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 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 A1 - Serrano-Munoz, Itziar A1 - Kupsch, Andreas A1 - Müller, Bernd R. T1 - X-Ray-Refraction-Imaging-Techniques high-resolution microstructural characterization N2 - X-ray refraction is analogous to visible light deflection by matter; it occurs at boundaries between different media. The main difference between visible light and X-rays is that in the latter case deflection angles are very small, from a few seconds to a few minutes of arc (i.e., the refraction index n is near to 1). Trivially but importantly, deflection of X-rays is also sensitive to the orientation of the object boundaries. These features make X-ray refraction techniques extremely suitable to a) detect defects such as pores and microcracks and quantify their densities in bulk (not too heavy) materials, and b) evaluate porosity and particle properties such as orientation, size, and spatial distribution (by mapping). While X-ray refraction techniques cannot in general image single defects, their detectability is simply limited by the wavelength of the radiation. We thereby show the application of X-ray refraction 2D mapping (topography) and tomography to different sorts of problems in materials science and technology: 1) Sintering of SiC green bodies; 2) Porosity analysis in additively manufactured alloys; 3) Fiber de-bonding in metal and polymer matrix composites. Such techniques, especially at the Synchrotron BESSY II, Berlin, Germany, can be used in-situ, i.e. when the specimen is subjected to temperatures or external loads. Applications of in-situ X-ray refraction radiography on aluminum alloys and composites are also shown. The use of X-ray refraction analysis yields quantitative information, which can be directly input in kinetics, mechanical and damage models. T2 - ICT 2023 CY - Fürth, Germany DA - 27.02.2023 KW - X-ray refraction KW - Composites KW - In-situ KW - Additive Manufacturing KW - Sintering KW - Ceramics PY - 2023 AN - OPUS4-57200 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 - Khrapov, D. A1 - Paveleva, A. A1 - Kozadayeva, M. A1 - Evsevleev, Sergei A1 - Mishurova, Tatiana A1 - Bruno, Giovanni A1 - Surmenev, R. A1 - Koptyug, A. A1 - Surmeneva, M. T1 - Trapped powder removal from sheet-based porous structures based on triply periodic minimal surfaces fabricated by electron beam powder bed fusion N2 - Electron Beam Powder Bed Fusion-manufactured (E-PBF) porous components with narrow pores or channels and rough walls or struts can be filled with trapped powder after the manufacturing process. Adequate powder removal procedures are required, especially for high-density porous structures. In the present research, sheetbased porous structures with different thicknesses based on triply periodic minimal surfaces fabricated by EPBF were subjected to different post-processing methods, including a traditional powder recovery system for EPBF, chemical etching and ultrasound vibration-assisted powder removal. Wall thickness, internal defects, microstructure and morphology features, powder distribution inside the specimens, mechanical properties and deformation modes were investigated. A powder recovery system could not remove all residual powder from dense structures. In turn, chemical etching was effective for surface morphology changes and subsurface layers elimination but not for powder removal, as it affected the wall thickness, considerably influencing the mechanical properties of the whole structure. The ultrasound vibration method was quite effective for the removal of residual powder from sheet-based TMPS structures and without a severe degradation of mechanical properties. 10.1016/j.msea.2022.144479 Ultrasound vibration also caused grain refinement. KW - Additive manufacturing KW - Residual powder removal KW - Ti6Al4V alloy KW - Electron beam powder bed fusion KW - TPMS structures PY - 2023 DO - https://doi.org/10.1016/j.msea.2022.144479 VL - 862 SP - 1 EP - 16 PB - Elsevier B.V. AN - OPUS4-56564 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 - CHAP A1 - Bruno, Giovanni A1 - Kachanov, M. A1 - Sevostianov, I. ED - Bruno, Giovanni ED - Altenbach, H. ED - Eremeyev, V. ED - Müller, W. ED - Gutkin, M. T1 - Micromechanical Modeling of Non-linear Stress–Strain Behavior of Polycrystalline Microcracked Ceramics N2 - We discuss the non-linear stress–strain behavior of microcracked polycrystalline ceramics under uniaxial tension and compression (displacement control). Micromechanics explanation and modeling of its basic features, such as non-linearity and hysteresis in stress–strain curves, are developed, with stable microcrack propagation and “roughness” of intergranular cracks playing critical roles in tension and crack sliding playing a critical role in compression. Experiments involving complex loading histories are explained, and themodel is shown to reproduce the basic features of the observed stress–strain curves. KW - Neutron Diffraction KW - X-ray diffraction KW - Mechanical Properties KW - X-ray refraction radiography KW - Microcracking KW - Aluminum Titanate PY - 2023 SN - 978-3-031-28743-5 DO - https://doi.org/10.1007/978-3-031-28744-2 SN - 1869-8433 SP - 1 EP - 22 PB - Springer CY - Heidelberg AN - OPUS4-57938 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bruno, Giovanni T1 - Strategies to use neutrons as an industrial problem-solving tool N2 - For how trivial or provocative it can sound, the best neutron spectrometer in the world does not produce science and technology by itself. By definition of “Materials Science”, neutron scattering data on engineering materials must be used as a tool to understand, and even tailor, materials performance. In order for this to happen, neutron data need to be 1. Acquired under the most relevant condition possible 2. Coupled to other experimental techniques 3. Capitalized by means of proper simulations and data analysis Point 1- calls for an intense use and the development of top-notch of in-situ techniques; Point 2- means that the sole use of neutron data will not lead to any solution of a global problem; All points above hint to the fact that access to neutron sources is not routine, and therefore it is imperative to search ways to make neutron data rentable and sustainable for the material science and industrial research community. In this presentation, and based on two examples, we will show a couple of strategies to combine neutron data with other experiments, and with theoretical models to raise the validity of experiments to the level of problem-solving. As one might imagine, these are only a few among the almost infinite combinations possible to help improving material properties, performance, and safety, i.e., ripe for everyday use. T2 - MORIS Workshop CY - Garching, Germany DA - 26.04.2023 KW - Neutron Diffraction KW - Residual Stress KW - Large Scale Facilities KW - X-ray Computed Tomography PY - 2023 AN - OPUS4-57361 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bruno, Giovanni T1 - X-ray refraction techniques non-destructively quantify and classify defects in am materials N2 - X-ray refraction is analogous to visible light deflection by matter; it occurs at boundaries between different media. The main difference between visible light and X-rays is that in the latter case deflection angles are very small, from a few seconds to a few minutes of arc (i.e., the refraction index n is near to 1). Importantly, deflection of X-rays is also sensitive to the orientation of the object boundaries. These features make X-ray refraction techniques extremely suitable to a) detect defects such as pores and microcracks and quantify their densities in bulk (not too heavy) materials, and b) evaluate porosity and particle properties such as orientation, size, and spatial distribution (by mapping). While X-ray refraction techniques cannot in general image single defects, they can detect objects with size above a few wavelengths of the radiation. Such techniques, especially at the Synchrotron BESSY II, Berlin, Germany, can be used in-situ, i.e. when the specimen is subjected to temperatures or external loads. The use of X-ray refraction analysis yields quantitative information, which can be directly input in kinetics, mechanical and damage models. We hereby show the application of non-destructive X-ray refraction radiography (SXRR, 2D mapping also called topography) to problems in additive manufacturing: 1) Porosity analysis in PBF-LM-Ti64. Through the use of SXRR, we could not only map the (very sparse) porosity distribution between the layers and quantify it, but also classify, and thereby separate, the filled porosity (unmolten powder) from the keyhole and gas pores (Figure 1). 2) In-situ heat treatment of laser powder bed fusion PBF-LM-AlSi10Mg to monitor microstructure and porosity evolution as a function of temperature (Figure 2). By means of SXRR we indirectly observed the initial eutectic Si network break down into larger particles as a function of increasing temperature. We also could detect the thermally induced porosity (TIP). Such changes in the Si-phase morphology upon heating is currently only possible using scanning electron microscopy, but with a much smaller field-of-view. SXRR also allows observing the growth of some individual pores, usually studied via X-ray computed tomography, but again on much smaller fields-of-view. Our results show the great potential of in-situ SXRR as a tool to gain in-depth knowledge of the defect distribution and the susceptibility of any material to thermally induced damage and/or microstructure evolution over statistically relevant volumes. T2 - AAMS 2023 CY - Madrid, Spain DA - 27.09.2023 KW - X-ray refraction radiography KW - Defects KW - Large scale facilities KW - Computed tomography PY - 2023 AN - OPUS4-58508 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 -