TY - CONF A1 - Serrano, Itziar A1 - Müller, Bernd A1 - Kupsch, Andreas A1 - Bruno, Giovanni T1 - X-ray refractio 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 Refaction 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 - 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 - 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 - 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 - JOUR A1 - Tsamos, Athanasios A1 - Evsevleev, Sergei A1 - Bruno, Giovanni T1 - Noise and blur removal from corrupted X-ray computed tomography scans: A multilevel and multiscale deep convolutional framework approach with synthetic training data (BAM SynthCOND) N2 - Regardless of the experimental care practiced in acquiring X-ray computed tomography (XCT) data, artifacts might still exist, such as noise and blur. This is typical for fast XCT data acquisitions (e.g., in-situ investigations), or low-dose XCT. Such artifacts can complicate subsequent analysis of the data. Digital filters can moderately cure extensive artifacts. The selection of filter type, intensity, and order of application is not always straight forward. To tackle these problems, a complete sequential multilevel, multi-scale framework: BAM SynthCOND, employing newly designed deep convolutional neural networks (DCNNs), was formulated. Although data conditioning with neural networks is not uncommon, the main complication is that completely artifact-free XCT data for training do not exist. Thus, training data were acquired from an in-house developed library (BAM SynthMAT) capable of generating synthetic XCT material microstructures. Some novel DCNN architectures were introduced (2D/3D ACEnet_Denoise, 2D/3D ACEnet_Deblur) along with the concept of Assertive Contrast Enhancement (ACE) training, which boosts the performance of neural networks trained with continuous loss functions. The proposed methodology accomplished very good generalization from low resemblance synthetic training data. Indeed, denoising, sharpening (deblurring), and even ring artifact removal performance were achieved on experimental post-CT scans of challenging multiphase Al-Si Metal Matrix Composite (MMC) microstructures. The conditioning efficiencies were: 92% for combined denoising/sharpening, 99% for standalone denoising, and 95% for standalone sharpening. The results proved to be independent of the artifact intensity. We believe that the novel concepts and methodology developed in this work can be directly applied on the CT projections prior to reconstruction, or easily be extended to other imaging techniques such as: Microscopy, Neutron Tomography, Ultrasonics, etc. KW - XCT Data Conditioning KW - Denoising Deblurring Sharpening KW - Deep Convolutional Neural Network (DCNN) KW - Synthetic Training Data KW - ACEnet KW - Metal Matrix Composite (MMC) PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-579129 DO - https://doi.org/10.1016/j.tmater.2023.100011 VL - 2 SP - 1 EP - 16 PB - Elsevier B.V. AN - OPUS4-57912 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Dayani, Shahabeddin A1 - Markötter, Henning A1 - Krug von Nidda, Jonas A1 - Schmidt, Anita A1 - Bruno, Giovanni T1 - Quantification of the Deep Discharge Induced Asymmetric Copper Deposition in Lithium‐Ion Cells by Operando Synchrotron X‐Ray Tomography N2 - AbstractLithium‐ion cells connected in series are prone to an electrical safety risk called overdischarge. This paper presents a comprehensive investigation of the overdischarge phenomenon in lithium‐ion cells using operando nondestructive imaging. The study focuses on understanding the behavior of copper dissolution and deposition during overdischarge, which can lead to irreversible capacity loss and internal short‐circuits. By utilizing synchrotron X‐ray computed tomography (SXCT), the concentration of dissolved and deposited copper per surface area is quantified as a function of depth of discharge, confirming previous findings. The results also highlight for the first time a nonuniform distribution pattern for copper deposition on the cathode. This research provides insights for safer battery cell design. KW - Lithium Ion Batteries KW - Deep Discharge KW - Computer Tomography KW - Copper Deposition KW - Litium Ion Cells PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-592717 DO - https://doi.org/10.1002/admt.202301246 SP - 1 EP - 7 PB - Wiley AN - OPUS4-59271 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Zurcher, Theo A1 - Serrano-Munoz, Itziar A1 - Mishurova, Tatiana A1 - Abreu Faria, Guilherme A1 - Degener, Sebastian A1 - Fridrici, Vincent A1 - Charkaluk, Eric A1 - Bruno, Giovanni T1 - Sliding wear resistance and residual stresses of parts repaired by laser metal deposition N2 - Large temperature gradients inherent to additive manufacturing (AM) processes induce large residual stress (RS) in the final part. Because RS can influence the tribological properties, this study focuses on the relationship between wear sliding properties and RS in IN718 coatings. Such coatings were deposited with a Laser metal deposition (LMD) machine using two different scanning strategies. The wear resistance and RS state were investigated after surface milling. RS were measured before and after wear tests on a reciprocating sliding test apparatus. Two different X-ray diffraction techniques were employed to measure the surface and subsurface state RS: Laboratory Energy Dispersive X-ray Diffraction (LEDXD) and Synchrotron X-ray Energy Dispersive Diffraction (SXEDD). Due to the milling process, the coatings show similar depth distributions of RS from 22 to 92 μm depth, but exhibit different magnitudes depending on the scanning strategy used. Reciprocating sliding wear tests induced high compressive residual stresses that erased the initial RS state, and a similar wear behavior was observed in the two samples. These samples possess similar texture and grain morphology. This demonstrates that the influence of RS on wear resistance is a second-order effect. Nevertheless, it was observed that RS can still impact the wear performance at the early testing stages of the repaired parts. KW - Additive manufacturing KW - Wear resistance KW - Residual stress PY - 2023 DO - https://doi.org/10.1007/s10853-023-09129-4 SN - 0022-2461 SP - 1 EP - 18 PB - Springer AN - OPUS4-59084 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 - Mouiya, M. A1 - Martynyuk, M. A1 - Kupsch, Andreas A1 - Laquai, R. A1 - Müller, Bernd R. A1 - Doyen, N.T. A1 - Tamraoui, Y. A1 - Serrano Munoz, Itziar A1 - Huger, M. A1 - Kachanov, M. A1 - Bruno, Giovanni T1 - The stress–strain behavior of refractory microcracked aluminum titanate: The effect of zigzag microcracks and its modeling N2 - The stress–strain behavior of ceramics, such as aluminum titanate, has certain features that are unusual for brittle materials—in particular, a substantial nonlinearity under uniaxial tension, and load–unload hysteresis caused by the sharp increase of the incremental stiffness at the beginning of unloading. These features are observed experimentally and are attributed to microcracking. Here we compare different degrees of stress–strain nonlinearity of aluminum titanate materials and quantitatively model them. We use advanced mechanical testing to observe the mechanical response at room and high temperature; electron microscopy, and X-ray refraction radiography to observe the microstructural changes. Experiments show that two types of microcracks can be distinguished: (i) microcracks induced by cooling from the sintering temperature (due to heterogeneity and anisotropy of thermal expansion), with typical sizes of the order of grain size, and (ii) much larger microcracks generated by the mechanical loading. The two microcrack types produce different effects on the stress–strain curves. Such microcracks and the features of the stress–strain behavior depend on the density of the cooling-induced microcracks and on the distribution of grain sizes. They are modeled analytically and numerically. KW - Hystersis KW - Nonlinear stress-strain curve KW - Refractory KW - Stiffness KW - X-ray refraction PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-580517 DO - https://doi.org/10.1111/jace.19325 SN - 1551-2916 VL - 106 SP - 6995 EP - 7008 PB - Wiley-Blackwell CY - Oxford [u.a.] AN - OPUS4-58051 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -