are explained in more detail such as gusts, tower passing, earth boundary layer crossing, free stream turbulences, yaw misalignment, etc. At the end, extra loads due to an oscillating free stream are introduced. T2 - QI-Digital: KI-Machbarkeitsstudie Thermografie Rotorblätter CY - Berlin, Germany DA - 13.12.2022 KW - Wind turbine KW - Nondestructive testing KW - Aerodynamics KW - Dynamic stall PY - 2022 AN - OPUS4-56728 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bulling, Jannis T1 - A Combination of the Scaled Boundary Finite Element Method with the Mortar Method N2 - The simulation of ultrasonic waves in a linearly elastic body can be computationally intensive. The reason is the relatively short wavelength compared to the body size for high frequencies. One possible approach to counteract the high computational costs is to decompose the domain into small parts and strive for parallelization. The Mortar Method is a well-established approach for domain decomposition. A rather new approach to discretize the emerging subdomains is the Scaled Boundary Finite Element Method. This semi-analytical method has many attractive properties. Some of these properties are listed subsequently. The grid consists of polygonal elements, which leaves much freedom in the meshing process. A variety of material distributions, including anisotropic materials, can be considered. High-order shape functions can be used for optimal convergence properties. The approach treats singularities at crack tips and corners analytically. Especially in the frequency domain, the Scaled Boundary Finite Element Method reduces the dimension of the approximation because only degrees of freedom which are associated with the boundary of a polygonal element are necessary. Those desirable properties make the method particularly suitable for calculating the dynamic responses in bodies with cracks, as it is essential for many non-destructive testing and structural health monitoring applications. In this contribution, we present a combination of the Scaled Boundary Finite Element Method with the Mortar Method in two dimensions. The presentation starts with a theoretical overview of both approaches. Subsequently, numerical examples demonstrate the stability of the combination for the polygonal boundary of the elements. The numerical examples increase in complexity and are compared to results computed on non-divided domains with the Finite Element Method. T2 - WCCM-ECCOMAS CONGRESS CY - Online meeting DA - 11.01.2021 KW - Ultrasound KW - Numerical Simulation KW - Scaled Boundary Finite Element Method, Mortar Method PY - 2021 AN - OPUS4-52275 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Chand, Keerthana A1 - Fritsch, Tobias A1 - Oster, Simon A1 - Ulbricht, Alexander A1 - Poka, Konstantin A1 - Bruno, Giovanni T1 - A Comparative Study of Rigid Three-Dimensional Image Registration Methods for Powder Bed Fusion with Laser Beam of Metals Using a Gold Standard Approach N2 - In Additive Manufacturing (AM), precise rigid three-dimensional (3D) image registration between X-ray Computed Tomography (XCT) scans and Computer-Aided Design (CAD) models is an important step for the quantification of distortions in produced parts. Given the absence of standardized benchmarks for image registration in AM, we introduce a gold standard for 3D image registration, using a reference base plate beneath the build structure. This gold standard is used to quantify the accuracy of rigid registration, with a proof of concept demonstrated in PBF-LB/M. In this study, we conduct a comparative analysis of various rigid 3D registration methods useful for quality assurance of PBF-LB/M parts including feature-based, intensity-based, and point cloud-based approaches. The performance of each registration method is evaluated using measures of alignment accuracy based on the gold standard and computational efficiency. Our results indicate significant differences in the efficacy of these methods, with point cloud based Coherent Point Drift (CPD) showing superior performance in both alignment and computational efficiency. The rigidly registered 3D volumes are used to estimate the deformation field of the printed parts relative to the nominal CAD design using Digital Volume Correlation (DVC). The quality of the estimated deformation field is assessed using the Dice score metric. This study provides insights into methods for enhancing the precision and reliability of AM process. KW - Digital volume correlation KW - 3D image registration KW - 3D image processing KW - X-ray computed tomography KW - Computer-aided design KW - Displacement field estimation PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-627043 DO - https://doi.org/10.1007/s10921-025-01174-0 SN - 1573-4862 VL - 44 IS - 30 SP - 1 EP - 20 PB - Springer AN - OPUS4-62704 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Oster, Simon T1 - A comparison of machine learning approaches for porosity prediction in PBF-LB/M based on thermography N2 - Metal-based additive manufacturing processes are increasingly used in industry to produce complex-shaped components. In this regard, the laser-based Powder Bed Fusion process (PBF-LB/M) is one of the key technologies due to its capability to produce components in high spatial accuracy. The formation of porosity during manufacturing poses a serious risk to the safety of the printed parts. For quality assessment, in-situ monitoring technologies such as thermography can be used to capture the thermal history during production. It was shown that discontinuities within the thermal history can be correlated with the probability of porosity or defect formation. In this context, Machine Learning (ML) algorithms have achieved promising results for the task of porosity prediction based on thermographic in-situ monitoring data. One important technique is the use of thermogram features for porosity prediction that are extracted from the raw data (e.g., features related to the melt pool geometry or spatter generation). However, the reduction from large thermogram data to discrete features holds the risk of losing potentially important thermal information and, thereby, introducing bias in the model. Therefore, we present a raw data-based deep learning approach that uses thermographic image sequences for the prediction of local porosity. The model takes advantage of the self-attention mechanism that considers not only the thermogram information but also its positional context within the sequence. The model is used to predict porosity in the form of a many-to-one regression. It is trained and tested on a dataset retrieved from the manufacturing of HAYNES282 cuboid specimens. The model results are compared against state-of-the-art thermogram feature-based ML models and artificial neural networks. The raw data model outperforms its feature-based counterparts in terms of prediction scores and, therefore, seems to make better use of the information available in the thermogram data. T2 - 4th Symposium on Materials and Additive Manufacturing CY - Berlin, Germany DA - 12.06.2024 KW - PBF-LB/M KW - In situ monitoring KW - Thermography KW - Additive Manufacturing KW - Machine learning KW - Porosity prediction PY - 2024 AN - OPUS4-62472 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Tsamos, Athanasios A1 - Evsevleev, Sergei A1 - Fioresi, Rita A1 - Faglioni, Francesco A1 - Bruno, Giovanni T1 - A Complete Strategy to Achieve High Precision Automatic Segmentation of Challenging Experimental X‐Ray Computed Tomography Data Using Low‐Resemblance Synthetic Training Data N2 - It is shown that preconditioning of experimental X‐ray computed tomography (XCT) data is critical to achieve high‐precision segmentation scores. The challenging experimental XCT datasets and deep convolutional neural networks (DCNNs) are used that are trained with low‐resemblance synthetic XCT data. The material used is a 6‐phase Al–Si metal matrix composite‐reinforced with ceramic fibers and particles. To achieve generalization, in our past studies, specific data augmentation techniques were proposed for the synthetic XCT training data. In addition, two toolsets are devised: (1) special 3D DCNN architecture (3D Triple_UNet), slicing the experimental XCT data from multiple views (MultiView Forwarding), the i.S.Sy.Da.T.A. iterative segmentation algorithm, and (2) nonlocal means (NLM) conditioning (filtering) for the experimental XCT data. This results in good segmentation Dice scores across all phases compared to more standard approaches (i.e., standard UNet architecture, single view slicing, standard single training, and NLM conditioning). Herein, the NLM filter is replaced with the deep conditioning framework BAM SynthCOND introduced in a previous publication, which can be trained with synthetic XCT data. This leads to a significant segmentation precision increase for all phases. The proposed methods are potentially applicable to other materials and imaging techniques. KW - Automatic Segmentation KW - XCT KW - Artificial Intelligence KW - Synthetic Training Data KW - i.S.Sy.Da.T.A. KW - BAM SynthMAT KW - BAM SynthCOND KW - Triple UNet KW - Convolutional Neural Network (DCNN) PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-590912 DO - https://doi.org/10.1002/adem.202301030 SN - 1438-1656 VL - 26 IS - 2 SP - 1 EP - 9 PB - Wiley online library AN - OPUS4-59091 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Agasty, Amit A1 - Costard, Rene A1 - Kadoke, Daniel A1 - Kind, Thomas A1 - Hicke, Konstantin A1 - Hüsken, Götz T1 - A comprehensive experimental assessment of reinforced concrete walls under blast: In situ monitoring of loading, dynamic response and damage with NDT methods and embedded sensors N2 - A safety or security related assessment of explosions, accidental and intentional scenarios alike, often necessitate performance of resource intensive replication tests. For an efficient assessment without performing full scale blast tests, e.g., supported by numerical simulations, detailed knowledge is necessary to predict the blast loading from a given charge configuration, the resulting dynamic response of the structure under investigation as well as the resulting damage. Validation of numerical simulation requires the spatially resolved acquisition of all these parameters in real time. In this paper we present a set of measurement techniques and discuss their suitability for monitoring reinforced concrete (RC) walls under blast loading. Different blast-loading scenarios were realized by varying the charge weight and the standoff distance. The dynamic loading of the wall was characterized with pressure sensors complemented by numerical simulations using the APOLLO Blastsimulator and ConWep. High speed digital image correlation (DIC) was implemented in combination with multiple acceleration sensors to observe the dynamic deflection of the walls during the loading and to determine the residual deformation after the loading had ceased. In addition, one test specimen was instrumented with fiber optic sensor cables. These fiber optic sensors were used for distributed acoustic sensing (DAS) delivering information on dynamics of compression and tension cycles from within the structure. Additionally, the local damage pattern emerging during the series of blasts was determined via distributed fiber optic strain sensing (DSS) to enable the characterization of visual and non-visual damage to the structure. The obtained information was compared to results by an ultrasound structure-scanner. KW - Blast testing KW - Reinforced concrete KW - NDT methods KW - Fiber optic sensing KW - DIC KW - Numerical simulations KW - APOLLO Blastsimulator PY - 2025 DO - https://doi.org/10.1177/20414196251353795 SN - 2041-4196 SP - 1 EP - 31 PB - Sage Publications AN - OPUS4-63832 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bruno, Giovanni A1 - Mishurova, Tatiana A1 - Serrano-Munoz, Itziar A1 - Ulbricht, Alexander A1 - Fritsch, Tobias A1 - Sprengel, Maximilian A1 - Evans, Alexander A1 - Kromm, Arne A1 - Madia, Mauro ED - Bruno, Giovanni T1 - A Critical Discussion on the Diffraction-Based Experimental Determination of Residual Stress in AM Parts N2 - As opposed to reviewing results on experimental determination of residual stress by diffraction, this paper discusses the open issues when dealing with residual stress determination in additive manufactured parts, in particular those manufactured with laser powder bed fusion techniques. Three points are addressed in detail: (a) the proper determination of the strain-free reference d0, (b) the problem of the determination of the principal axes, and (c) the use of the correct diffraction elastic constants. It is shown that all methods to determine the strain-free reference d0 suffer from caveats, and care must be taken in evaluating the most suitable for the problem being tackled. In addition, it is shown that, in some systems, the principal axes do correspond to the geometrical axes of the specimen, but this needs to be systematically checked, especially in the case of uni- or bidirectional hatching strategies. Finally, the need to experimentally determine the proper diffraction elastic constants is underlined, especially in the case of strongly textured specimens, which again depends on the deposition strategy. T2 - ASTM ICAM 2020 – ASTM International Conference on Additive Manufacturing CY - Online meeting DA - 16.11.2020 KW - Additive Manufacturing KW - Diffraction KW - Residual Stress PY - 2020 DO - https://doi.org/10.1520/STP163120190148 VL - STP1631 SP - 122 EP - 138 PB - ASTM International CY - USA AN - OPUS4-51347 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Oster, Simon A1 - Breese, Philipp Peter A1 - Ulbricht, Alexander A1 - Mohr, Gunther A1 - Altenburg, Simon T1 - A deep learning framework for defect prediction based on thermographic in-situmonitoring in laser powder bed fusion N2 - The prediction of porosity is a crucial task for metal based additive manufacturing techniques such as laser powder bed fusion. Short wave infrared thermography as an in-situ monitoring tool enables the measurement of the surface radiosity during the laser exposure. Based on the thermogram data, the thermal history of the component can be reconstructed which is closely related to the resulting mechanical properties and to the formation of porosity in the part. In this study, we present a novel framework for the local prediction of porosity based on extracted features from thermogram data. The framework consists of a data pre-processing workflow and a supervised deep learning classifier architecture. The data pre-processing workflow generates samples from thermogram feature data by including feature information from multiple subsequent layers. Thereby, the prediction of the occurrence of complex process phenomena such as keyhole pores is enabled. A custom convolutional neural network model is used for classification. Themodel is trained and tested on a dataset from thermographic in-situ monitoring of the manufacturing of an AISI 316L stainless steel test component. The impact of the pre-processing parameters and the local void distribution on the classification performance is studied in detail. The presented model achieves an accuracy of 0.96 and an f1-Score of 0.86 for predicting keyhole porosity in small sub-volumes with a dimension of (700 × 700 × 50) μm3. Furthermore, we show that pre-processing parameters such as the porosity threshold for sample labeling and the number of included subsequent layers are influential for the model performance. Moreover, the model prediction is shown to be sensitive to local porosity changes although it is trained on binary labeled data that disregards the actual sample porosity. KW - Laser Powder Bed Fusion (PBF-LB/M, L-PBF) KW - Selective Laser Melting (SLM) KW - SWIR thermography KW - Online monitoring KW - Flaw detection KW - Machine learning KW - Convolutional neural networks (CNN) PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-575148 DO - https://doi.org/10.1007/s10845-023-02117-0 SN - 0956-5515 SP - 1 EP - 20 PB - Springer AN - OPUS4-57514 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Richard A1 - Oehrl, A. A1 - Wischerhoff, E. A1 - Schukar, Marcus T1 - A fibre optic temperature sensor based on thermoresponsive polymer N2 - We present a temperature sensor based on a polymer exhibiting a Lower Critical Solution Temperature (LCST) in aqueous solution encapsulated in a capillary. Parameters are chosen such that the solution exhibits a cloud point in a temperature range of 30° C to 39°C. The characteristic of thermoresponsive polymers with an LCST, is that above that temperature phase separation of the polymer takes place which leads to a temperature-dependent formation of a cloudy suspension. An optical intensity measurement over the desired temperature range is established by an increase of optical attenuation inside the polymer solution caused by a rising temperature. For our purpose, the polymer capillary is connected to transmitter and receiver via a Polymer Optical Fibre (POF). Our intensity measurement is, to the best of our knowledge, a novel method and can be considered simple when compared to existing fibre-based temperature measurement techniques. Due to the lack of electrical components at the probe, this sensor is suitable for measurements in strong electromagnetic fields and environments for which flying sparks are hazardous, i.e., inflammable fluids or gases. Furthermore, all manufactured sensors share the same temperature dependence and, therefore, are well-suited for comparative measurement, e.g., flow measurement systems. With the given temperature range, a body temperature measurement is also suitable. T2 - European Workshop on Optical Fibre Sensors (EWOFS 2023) CY - Mons, Belgium DA - 23.05.2023 KW - Thermoresponsive polymer KW - Aqueous polymer solution KW - Temperature sensor KW - LCST KW - POF PY - 2023 DO - https://doi.org/https://doi.org/10.1117/12.2678418 SP - 12643-69 PB - SPIE CY - Bellingham, Wash. AN - OPUS4-57735 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Wu, Cheng-Chieh A1 - Weisbrich, S. A1 - Burger, M. A1 - Neitzel, F. ED - Zemčík, R. T1 - A four-point bending test apparatus for measurement- and model-based structural analysis N2 - By means of a small-scale truss bridge, the ability of the Measurement- and Model-based Structural Analysis to detect and localise damage was examined in. Although there was no noteworthy difficulty in detecting damage, it turned out that damage localisation responds sensitively to systematic influences, i.e. non-modelled properties of the mechanical model. Therefore, another experiment is being conducted to re-examine the Measurement- and Model-based Structural Analysis. For this purpose, the bending test is carried out as it has been already theoretically respectively numerically discussed in. In this attempt, the systematic influences such as residual stress are kept as low as possible. T2 - 36th Danubia Adria Symposium on Advances in Experimental Mechanics CY - Pilsen, Czech Republic DA - 24.09.2019 KW - Damage detection KW - Adjustment calculation KW - Finite element method KW - Integrated analysis PY - 2020 UR - http://www.sciencedirect.com/science/article/pii/S2214785320326432 DO - https://doi.org/10.1016/j.matpr.2020.04.028 SN - 2214-7853 VL - 32 IS - 2 SP - 156 EP - 161 PB - Elsevier Ltd. AN - OPUS4-51551 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schönsee, Eric A1 - Hüsken, Götz A1 - Kocherla, Amarteja A1 - Strangfeld, Christoph T1 - A framework for characterising the reflection intensity of a 3D-CP mortar N2 - The growing interest of 3D Concrete Printing (3DCP) arises new challenges for quality control. A major challenge lies in evaluation of the interlayer bond strength. An optical approach is developed to monitor the surface properties of the printed layer. Commercial laser profile scanners are utilised to record the reflection intensity of the mortar’s surface. To be applicable in the printing process, the reflection intensity needs to be characterised for each material within the printable region and further effects, like geometry and environmental light, need to be considered. The presented work aims to provide a framework for implementing the laser reflection intensity to 3DCP monitoring. T2 - NDT-CE 2025 - The International Symposium on Nondestructive Testing in Civil Engineering CY - Izmir, Türkiye DA - 24.09.2025 KW - 3D Concrete Printing KW - In Situ Measurements KW - Laser reflection intensity PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-643047 DO - https://doi.org/10.58286/31728 SN - 1435-4934 SP - 1 EP - 4 PB - NDT.net AN - OPUS4-64304 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schönsee, Eric T1 - A framework for characterising the reflection intensity of a 3D-CP mortar N2 - The growing interest of 3D Concrete Printing (3DCP) arises new challenges for quality control. A major challenge lies in evaluation of the interlayer bond strength. An optical approach is developed to monitor the surface properties of the printed layer. Commercial laser profile scanners are utilised to record the reflection intensity of the mortar’s surface. To be applicable in the printing process, the reflection intensity needs to be characterised for each material within the printable region and further effects, like geometry and environmental light, need to be considered. The presented work aims to provide a framework for implementing the laser reflection intensity to 3DCP monitoring. T2 - NDT-CE 2025 - The International Symposium on Nondestructive Testing in Civil Engineering CY - Izmir, Türkiye DA - 24.09.2025 KW - 3D Concrete Printing KW - In Situ Measurements KW - Laser reflection intensity PY - 2025 AN - OPUS4-64305 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schaad, C. T1 - A Framework for Transforming Process Control System Data from a Hydrogen Fueling Station into HyCReD Data N2 - Reliability data for hydrogen infrastructure components is essential for developing Quantitative Risk Assessment (QRA) for these technologies, which in turn is necessary for a safer deployment and expansion of the hydrogen market. However, there is currently a lack of hydrogen component reliability data available for these systems, thus limiting the usefulness of insights obtained from these QRA. The Hydrogen Component Reliability Database (HyCReD) has been proposed as a tool for reliability data collection and as a source for future QRAs. In this paper, we develop a digital tool that automatically processes data coming from Process Control System (PCS) in a hydrogen fueling station, detects the relevant failure events for hydrogen systems during its operation, and then logs the event information into HyCReD. To build this tool, we first categorized the station components in hydrogen service, their specific failure modes, and the specific failure mechanisms that are relevant to a QRA. Then, we identified the data available in the station PCS and the methods available for diagnosing the relevant failure events. The resulting tool is divided into three steps: (1) PCS data collection through an API, (2) data analysis for the detection and diagnosis of new failure events, and (3) logging that event into HyCReD. Finally, we discuss the potential for expanding the detection and diagnosis to more complex failure modes present in a hydrogen fueling station. This digital tool is set for implementation and validation on an experimental hydrogen fueling site. The goal for this digital tool is to be applicable to every kind of hydrogen fueling station and to be extendable to similar hydrogen technologies. T2 - 35th European Safety and Reliability Conference (ESREL2025) and the 33rd Society for Risk Analysis Europe Conference (SRA-E 2025) CY - Stavanger, Norway DA - 15.06.2025 KW - Reliability data KW - Hydrogen safety KW - HyCReD KW - Hydrogen Component Reliability Database KW - Hydrogen fueling station KW - Hydrogen refuelling station KW - HRS PY - 2025 AN - OPUS4-63533 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schaad, C. A1 - Tiebe, Carlo A1 - Groth, Katrina M. T1 - A Framework for Transforming Process Control System Data from a Hydrogen Fueling Station into HyCReD Data N2 - Reliability data for hydrogen infrastructure components is essential for developing Quantitative Risk Assessment (QRA) for these technologies, which in turn is necessary for a safer deployment and expansion of the hydrogen market. However, there is currently a lack of hydrogen component reliability data available for these systems, thus limiting the usefulness of insights obtained from these QRA. The Hydrogen Component Reliability Database (HyCReD) has been proposed as a tool for reliability data collection and as a source for future QRAs. In this paper, we develop a digital tool that automatically processes data coming from Process Control System (PCS) in a hydrogen fueling station, detects the relevant failure events for hydrogen systems during its operation, and then logs the event information into HyCReD. To build this tool, we first categorized the station components in hydrogen service, their specific failure modes, and the specific failure mechanisms that are relevant to a QRA. Then, we identified the data available in the station PCS and the methods available for diagnosing the relevant failure events. The resulting tool is divided into three steps: (1) PCS data collection through an API, (2) data analysis for the detection and diagnosis of new failure events, and (3) logging that event into HyCReD. Finally, we discuss the potential for expanding the detection and diagnosis to more complex failure modes present in a hydrogen fueling station. This digital tool is set for implementation and validation on an experimental hydrogen fueling site. The goal for this digital tool is to be applicable to every kind of hydrogen fueling station and to be extendable to similar hydrogen technologies. T2 - 35th European Safety and Reliability Conference (ESREL2025) and the 33rd Society for Risk Analysis Europe Conference (SRA-E 2025) CY - Stavanger, Norway DA - 15.06.2025 KW - Reliability data KW - Hydrogen safety KW - HyCReD KW - Hydrogen Component Reliability Database KW - Hydrogen fueling station KW - Hydrogen refuelling station KW - HRS PY - 2025 UR - https://rpsonline.com.sg/proceedings/esrel-sra-e2025/html/ESREL-SRA-E2025-P6264.html SN - 978-981-94-3281-3 DO - https://doi.org/10.3850/978-981-94-3281-3-procd SP - 2663 EP - 2670 PB - Research Publishing CY - Singapore AN - OPUS4-63532 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Mehri Sofiani, F. A1 - Tacq, J. A1 - Elahi, S.A. A1 - Chaudhuri, Somsubhro A1 - De Waele, W. T1 - A hybrid probabilistic-deterministic framework for prediction of characteristic size of corrosion pits in low-carbon steel following long-term seawater exposure N2 - A hybrid deterministic-probabilistic framework is presented that combines in-situ measurements and numerical analysis to predict the characteristic size of corrosion pits over extended periods of exposure to seawater. A probabilistic model, informed by corrosion current density and temperature data, applies a sampling method to a range of overpotential values to reflect the surface deterioration in terms of pitting. The model is calibrated and validated with pit size data obtained from corroded coupons. Results reveal a positively skewed truncated lognormal distribution of pit depths which implies a higher prevalence of smaller pit depths, corroborated both experimentally and numerically. KW - Pitting corrosion KW - Offshore structures KW - Probabilistic analysis KW - Potentiodynamic polarisation PY - 2024 DO - https://doi.org/10.1016/j.corsci.2024.112039 VL - 232 SP - 1 EP - 15 PB - Elsevier Ltd. AN - OPUS4-60693 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Charmi, Amir A1 - Mustapha, Samir A1 - Yilmaz, Bengisu A1 - Heimann, Jan A1 - Prager, Jens T1 - A Machine Learning Based-Guided Wave Approach for Damage Detection and Assessment in Composite Overwrapped Pressure Vessels N2 - The applications of composite overwrapped pressure vessels (COPVs) in extreme conditions, such as storing hydrogen gases at very high pressure, impose new requirements related to the system's integrity and safety. The development of a structural health monitoring (SHM) system that allows for continuous monitoring of the COPVs provides rich information about the structural integrity of the component. Furthermore, the collected data can be used for different purposes such as increasing the periodic inspection intervals, providing a remaining lifetime prognosis, and also ensuring optimal operating conditions. Ultimately this information can be complementary to the development of the envisioned digital twin of the monitored COPVs. Guided waves (GWs) are preferred to be used in continuous SHM given their ability to travel in complex structures for long distances. However, obtained GW signals are complex and require advanced processing techniques. Machine learning (ML) is increasingly utilized as the main part of the processing pipeline to automatically detect anomalies in the system's integrity. Hence, in this study, we are scrutinizing the potential of using ML to provide continuous monitoring of COPVs based on ultrasonic GW data. Data is collected from a network of sensors consisting of fifteen Piezoelectric (PZT) wafers that were surface mounted on the COPV. Two ML algorithms are used in the automated evaluation procedure (i) a long short-term memory (LSTM) autoencoder for anomaly detection (defects/impact), and (ii) a convolutional neural network (CNN) model for feature extraction and classification of the artificial damage sizes and locations. Additional data augmentation steps are introduced such as modification and addition of random noise to original signals to enhance the model's robustness to uncertainties. Overall, it was shown that the ML algorithms used were able to detect and classify the simulated damage with high accuracy. T2 - 13th European Conference on Non-Destructive Testing (ECNDT) 2023 CY - Lisbon, Portugal DA - 03.07.2023 KW - Machine learning KW - Structural health monitoring KW - COPV KW - Guided waves KW - Damage localization PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-590789 DO - https://doi.org/10.58286/28079 SN - 2941-4989 VL - 1 IS - 1 SP - 1 EP - 6 PB - NDT.net CY - Mayen AN - OPUS4-59078 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Charmi, Amir T1 - A Machine Learning Based-Guided Wave Approach for Damage Detection and Assessment in Composite Overwrapped Pressure Vessels N2 - The applications of composite overwrapped pressure vessels (COPVs) in extreme conditions, such as storing hydrogen gases at very high pressure, impose new requirements related to the system's integrity and safety. The development of a structural health monitoring (SHM) system that allows for continuous monitoring of the COPVs provides rich information about the structural integrity of the component. Furthermore, the collected data can be used for different purposes such as increasing the periodic inspection intervals, providing a remaining lifetime prognosis, and also ensuring optimal operating conditions. Ultimately this information can be complementary to the development of the envisioned digital twin of the monitored COPVs. Guided waves (GWs) are preferred to be used in continuous SHM given their ability to travel in complex structures for long distances. However, obtained GW signals are complex and require advanced processing techniques. Machine learning (ML) is increasingly utilized as the main part of the processing pipeline to automatically detect anomalies in the system's integrity. Hence, in this study, we are scrutinizing the potential of using ML to provide continuous monitoring of COPVs based on ultrasonic GW data. Data is collected from a network of sensors consisting of fifteen Piezoelectric (PZT) wafers that were surface mounted on the COPV. Two ML algorithms are used in the automated evaluation procedure (i) a long short-term memory (LSTM) autoencoder for anomaly detection (defects/impact), and (ii) a convolutional neural network (CNN) model for feature extraction and classification of the artificial damage sizes and locations. Additional data augmentation steps are introduced such as modification and addition of random noise to original signals to enhance the model's robustness to uncertainties. Overall, it was shown that the ML algorithms used were able to detect and classify the simulated damage with high accuracy. T2 - European Conference on Non-Destructive Testing (ECNDT) CY - Lisbon, Portugal DA - 03.07.2023 KW - Guided waves KW - Structural Health Monitoring KW - Machine learning KW - Damage localization KW - COPV PY - 2023 AN - OPUS4-58687 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Landis, E. A1 - Hassefras, Emiel A1 - Oesch, Tyler A1 - Niederleithinger, Ernst T1 - A Microstructural Basis for Diffuse Ultrasound in Concrete N2 - Attenuation of ultrasonic signals in concrete has the potential to carry much information about the microstructure of the material. In this work a series of concrete specimens of varying porosities and pore size distributions were internally imaged with x-ray computed tomography (CT), and then subsequently examined with throughtransmission ultrasound. The CT images were used to quantify both capillary porosity of cement paste as well as internal interfaces that are likely to produce elastic wave scattering. Ultrasound signals were represented as a diffusion process such that absorption and scattering attenuation could be isolated. As implemented, the diffusion model was a poor predictor of capillary porosity, while diffusivity was a reasonable predictor of scattering interfaces. Diffusivity was found to scale extremely well with small scale porosity, which made it a good predictor of compressive strength. T2 - NDT-CE 2022 CY - Zurich, Switzerland DA - 16.08.2022 KW - Concrete KW - X-ray tomography KW - Ultrasound PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-563371 UR - https://www.ndt.net/article/ndtce2022/paper/61592_manuscript.pdf SP - 1 EP - 4 PB - NDT.net AN - OPUS4-56337 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Tschöke, K. A1 - Mueller, I. A1 - Memmolo, V. A1 - Sridaran Venkat, R. A1 - Golub, M. A1 - Eremin, A. A1 - Moix-Bonet, M. A1 - Möllenhoff, K. A1 - Lugovtsova, Yevgeniya A1 - Moll, J. A1 - Freitag, S. ED - Rizzo, P. ED - Milazzo, A. T1 - A Model-Assisted Case Study Using Data from Open Guided Waves to Evaluate the Performance of Guided Wave-Based Structural Health Monitoring Systems N2 - Reliability assessment of Structural Health Monitoring (SHM) systems poses new challenges pushing the research community to address many questions which are still open. For guided wave-based SHM it is not possible to evaluate the system performance without taking into account the target structure and applied system parameters. This range of variables would result in countless measurements. Factors like environmental conditions, structural dependencies and wave characteristics demand novel solutions for performance analysis of SHM systems compared to those relying on classical non-destructive evaluation. Such novel approaches typically require model-assisted investigations which may not only help to explain and understand performance assessment results but also enable complete studies without costly experiments. Within this contribution, a multi input multi output approach using a sparse transducer array permanently installed on a composite structure to excite and sense guided waves is considered. Firstly, the method and the analysis of path-based performance assessment are presented considering an open-access dataset from the Open Guided Wave platform. Then, a performance analysis of a guided wave-based SHM system using Probability of Detection is presented. To explain some unexpected results, the model-assisted investigations are used to understand the physical phenomena of wave propagation in the test specimen including the interaction with damage. Finally, issues and future steps in SHM systems’ performance assessment and their development are discussed. T2 - 10th European Workshop on Structural Health Monitoring (EWSHM 2022) CY - Palermo, Italy DA - 04.07.2022 KW - Performance assessment KW - Ultrasonic Guided Waves KW - Open Guided Waves Platform PY - 2022 SN - 978-3-031-07257-4 DO - https://doi.org/10.1007/978-3-031-07258-1 SN - 2366-2557 VL - 2 SP - 938 EP - 944 PB - Springer CY - Cham, Switzerland AN - OPUS4-55270 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Trogadas, P. A1 - Cho, J. I. S. A1 - Rasha, L. A1 - Lu, X. A1 - Kardjilov, N. A1 - Markötter, Henning A1 - Manke, I. A1 - Shearing, P. R. A1 - Brett, D. J. L. A1 - Coppens, M. O. T1 - A nature-inspired solution for water management in flow fields for electrochemical devices N2 - A systematic, nature-inspired chemical engineering approach is employed to solve the issue of flooding in electrochemical devices. The mechanism of passive water transport utilized by lizards living in arid environments is leveraged to design flow-fields with a microchannel structure on their surface, through which capillary pressure rapidly removes the water generated in the electrochemical device. This water management strategy is implemented in proton exchange membrane fuel cells (PEMFCs) with a lunginspired flow-field, which ensures uniform distribution of reactants across the catalyst layer. Jointly, this nature-inspired approach results in flood-free, stable operation at 100% RH and a B60% increase in current (B1.9 A cm-2) and peak power density (B650 mW cm−2) compared to current PEMFCs with a flood-prone, serpentine flow-field (B0.8 A cm-2 and 280 mW cm-2, respectively). This significant advance allows for PEMFC operation at fully humidified conditions. KW - Neutron imaging KW - X-ray tomography KW - Fuel cell PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-596662 DO - https://doi.org/10.1039/d3ee03666a VL - 17 SP - 2007 EP - 2017 PB - Royal Society of Chemistry (RSC) AN - OPUS4-59666 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Tsamos, Athanasios A1 - Evsevleev, Sergei A1 - Fioresi, R. A1 - Faglioni, F. A1 - Bruno, Giovanni T1 - A novel iterative algorithm to improve segmentations with deep convolutional neural networks trained with synthetic X-ray computed tomography data (i.S.Sy.Da.T.A) N2 - We propose a novel iterative segmentation algorithm (i.S.Sy.Da.T.A: Iterative Segmentation Synthetic Data Training Algorithm) employing Deep Convolutional Neural Networks and synthetic training data for X-ray tomographic reconstructions of complex microstructures. In our method, we reinforce the synthetic training data with experimental XCT datasets that were automatically segmented in the previous iteration. This strategy produces better segmentations in successive iterations. We test our algorithm with experimental XCT re constructions of a 6-phase Al-Si Matrix Composite reinforced with ceramic fibers and particles. We perform the analysis in 3D with a special network architecture that demonstrates good generalization with synthetic training data. We show that our iterative algorithm returns better segmentations compared to the standard single training approach. More specifically, phases possessing similar attenuation coefficients can be better segmented: for Al2O3 fibers, SiC particles, and Intermetallics, we see an increase of the Dice score with respect to the classic approach: from 0.49 to 0.54, from 0.66 to 0.72, and from 0.55 to 0.66 respectively. Furthermore, the overall Dice score increases from 0.77 to 0.79. The methods presented in this work are also applicable to other materials and imaging techniques. KW - Metal matrix composites (MMC) KW - Multi-phase materials KW - 3D imaging KW - Dice score KW - Automatic segmentation KW - Deep convolutional neural network (DCNN) KW - Modified U-net architectures PY - 2023 DO - https://doi.org/10.1016/j.commatsci.2023.112112 SN - 0927-0256 VL - 223 SP - 1 EP - 8 PB - Elsevier B.V. AN - OPUS4-57482 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Elahi, Seyed Ahmad A1 - Mehri Sofiani, F. A1 - Chaudhuri, Somsubhro A1 - Balbin, J.A. A1 - Larossa, N.O. A1 - De Waele, Wim T1 - A numerical study on fatigue strength degradation due to pitting corrosion of S355 structural steel in a marine environment N2 - This study investigates the influence of pitting corrosion on the fatigue strength of S355 steel, commonly used in offshore wind support structures. A short crack microstructural model is employed to estimate the degraded fatigue strength, effectively capturing the physics of the problem with minimal input parameters. Material characterization tests provide the key mechanical properties of S355 steel. A parametric analysis is used to examine the effects of pit shape, size, and aspect ratio. A generic relationship is identified between degraded fatigue strength and pit size for various pit aspect ratios. A sensitivity analysis reveals that the pit aspect ratio significantly influences fatigue strength, with sharper pits leading to greater reductions. The threshold stress intensity factor has a moderate effect, while in-air fatigue strength and grain size have minimal impacts. A case study is performed to evaluate the fatigue strength degradation of S355 steel exposed to the North Sea environment. Findings indicate that the most severe degradation occurs in the initial years of exposure, with the degradation rate declining over time. The results align well with experimental data, offering a robust framework for assessing structural integrity in a marine environment. KW - Fatigue strength KW - Corrosion fatigue KW - Pitting corrosion KW - Short crack KW - Micromechanical model KW - S355 structural steel PY - 2025 DO - https://doi.org/10.1016/j.engfailanal.2025.109669 SN - 1873-1961 VL - 177 SP - 1 EP - 21 PB - Elsevier Ltd. AN - OPUS4-63062 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bruno, Giovanni T1 - A paradigm shift in the description of creep in metals can only occur through multi-scale imaging N2 - The description of creep in metals has reached a high level of complexity; fine details are revealed by all sorts of characterization techniques and different theoretical models. However, to date virtually no fully microstructure-driven quantitative description of the phenomenon is available. This has brought to interesting inconsistencies; the classic description of (secondary) creep rests on the so-called power law, which however: a- has a pre-factor spanning over 10 orders of magnitude; b- has different reported exponents for the same material; c- has no explanation for the values of such exponents. Recently, a novel description (the so-called Solid State Transformation Creep (SSTC) Model) has been proposed to tackle the problem under a different light. The model has two remarkable features: 1- it describes creep as the accumulation of elementary strains due to dislocation motion; 2- it predicates that creep is proceeding by the evolution of a fractal arrangement of dislocations. Such description, however, needs a great deal of corroborating evidence, and indeed, is still incomplete. To date, we have been able to observe and somehow quantify the fractal arrangement of microstructures through Transmission Electron Microscopy (TEM), observe the accumulation of dislocations at grain boundaries by EBSD-KAM (Electron Back-Scattered Diffraction-Kernel Angular Misorientation) analysis, quantify the kinetic character (solid state transformation) of experimental creep curves, and estimate the sub-grain size of the fractal microstructure through X-ray refraction techniques. All pieces of the mosaic seem to yield a consistent picture: we seem being on the right path to reconstruct the whole elephant by probing single parts of it. What is still missing is the bond between the various scales of investigation. T2 - Korrelative Materialcharakterisierung 2022 CY - Dresden, Germany DA - 13.10.2022 KW - X-ray refraction KW - EBSD KW - Alloys KW - TEM KW - SEM PY - 2022 AN - OPUS4-56163 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bayerlein, Bernd A1 - Hanke, T. A1 - Muth, Thilo A1 - Riedel, Jens A1 - Schilling, Markus A1 - Schweizer, C. A1 - Skrotzki, Birgit A1 - Todor, A. A1 - Moreno Torres, Benjami A1 - Unger, Jörg F. A1 - Völker, Christoph A1 - Olbricht, Jürgen T1 - A Perspective on Digital Knowledge Representation in Materials Science and Engineering N2 - The amount of data generated worldwide is constantly increasing. These data come from a wide variety of sources and systems, are processed differently, have a multitude of formats, and are stored in an untraceable and unstructured manner, predominantly in natural language in data silos. This problem can be equally applied to the heterogeneous research data from materials science and engineering. In this domain, ways and solutions are increasingly being generated to smartly link material data together with their contextual information in a uniform and well-structured manner on platforms, thus making them discoverable, retrievable, and reusable for research and industry. Ontologies play a key role in this context. They enable the sustainable representation of expert knowledge and the semantically structured filling of databases with computer-processable data triples. In this perspective article, we present the project initiative Materials-open-Laboratory (Mat-o-Lab) that aims to provide a collaborative environment for domain experts to digitize their research results and processes and make them fit for data-driven materials research and development. The overarching challenge is to generate connection points to further link data from other domains to harness the promised potential of big materials data and harvest new knowledge. KW - Data infrastructures KW - Digital representations KW - Digital workflows KW - Knowledge graphs KW - Materials informatics KW - Ontologies KW - Vocabulary providers PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-546729 DO - https://doi.org/10.1002/adem.202101176 SN - 1438-1656 SP - 1 EP - 14 PB - Wiley-VCH GmbH CY - Weinheim AN - OPUS4-54672 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Fontoura Barroso, Daniel A1 - Epple, Niklas A1 - Niederleithinger, Ernst T1 - A Portable Low-Cost Ultrasound Measurement Device for Concrete Monitoring N2 - This paper describes a new ultrasonic measuring device called “W-Box”. It was developed based on the requirements of the DFG Forschergruppe (research unit) CoDA for a portable device for monitoring of concrete specimens, models and actual structures using embedded ultrasonic transducers as well as temperature and humidity sensors. The W-Box can send ultrasonic pulses with a variable frequency of 50–100 kHz to one selectable transducer and records signals from up to 75 multiplexed channels with a sample rate of 1 MHz and a resolution of 14 bits. In addition, it measures temperature and humidity with high accuracy, adjustable amplification, restarts automatically after a power failure and can be fully controlled remotely. The measured data are automatically stored locally on-site data quality checks and transferred to remote servers. The comparison of the W-Box with a laboratory setup using commercial devices proves that it is equally reliable and precise, at much lower cost. The W-Box also shows that their measurement capacities, with the used embedded ultrasonic transducers, can reach above 6 m in concrete. KW - Low-cost KW - Coda wave interferometry KW - Ultrasound KW - IoT KW - Non-destructive testing PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-546756 DO - https://doi.org/10.3390/inventions6020036 SN - 2411-5134 VL - 6 IS - 2 SP - 1 EP - 17 PB - MDPI CY - Basel, Switzerland AN - OPUS4-54675 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Agasty, Amit T1 - A Preliminary Study on the Scaling of RC Structures under Blasting Loading N2 - Current capabilities for full-scale field blast testing are highly resource intensive. Reliable small-scale experiments are an effective alternative. Characterization of the dynamic response and damage of RC elements to scaled blast loads was investigated in scaled-down field experiments. Spatially resolved information on the dynamic structural response to blast loading was obtained using distributed fiber optic acoustic sensing (DAS), acceleration sensors as well as piezoelectric pressure sensors. T2 - 46. Informationstagung Sprengtechnik CY - Siegen, Germany DA - 25.04.2025 KW - Explosives KW - Blast and scaling effects KW - Concrete PY - 2025 AN - OPUS4-62994 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Markötter, Henning A1 - Müller, Bernd R. A1 - Kupsch, Andreas A1 - Evsevleev, Sergei A1 - Arlt, T. A1 - Ulbricht, Alexander A1 - Dayani, Shahabeddin A1 - Bruno, Giovanni T1 - A Review of X-Ray Imaging at the BAMline (BESSY II) N2 - The hard X-ray beamline BAMline at BESSY II (Berlin, Germany) has now been in service for 20 years. Several improvements have been implemented in this time, and this review provides an overview of the imaging methods available at the BAMline. Besides classic full-field synchrotron X-ray computed tomography (SXCT), also absorption edge CT, synchrotron X-ray refraction radiography (SXRR), and synchrotron X-ray refraction tomography (SXRCT) are used for imaging. Moreover, virtually any of those techniques are currently coupled in situ or operando with ancillary equipment such as load rigs, furnaces, or potentiostats. Each of the available techniques is explained and both the current and the potential usage are described with corresponding examples. The potential use is manifold, the examples cover organic materials, composite materials, energy-related materials, biological samples, and materials related to additive manufacturing. The article includes published examples as well as some unpublished applications. KW - Material science KW - Radiography KW - Refraction KW - Tomography KW - X-ray imaging PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-572417 DO - https://doi.org/10.1002/adem.202201034 SN - 1438-1656 SP - 1 EP - 22 PB - Wiley VHC-Verlag AN - OPUS4-57241 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Waske, Anja T1 - A unique Authenticator for additively manufactured parts N2 - Components produced using additive manufacturing can be marked for unique identification and secure authentication [1,2]. Serial numbers and machine-readable codes can be used to identify the component, and link digital product-related data (i.e., a digital product passport) to the actual components. The most prevailing solution consists of local process manipulation, such as printing a quick response (QR) code [3] or a set of blind holes on the surface of the internal cavity of hollow components. However, local manipulation of components may alter the properties, and external tagging features can be altered or even removed by post-processing treatments. This work therefore aims to provide a new methodology for identification, authentication, and traceability of additively manufactured (AM) components using microstructural features that are unique to each part. X-ray computed tomography (XCT) was employed to image the microstructural features of AlSi10Mg parts. Based on size and geometry, the most prominent features were selected to create a unique digital authenticator. We implemented a framework in Python using open-access modules that can successfully create a digital object authenticator using the segmented microstructure information from XCT. The authenticator is stored as a QR code, along with the 3D information of the selected features. T2 - DGM Additive Berlin 2024 CY - Berlin, Germany DA - 12.06.2024 KW - Additive Fertigung KW - Authentifizierung KW - Mikrostruktur PY - 2024 AN - OPUS4-60957 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Waske, Anja T1 - A unique authenticator for additively manufactured parts derived from their microstructure N2 - In the field of additive manufacturing, the ability to uniquely identify and authenticate parts is crucial for certification, logistics, and anti-counterfeiting efforts. This study introduces a novel methodology that leverages the intrinsic microstructural features of additively manufactured components for their identification, authentication, and traceability. Unlike traditional tagging methods, such as embedding QR codes on the surface [1] or within the volume of parts, this approach requires no alteration to the printing process, as it utilizes naturally occurring microstructural characteristics. The proposed workflow [2] involves the analysis of 3D micro-computed tomography data to identify specific voids that meet predefined identification criteria. This method is demonstrated on a batch of 20 parts manufactured with identical process parameters, proving capable of achieving unambiguous identification and authentication. By establishing a tamper-proof link between the physical part and its digital counterpart, this methodology effectively bridges the physical and digital realms. This not only enhances the traceability of additively manufactured parts but also provides a robust tool for integrating digital materials, parts databases, and product passports with their physical counterparts. T2 - Artificial Intelligence in MSE CY - Bochum, Germany DA - 18.11.2025 KW - Authentication KW - Additive Manufacturing KW - Non-destructive testing PY - 2025 AN - OPUS4-65204 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Waske, Anja T1 - A unique authenticator for additively manufactured parts derived from their microstructure N2 - The international research community is currently devoting extensive resources to the development of digital material data spaces and the associated digital twins and product passports of materials and components. A common weak link in these projects to date has been the connection between physical components / samples and their digital data and documents. This is where the concept of the unique identification comes in. Components produced using additive manufacturing can be marked for unique identification and secure authentication [1,2]. Serial numbers and machine-readable codes can be used to identify the component, and link digital product-related data (i.e., a digital product passport) to the actual components. The most prevailing solution consists of local process manipulation, such as printing a quick response (QR) code [3] or a set of blind holes on the surface or the internal cavity of hollow components. However, local manipulation of components may alter the properties, and external tagging features can be altered or even removed by post-processing treatments. This work provides a new methodology for identification, authentication, and traceability of additively manufactured (AM) components using microstructural features that are unique to each part. X-ray computed tomography (XCT) was employed to image the microstructural features of a batch of AlSi10Mg parts. Based on size and geometry, the most prominent features were selected to create a unique digital authenticator. We implemented a framework in Python using open-access modules that can successfully create a digital object authenticator using the segmented microstructure information from XCT. We show that this method allows to authenticate individual parts from the build job based on its microstructural fingerprint. This is our contribution to enhancing the security and product protection of additively manufactured components. T2 - FEMS EUROMAT CY - Granada, Spain DA - 15.09.2025 KW - Authentication KW - Fingerprint KW - Non-destructive testing PY - 2025 AN - OPUS4-65202 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Waske, Anja T1 - A unique authenticator for additively manufactured parts derived from their microstructure N2 - Components produced using additive manufacturing can be marked for unique identification and secure authentication [1,2]. Serial numbers and machine-readable codes can be used to identify the component, and link digital product-related data (i.e., a digital product passport) to the actual components. The most prevailing solution consists of local process manipulation, such as printing a quick response (QR) code [3] or a set of blind holes on the surface of the internal cavity of hollow components. However, local manipulation of components may alter the properties, and external tagging features can be altered or even removed by post-processing treatments. This work therefore aims to provide a new methodology for identification, authentication, and traceability of additively manufactured (AM) components using microstructural features that are unique to each part. X-ray computed tomography (XCT) was employed to image the microstructural features of AlSi10Mg parts. Based on size and geometry, the most prominent features were selected to create a unique digital authenticator. We implemented a framework in Python using open-access modules that can successfully create a digital object authenticator using the segmented microstructure information from XCT. The authenticator is stored as a QR code, along with the 3D information of the selected features. T2 - MRS Spring Meeting Seattle CY - Seattle, WA, USA DA - 07.04.2025 KW - Additive Manufacturing KW - Fingerprint KW - Non-destructive testing PY - 2025 AN - OPUS4-65199 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Strangfeld, Christoph A1 - Wiehle, Philipp A1 - Munsch, Sarah Mandy T1 - About the dominance of mesopores in physisorption in amorphous materials N2 - Amorphous, porous materials represent by far the largest proportion of natural and men-made materials. Their pore networks consists of a wide range of pore sizes, including mesoand macropores. Within such a pore network, material moisture plays a crucial role in almost all transport processes. In the hygroscopic range, the pores are partially saturated and liquid water is only located at the pore fringe due to physisorption. Therefore, material parameters such as porosity or median pore diameter are inadequate to predict material moisture and moisture transport. To quantify the spatial distribution of material moisture, Hillerborg’s adsorption Theory is used to predict the water layer thickness for different pore geometries. This is done for all pore sizes, including those in the lower nanometre range. Based on this approach, it is shown that the material moisture is almost completely located in mesopores, although the pore network is highly dominated by macropores. Thus, mesopores are mainly responsible for the moisture storage capacity, while macropores determine the moisture transport capacity, of an amorphous material. Finally, an electrical analogical circuit is used as a model to predict the diffusion coefficient based on the pore-size distribution, including physisorption. KW - Physisorption KW - Mesopores KW - Amorphous materials KW - Macropores KW - Adsorbed water layer thickness KW - Material moisture KW - Moisture distribution PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-538525 UR - https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/NILGW2 DO - https://doi.org/10.3390/molecules26237190 VL - 26 IS - 23 SP - 1 EP - 22 PB - MDPI AN - OPUS4-53852 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -