TY - JOUR A1 - Serrano Munoz, Itziar A1 - Fernández, R. A1 - Saliwan-Neumann, Romeo A1 - González-Doncel, G. A1 - Bruno, Giovanni T1 - Dislocation structures after creep in an Al-3.85 %Mg alloy studied using EBSD-KAM technique JF - Materials Letters N2 - The electron backscatter diffraction (EBSD) technique is used to investigate the dislocation structures formed after steady-state creep deformation of an Al-3.85%Mg alloy. This material is crept at two different stress levels, corresponding to the so-called power-law and power-law breakdown regimes. The results show that, regardless of the creep stress level, the strain tends to localize, leading to the formation of intragranular bands. The thickness of such bands is larger when the material is tested at loads corresponding to the power-law breakdown. This suggests enhanced diffusion by dislocation pipes. KW - Steady-state creep KW - Al-3.85%Mg alloy KW - Power-law breakdown KW - Electron backscatter diffraction (EBSD) KW - Denoising filter PY - 2023 DO - https://doi.org/10.1016/j.matlet.2023.133978 SN - 0167-577X VL - 337 SP - 1 EP - 4 PB - Elsevier B.V. AN - OPUS4-56941 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Schröder, Jakob A1 - Heldmann, A. A1 - Hofmann, M. A1 - Evans, Alexander A1 - Petry, W. A1 - Bruno, Giovanni T1 - Determination of diffraction and single-crystal elastic constants of laser powder bed fused Inconel 718 JF - Materials Letters N2 - High energy X-ray synchrotron diffraction is used to investigate the elastic anisotropy of the nickel-based superalloy IN718 produced by laser powder bed fusion (PBF-LB). This material is characterized by a columnar grain morphology with some crystallographic texture. The material is subjected to elastic loading to determine the diffraction elastic constants (DECs). Furthermore, the single-crystal elastic constants (SCEC) are refined from these experiments using different micromechanical models. The results show that each micromechanical model predicts a specific set of SCEC that well describes the elastic anisotropy of PBF-LB/IN718. KW - Mechanical Engineering KW - Mechanics of Materials KW - Condensed Matter Physics KW - General Materials Science PY - 2023 DO - https://doi.org/10.1016/j.matlet.2023.135305 SN - 0167-577X VL - 353 SP - 1 EP - 5 PB - Elsevier B.V. AN - OPUS4-58477 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bruno, Giovanni T1 - BAM Akademie 2023- Modul 2 Computertomographie N2 - Hier werden die Grundprinzipien der Computertomographie dargestellt, die Artefakte, die bei den Messungen auftreten und die Datenanalysemethoden erklärt. T2 - BAM Akademie - Webinar Reihe CY - Berlin, Germany DA - 05.10.2023 KW - Artefakte KW - Auflösung KW - Radon Transformation KW - Rekonstruktion KW - Metrologie PY - 2023 AN - OPUS4-58509 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Mishurova, Tatiana A1 - Bruno, Giovanni T1 - BAM activities in material characterization by advanced X-ray imaging 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). Also, two successful research project in collaboration with CAM2, Sweden are presented. T2 - CAM2 Annual Meeting CY - Gothenburg, Sweden DA - 25.10.2023 KW - Additive manufacturing KW - Residual stress KW - X-ray computed tomography PY - 2023 AN - OPUS4-58828 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Müller, Bernd R. A1 - Kupsch, Andreas A1 - Serrano Munoz, Itziar A1 - Laquai, René A1 - Bruno, Giovanni T1 - Anwendungen der Röntgenrefraktionstechnik zur zerstörungsfreien Charakterisierung von Keramiken und Verbundwerkstoffen T2 - DGZfP-Berichtsband BB 180 N2 - Die Brechung von Röntgenstrahlen (Röntgenrefraktion) an Grenzflächen zwischen Materialien unterschiedlicher Dichte ist analog zur Ablenk-ung von sichtbarem Licht an z.B. Glasoberflächen. Es gibt jedoch zwei wesentliche Unterschiede: a) konvexe Grenzflächen verursachen Divergenz (d.h. der Brechungsindex n ist kleiner als 1), und b) die Ablenkungswinkel sind sehr klein, und reichen von einigen Bogensekunden bis zu einigen Bogenminuten (d.h. n ist nahe bei 1); Wie auch bei sichtbarem Licht ist die Ablenkungsrichtung der Röntgenstrahlen abhängig von der Orientierung der durchstrahlten Grenzfläche. Aufgrund dieser Eigenschaften eignen sich Röntgenrefraktionsmethoden hervorragend für: a) die Erkennung und Quantifizierung von Defekten wie Poren und Mikrorissen und b) die Bewertung von Porosität und Partikeleigenschaften wie Orientierung, Größe und räumliche Verteilung. Wir zeigen die Anwendung der Röntgenrefraktionsradiographie (2,5D Technik) und der -tomographie (3D Technik) für die Untersuchung verschiedener Probleme in der Werkstoffwissenschaft und -technologie: a) Sintern von SiC-Grünkörpern b) Porositätsanalyse in Dieselpartikelfiltersilikaten c) Faser-Matrix-Haftung in Metall- und Polymermatrixverbundwerkstoffen d) Mikrorissbildung in Glaskeramik. Wir zeigen, dass der Einsatz von Röntgenrefraktionsmethoden quantitative Ergebnisse liefert, die direkt als Parameter in Werkstoffmodellen verwendet werden können. T2 - DACH-Jahrestagung 2023 CY - Friedrichshafen, Germany DA - 15.05.2023 KW - Röntgen-Refraktion KW - Verbundwerkstoffe KW - Keramik PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-576171 UR - https://jahrestagung.dgzfp.de/Portals/jt2023/BB180/Inhalt/p9.pdf UR - https://jahrestagung.dgzfp.de/Portals/jt2023/BB180/Inhalt/default.htm SN - 978-3-947971-29-9 SP - 1 EP - 16 PB - Deutsche Gesellschaft für Zerstörungsfreie Prüfung (DGZfP) CY - Berlin AN - OPUS4-57617 LA - deu 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) JF - Advanced Engineering Materials 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 - 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) JF - Computational Materials Science 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 - 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 JF - Advanced Engineering Materials 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 - 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 -