TY - JOUR A1 - Thiede, Tobias A1 - Mishurova, Tatiana A1 - Evsevleev, Sergei A1 - Serrano Munoz, Itziar A1 - Gollwitzer, Christian A1 - Bruno, Giovanni T1 - 3D shape analysis of powder for laser beam melting by synchrotron X-ray CT N2 - The quality of components made by laser beam melting (LBM) additive manufacturing is naturally influenced by the quality of the powder bed. A packing density <1 and porosity inside the powder particles lead to intrinsic voids in the powder bed. Since the packing density is determined by the particle size and shape distribution, the determination of these properties is of significant interest to assess the printing process. In this work, the size and shape distribution, the amount of the particle’s intrinsic porosity, as well as the packing density of micrometric powder used for LBM, have been investigated by means of synchrotron X-ray computed tomography (CT). Two different powder batches were investigated: Ti–6Al–4V produced by plasma atomization and stainless steel 316L produced by gas atomization. Plasma atomization particles were observed to be more spherical in terms of the mean anisotropy compared to particles produced by gas atomization. The two kinds of particles were comparable in size according to the equivalent diameter. The packing density was lower (i.e., the powder bed contained more voids in between particles) for the Ti–6Al–4V particles. The comparison of the tomographic results with laser diffraction, as another particle size measurement technique, proved to be in agreement. KW - Additive manufacturing KW - Laser beam melting KW - Synchrotron computed tomography KW - Powder analysis KW - Imaging PY - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-474070 SN - 2412-382X VL - 3 IS - 1 SP - 3, 1 EP - 12 PB - MDPI AN - OPUS4-47407 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 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-590912 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 - 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 U6 - 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 - 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 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-572417 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 - Evsevleev, Sergei A1 - Paciornik, S. A1 - Bruno, Giovanni T1 - Advanced Deep Learning-Based 3D Microstructural Characterization of Multiphase Metal Matrix Composites N2 - The quantitative analysis of microstructural features is a key to understanding the micromechanical behavior of metal matrix composites (MMCs), which is a premise for their use in practice. Herein, a 3D microstructural characterization of a five-phase MMC is performed by synchrotron X-ray computed tomography (SXCT). A workflow for advanced deep learning-based segmentation of all individual phases in SXCT data is shown using a fully convolutional neural network with U-net architecture. High segmentation accuracy is achieved with a small amount of training data. This enables extracting unprecedently precise microstructural parameters (e.g., volume fractions and particle shapes) to be input, e.g., in micromechanical models. KW - Computed tomography KW - Convolutional neural networks KW - Deep learning KW - Metal matrix composites KW - Segmentations PY - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-504261 SN - 1438-1656 VL - 22 IS - 4 SP - 1901197 PB - WILEY-VCH Verlag GmbH & Co. KGaA CY - Weinheim AN - OPUS4-50426 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Khimich, M. A. A1 - Prosolov, K. A. A1 - Mishurova, Tatiana A1 - Evsevleev, Sergei A1 - Monforte, X. A1 - Teuschl, A. H. A1 - Slezak, P. A1 - Ibragimov, E. A. A1 - Saprykin, A. A. A1 - Kovalevskaya, Z. G. A1 - Dmitriev, A. I. A1 - Bruno, Giovanni A1 - Sharkeev, Y. P. T1 - Advances in Laser Additive Manufacturing of Ti-Nb Alloys: From Nanostructured Powders to Bulk Objects N2 - The additive manufacturing of low elastic modulus alloys that have a certain level of porosity for biomedical needs is a growing area of research. Here, we show the results of manufacturing of porous and dense samples by a laser powder bed fusion (LPBF) of Ti-Nb alloy, using two distinctive fusion strategies. The nanostructured Ti-Nb alloy powders were produced by mechanical alloying and have a nanostructured state with nanosized grains up to 90 nm. The manufactured porous samples have pronounced open porosity and advanced roughness, contrary to dense samples with a relatively smooth surface profile. The structure of both types of samples after LPBF is formed by uniaxial grains having micro- and nanosized features. The inner structure of the porous samples is comprised of an open interconnected system of pores. The volume fraction of isolated porosity is 2 vol. % and the total porosity is 20 vol. %. Cell viability was assessed in vitro for 3 and 7 days using the MG63 cell line. With longer culture periods, cells showed an increased cell density over the entire surface of a porous Ti-Nb sample. Both types of samples are not cytotoxic and could be used for further in vivo studies. KW - Additive manufacturing KW - Biomaterials KW - Ti-Nb alloy KW - Nanostructured powder KW - Laser methods KW - Powder methods KW - Laser powder bed fusion PY - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-525344 VL - 11 IS - 5 SP - 1159 PB - MDPI AN - OPUS4-52534 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Evsevleev, Sergei A1 - Paciornik, Sidnei A1 - Gollwitzer, Christian A1 - Bruno, Giovanni T1 - Application of deep learning to the segmentation of synchrotron X-ray tomography data of multiphase metal matrix composites N2 - The 3D microstructure of an Al alloy matrix composite with two ceramic reinforcements was investigated by synchrotron X-ray tomography. A deep learning algorithm was used for the segmentation of four different phases. We show that convolutional networks with the U-Net architecture are able to solve complex segmentation tasks with small amount of training data. T2 - International Conference on Tomography of Materials & Structures CY - Cairns, Australia DA - 22.07.2019 KW - Synchrotron X-ray tomography KW - Deep learning KW - Segmentation KW - Metal matrix composite PY - 2019 AN - OPUS4-48606 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Mishurova, Tatiana A1 - Artzt, K. A1 - Haubrich, J. A1 - Evsevleev, Sergei A1 - Evans, Alexander A1 - Meixner, M. A1 - Serrano Munoz, Itziar A1 - Sevostianov, I. A1 - Requena, G. A1 - Bruno, Giovanni T1 - Connecting Diffraction-Based Strain with Macroscopic Stresses in Laser Powder Bed Fused Ti-6Al-4V N2 - The laser powder bed fusion (LPBF) production process often results in large residual stress (RS) in the parts. Nondestructive techniques to determine RS are badly needed. However, a reliable quantification of macro-RS (i.e., stress at the component level) by means of diffraction-based techniques is still a great challenge, because the link between diffraction-based strain and macro-RS is not trivial. In this study, we experimentally determine (by means of in-situ synchrotron radiation diffraction) this link for LPBF Ti-6Al-4V. We compare our results with commonly used models to determine the so-called diffraction elastic constants (DECs). We show that LPBF materials possess different DECs than wrought alloys, simply because their microstructural and mechanical properties are different. We also show that the existing models can be used to calculate DECs only if high accuracy of the RS values is not required. If the peculiarities of the microstructure have to be taken into account (as is the case of additively manufactured materials), a radically new approach is desirable. KW - Tiatanium KW - Synchrotron X-ray diffraction KW - Macroscopic stress KW - Laser powder bed fusion KW - Texture KW - Diffraction elastic constants PY - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-506483 VL - 51 IS - 6 SP - 3194 EP - 3204 PB - Springer AN - OPUS4-50648 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Evsevleev, Sergei A1 - Mishurova, Tatiana A1 - Cabeza, Sandra A1 - Bruno, Giovanni T1 - Damage Analysis in Metal Matrix Composites by means of Synchrotron Radiation Computed Tomography N2 - The damage evolution after compression tests of two types of MMC, consisting of eutectic AlSi12CuMgNi alloy and reinforced with 15vol% of Al2O3 fibers and with 7vol% of Al2O3 fibers+15vol% of SiC particles was studied by synchrotron CT. Internal damage at different pre-strain conditions in eutectic Si, intermetallic phases and Al2O3 fibers was observed, as well as debonding of SiC particles. T2 - HZB User Meeting CY - BESSY II, Berlin, Germany DA - 14.12.2017 KW - Aluminum KW - Metal Matrix Composite KW - Damage Analysis KW - Computed tomography KW - Synchrotron Radiation PY - 2017 AN - OPUS4-43467 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Serrano Munoz, Itziar A1 - Evsevleev, Sergei A1 - Laquai, René A1 - Müller, Bernd R. A1 - Kupsch, Andreas A1 - Bruno, Giovanni T1 - Damage characterization via 2D and 3D X-ray refraction techniques N2 - We present two examples of the potential of synchrotron X-ray refraction techniques. First, we focus on the 3D imaging of hydrogen assisted cracks in an EN AW – 6060 aluminium alloy which are otherwise undetected by absorption-based CT. The second work is a quantitative analysis of the damage evolution in an Al/Al2O3 Metal Matrix Composite during interrupted in-situ tensile load. T2 - International Conference on Tomography of Materials & Structures CY - Cairns, Australia DA - 22.07.2019 KW - X-ray refraction KW - Analyzer-based imaging KW - Aluminum alloy KW - Metal matrix composite KW - Damage characterization PY - 2019 AN - OPUS4-48604 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -