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 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-534728 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 - 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 - Hilgenberg, Kai A1 - Daum, Werner A1 - Maierhofer, Christiane A1 - Altenburg, Simon A1 - Bruno, Giovanni A1 - Heckel, Thomas A1 - Skrotzki, Birgit A1 - Zerbst, Uwe A1 - Kranzmann, Axel A1 - Bettge, Dirk A1 - Sommer, Konstantin A1 - Seeger, Stefan A1 - Nitsche, Michael A1 - Günster, Jens A1 - Evans, Alexander T1 - Additive manufacturing at the BAM: We focus on Safety N2 - In Germany, the Federal Institute for Materials Research and Testing (BAM) is addressing challenges in the implementation of additive manufacturing on the industrial landscape for safety-critical applications. KW - Process development KW - Additive Manufacturing KW - In-situ Process Monitoring KW - Non-destructive Materials KW - Characterisation KW - Safety KW - Fatigue KW - Environment KW - Standardisation PY - 2019 UR - https://static.asminternational.org/amp/201910/22/ SN - 0882-7958 VL - 177 IS - 7 SP - 22 EP - 26 PB - ASM International CY - Materials Park, OH, USA AN - OPUS4-49780 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bruno, Giovanni A1 - Maierhofer, Christiane T1 - Advanced Characterization and On-Line Process Monitoring of Additively Manufactured Materials and Components N2 - Additive manufacturing (AM) techniques have risen to prominence in many industrial sectors. This rapid success of AM is due to the freeform design, which offers enormous possibilities to the engineer, and to the reduction of waste material, which has both environmental and economic advantages. Even safety-critical parts are now being produced using AM. This enthusiastic penetration of AM in our daily life is not yet paralleled by a thorough characterization and understanding of the microstructure of materials and of the internal stresses of parts. The same holds for the understanding of the formation of defects during manufacturing. While simulation efforts are sprouting and some experimental techniques for on-line monitoring are available, still little is known about the propagation of defects throughout the life of a component (from powder to operando/service conditions). This Issue was aimed at collecting contributions about the advanced characterization of AM materials and components (especially at large-scale experimental facilities such as Synchrotron and Neutron sources), as well as efforts to liaise on-line process monitoring to the final product, and even to the component during operation. The goal was to give an overview of advances in the understanding of the impacts of microstructure and defects on component performance and life at several length scales of both defects and parts. KW - Non-destructive Testing KW - Additive Manufacturing KW - Materials Characterization KW - Online Monitoring KW - Residual Stress KW - Thermography KW - Computed Tomography PY - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-556833 VL - 12 IS - 9 SP - 1 EP - 3 PB - MDPI CY - Basel, Schweiz AN - OPUS4-55683 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 - JOUR A1 - Mishurova, Tatiana A1 - Cabeza, Sandra A1 - Artzt, Katia A1 - Haubrich, J. A1 - Klaus, M. A1 - Genzel, Ch. A1 - Requena, G. A1 - Bruno, Giovanni T1 - An Assessment of Subsurface Residual Stress Analysis in SLM Ti-6Al-4V N2 - Ti-6Al-4V bridges were additively fabricated by selective laser melting (SLM) under different scanning speed conditions, to compare the effect of process energy density on the residual stress state. Subsurface lattice strain characterization was conducted by means of synchrotron diffraction in energy dispersive mode. High tensile strain gradients were found at the frontal surface for samples in an as-built condition. The geometry of the samples promotes increasing strains towards the pillar of the bridges. We observed that the higher the laser energy density during fabrication, the lower the lattice strains. A relief of lattice strains takes place after heat treatment. KW - Selective laser melting KW - Additive manufacturing KW - Heat treatment KW - Ti-6Al-4V KW - Synchrotron X-ray diffraction KW - Residual stress PY - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-395759 SN - 1996-1944 VL - 10 IS - 4 SP - Article 348, 1 EP - 14 AN - OPUS4-39575 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -