TY - CONF A1 - Eddah, Mustapha A1 - Markötter, Henning A1 - Mieller, Björn A1 - Widjaja, Martinus Putra A1 - Beckmann, Jörg A1 - Bruno, Giovanni T1 - Multi-Energy High Dynamic Range (HDR) Synchrotron X-ray Computed Tomography applied to LTCC samples N2 - Synchrotron X-ray computed tomography (SXCT) is regularly used in materials science to correlate structural properties with macroscopic properties and to optimize manufacturing processes. The X-ray beam energy must be adapted to the sample properties, such as size and density. If both strongly and weakly absorbing materials are present, the contrast to the weakly absorbing materials is lost, resulting in image artifacts and a poor signal-to-noise ratio (SNR). One example is a low-temperature co-fired ceramics (LTCC), in which metal connections are embedded in a ceramic matrix and form 3-dimensional conducting structures. This article describes a method of combining SXCT scans acquired at different beam energies, significantly reducing metal artifacts, and improving image quality. We show how to solve the difficult task of merging the scans at low and high beam energy. Our proposed merging approach achieves up to 35% improvement in SNR within ceramic regions adjacent to metallic conductors. In this way, previously inaccessible regions within the ceramic structure close to the metallic conductors are made accessible. The paper further discusses methodological requirements, limitations, and potential extensions of the presented multienergy SXCT merging technique. T2 - iCT 2026 CY - Linz, Austria DA - 10.02.2026 KW - Synchrotron x-ray CT KW - Multi-energy CT KW - Low-Temperature cofired ceramics PY - 2026 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-655848 SP - 1 EP - 7 CY - e-Journal of Nondestructive Testing AN - OPUS4-65584 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bruno, Giovanni T1 - How 3D X-ray Imaging and Residual Stress Analysis contribute to safety of materials and structures N2 - The safety of materials and structures can be detrimentally influenced by residual stresses (RS) and defect populations (voids or other features leading to failure) if they are not correctly accounted for in the design. Therefore, the accurate characterization of these features and the consideration of their impact is crucial for the safe design of components. The ability to characterize these features non-destructively enables the direct correlation on resulting mechanical performance. 3D X-ray computed tomography (XCT) is used to resolve and quantitively analyze microstructural features (i.e., voids, porosity). This is often used to assess the capability of the manufacturing route, i.e., additive manufacturing (AM). The non-destructive nature of the method also enables the study of the evolution of damage in materials from such microstructural features [1]. Using in-situ methods such as compression or tension, the propagation of damage from initial microstructure can be assessed, aiding our understanding of which features are detrimental to safety [3]. Diffraction based residual stress analysis methods including high energy X-ray and neutron diffraction can be used to study the residual stress gradients from the surface, subsurface and into the bulk non-destructively. These methods can be used to study the influence of heat treatments on residual stress and can be combined with XCT results to correlate the interaction of residual stresses with microstructural features (i.e., void clusters). This talk will give an overview of the capabilities and opportunities of 3D XCT and diffraction based residual stress analysis to close the gap in our understanding of material degradation on mechanical performance, enabling manufacturers to adjust their designs accordingly for safety critical applications. A particular focus will be made on examples where the two advanced techniques are combined to enhance such understanding. T2 - MaterialsWeek 2025 CY - Frankfurt am Main, Germany DA - 02.04.2025 KW - Neutron Diffraction KW - Residual Stress KW - X-ray Computed Tomography KW - Additive Manufacturing KW - Large Scale facilites KW - Creep KW - Defects KW - BAMline PY - 2025 AN - OPUS4-62895 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bruno, Giovanni T1 - X-Ray absorption, refraction, and diffraction techniques for the characterization and non-destructive testing of materials N2 - The combination of tomographic, microstructural data with other experimental techniques and with modeling is paramount, if we want to extract the maximum amount of information on material and component properties. In particular, quantitative image analysis, statistical approaches, direct discretization of tomographic reconstructions represent concrete possibilities to extend the power of the tomographic 3D representation to insights into the material and component performance. This logic thread equally holds for industrial and academic research and valorizes expensive experiments such as those carried out at synchrotron sources, which cannot be daily repeated. I will show a few examples of possible use of X-ray tomographic data for quantitative assessment of damage evolution and microstructural properties, as well as for non-destructive testing. Examples of micro-structured inhomogeneous materials will be given, such as Composites, Ceramics, Concrete, and Additively manufactured parts. I will also show how X-ray refraction computed tomography (CT) can be highly complementary to classic absorption CT, being sensitive to internal interfaces. Additionally, I will show how Neutron Diffraction, which is extremely well suited to the study of internal stresses, both residual and under external load, can well be coupled to the microstructural framework gained by CT, allowing understanding the microstructure-property relationships in materials. T2 - VDI FA101- Anwendungsnahe zerstörungsfreie Werkstoff und Bauteilprüfung Jährliches Treffen CY - Berlin, Germany DA - 10.04.2025 KW - Neutron Diffraction KW - X-ray computed tomography KW - Large Scale Facilities KW - X-ray refraction radiography KW - BAMline PY - 2025 AN - OPUS4-62939 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Eddah, Mustapha A1 - Markötter, Henning A1 - Mieller, Björn A1 - Widjaja, Martinus Putra A1 - Beckmann, Jörg A1 - Bruno, Giovanni T1 - Multi-energy high dynamic range synchrotron X-ray computed tomography N2 - Synchrotron X-ray computed tomography (SXCT) is regularly used in materials science to correlate structural properties with macroscopic properties and to optimize manufacturing processes. The X-ray beam energy must be adapted to the sample properties, such as size and density. If both strongly and weakly absorbing materials are present, the contrast to the weakly absorbing materials is lost, resulting in image artifacts and a poor signal-tonoise ratio (SNR). One particular example is a low-temperature co-fired ceramics (LTCC), in which metal connections are embedded in a ceramic matrix and form 3-dimensional conducting structures. This article describes a method of combining SXCT scans acquired at different beam energies, significantly reducing metal artifacts, and improving image quality. We show how to solve the difficult task of merging the scans at low and high beam energy. Our proposed merging approach achieves up to 35 % improvement in SNR within ceramic regions adjacent to metallic conductors. In this way, previously inaccessible regions within the ceramic structure close to the metallic conductors are made accessible. The paper further discusses methodological requirements, limitations, and potential extensions of the presented multi-energy SXCT merging technique. KW - Synchrotron computed tomography KW - Reconstruction algorithm KW - High dynamic range KW - Data merging KW - Low-temperature cofired ceramics PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-652339 DO - https://doi.org/10.1016/j.tmater.2025.100079 SN - 2949-673X VL - 9 SP - 1 EP - 10 PB - Elsevier CY - Amsterdam AN - OPUS4-65233 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Fardan, Ahmed A1 - Fazi, Andrea A1 - Schröder, Jakob A1 - Mishurova, Tatiana A1 - Deckers, Tobias A1 - Bruno, Giovanni A1 - Thuvander, Matthias A1 - Markström, Andreas A1 - Brodin, Hakan A1 - Hryha, Eduard T1 - Microstructure tailoring for crack mitigation in CM247LC manufactured by powder bed fusion – Laser beam N2 - Tailored microstructures in powder bed fusion – laser beam (PBF-LB) can aid in crack mitigation of non-weldable Ni-base superalloys such as CM247LC. This study explores the effect of a range of stripe widths from 5 mm down to 0.2 mm to control solidification cracking, microstructure, and residual stress in CM247LC manufactured by PBF-LB. The decrease in melt pool depth with the reduction in stripe width from 5 to 0.2 mm promoted the < 100 > crystallographic texture along the build direction. The crack density measurements indicated that there is an increase from 0.62 mm/mm2 (5 mm) to 1.71 mm/mm2 (1 mm) followed by a decrease to 0.33 mm/mm2 (0.2 mm). Atom probe tomography investigations at high-angle grain boundaries revealed that there is higher Hf segregation in 0.2 mm stripe width when compared to 5 mm. This indicates that the cracking behavior is likely influenced by the grain boundary segregation which in turn is dependent on melt pool shape/size and mushy zone length indicated by accompanying simulations. Residual stress, measured by X-ray diffraction, decreased from 842 MPa (5 mm) to 690 MPa (1 mm), followed by an abnormal rise to 842 MPa (0.7 mm) and 875 MPa (0.5 mm). This residual stress behavior is likely associated with the cracks acting as a stress relief mechanism. However, the 0.2 mm stripe width exhibited the lowest stress of 647 MPa, suggesting a different mechanism for stress relief, possibly due to re-melting. These findings highlight the critical role of stripe width as a scan strategy in PBF-LB processing of crack-susceptible alloys. KW - Additive manufacturing KW - Residual stress KW - Scanning strategy KW - Non-weldable superalloy KW - Solidification cracking PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-624606 DO - https://doi.org/10.1016/j.addma.2025.104672 SN - 2214-7810 VL - 99 SP - 1 EP - 14 PB - Elsevier B.V. AN - OPUS4-62460 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CHAP A1 - Chand, Keerthana A1 - Fritsch, Tobias A1 - Hejazi, Bardia A1 - Poka, Konstantin A1 - Bruno, Giovanni T1 - Deep Learning Based 3D Volume Correlation for Additive Manufacturing Using High-Resolution Industrial X-Ray Computed Tomography N2 - Quality control in Additive Manufacturing (AM) is vital for industrial applications in areas such as the automotive, medical, and aerospace sectors. Geometric inaccuracies caused by shrinkage and deformations can compromise the life and performance of additively manufactured components. Such deviations can be quantified using Digital Volume Correlation (DVC), which compares the Computer-Aided Design (CAD) model with the X-ray Computed Tomography (XCT) geometry of the components produced. However, accurate registration between the two modalities is challenging due to the absence of a ground truth or reference deformation field. In addition, the extremely large data size of high-resolution XCT volumes makes computation difficult. In this work, we present a deep learning-based approach for estimating voxel-wise deformations between CAD and XCT volumes. Our method uses a dynamic patch-based processing strategy to handle high-resolution volumes. In addition to the Dice score, we introduce a Binary Difference Map (BDM) that quantifies voxel-wise mismatches between binarized CAD and XCT volumes to evaluate the accuracy of the registration. Our approach shows a 9.2% improvement in the Dice score and a 9.9% improvement in the voxel match rate compared to classic DVC methods, while reducing the interaction time from days to minutes. This work sets the foundation for deep learning-based DVC methods to generate compensation meshes that can then be used in closed-loop correlations during the AM production process. Such a system would be of great interest to industry, as it would make the manufacturing process more reliable and efficient, saving time and material. KW - Deep learning PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-646293 DO - https://doi.org/10.3233/FAIA251475 SN - 0922-6389 VL - 413 SP - 5368 EP - 5375 PB - IOS Press AN - OPUS4-64629 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Campos de Oliveira, Paula A1 - Markötter, Henning A1 - Zhang, Wen A1 - Eddah, Mustapha A1 - Widjaja, Martinus Putra A1 - Remacha, Clément A1 - Bruno, Giovanni T1 - Enhanced image segmentation of refractories using synchrotron X-ray computed tomography and machine learning techniques N2 - The microstructure of refractory materials is complex, featuring a variety of mineral phases, agglomerates, defects, and controlled porosity. The behavior of refractories at high temperatures adds another layer of complexity, as phase transitions and particle rearrangements can strongly affect their properties. To analyze such intricate microstructure, advanced imaging techniques such as Synchrotron X-ray Computed Tomography (SXCT) allow detailed 3D visualization and quantification of features up to 1 μm. However, the intricacy of these microstructures makes phase identification (known as image segmentation) in digital images a challenging process. X-ray images often contain noise and image artifacts, making the analysis more difficult. Therefore, this work describes image segmentation and artifact reduction methods to characterize refractories using X-ray imaging. We studied refractory ceramics used in the aerospace industry, primarily composed of fused silica. For image segmentation, the traditional approach of greyscale thresholding was compared with machine learning. Greyscale thresholding relies on predefined algorithms to assign phases based on intensity values. In contrast, machine learning extracts patterns from large datasets, enabling more adaptive and accurate segmentation. By combining high-resolution SXCT and machine learning analysis algorithms, we successfully segmented previously uncharacterized 3D microstructural key features of refractories, including agglomerates, grain boundaries, pore size distribution and interconnectivity. Compared to traditional methods, the machine learning-enhanced segmentation presented a more accurate quantification of porosity and defects. The integration of advanced imaging techniques with machine learning segmentation significantly improves the characterization of refractory materials, providing a more precise understanding of the relationship between microstructure and material performance, supporting the development of innovative industrial solutions. T2 - The 19th Biennial International Technical Conference on Refractories (UNITECR 2025) CY - Cancún, Mexiko DA - 27.10.2025 KW - Synchrotron X-ray Tomography KW - Machine learning KW - Image segmentation KW - Ceramics KW - Refractories PY - 2025 SP - 478 EP - 481 AN - OPUS4-64803 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bruno, Giovanni A1 - Lapenna, M. A1 - Faglioni, F. A1 - Fioresi, R. T1 - Temperature-based pruning for input features in Graph Neural Networks N2 - In the presentwork,we employ the concept of neural network temperature to prune unimportant features in input to aGraph Neural Network (GNN) architecture. In benchmark datasets for node and graph property prediction, each node comes equipped with a vector of numerous features. It is paramount to understand which information is actually necessary and which can be discarded, both for efficiency and explainability. The temperature is linked to the gradient activity due to the loss function minimization and leads to pruning of weight structures associated with small gradients. This study is done on different GNN architectures, one for node classification and another one for link prediction, and several benchmark datasets are employed.We compare the results with similar experiments previously conducted on the filters of Convolutional Neural Networks. Although still at the proof-of-concept stage, our temperature-based pruning technique stands as a promising alternative to state-of-the-art magnitude-based pruning techniques. KW - Temperature-based pruning KW - Graph Neural Networks PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-640886 VL - 140 SP - 1 EP - 20 PB - Springer AN - OPUS4-64088 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Gunnerek, R. A1 - Soundarapandiyan, G. A1 - Mishurova, T. A1 - Schröder, J. A1 - Bruno, Giovanni A1 - Boykin, J. A1 - Diaz, A. A1 - Klement, U. A1 - Hryha, E.ON T1 - Chemical mechanical polishing of powder bed fusion – laser beam processed 316 L stainless steel N2 - Additive manufacturing via powder bed fusion – laser beam (PBF-LB) enables the fabrication of complex geometries but suffers from inherently rough surfaces and surface tensile residual stresses, both of which can compromise structural integrity, particularly under fatigue loading. To address these limitations, this study investigates chemical mechanical polishing (CMP) as a surface finishing method for improving surface quality and modifying the residual stress state in PBF-LB 316 L stainless steel. The work uniquely examines how scan rotation (0◦ vs. 67◦ rotation) and contour parameters influence CMP effectiveness in material removal, surface smoothing, and subsurface stress redistribution. With a targeted material removal of 110 μm, CMP reduced surface roughness (Sa) by up to 94 %, achieving values as low as 0.7 μm. Microstructural analysis revealed no grain refinement but identified a thin, plastically deformed surface layer. This plastic deformation resulted in the transformation of tensile surface stresses (340 MPa) into beneficial compressive stresses (􀀀 400 MPa), as confirmed by synchrotron X-ray diffraction, which also showed a shift toward isotropic strain distribution. Further, these findings demonstrate that the initial scan strategy influences CMP performance and that CMP can enhance both surface integrity and mechanical reliability without altering the underlying microstructure. This study advances the understanding of how process induced microstructure and surface features affect CMP outcomes, enabling more informed design of post-processing strategies for improved surface integrity and mechanical performance in additively manufactured metals. KW - Residual stress KW - Additive manufacturing KW - Chemical mechanical polishing KW - As-built microstructure KW - Surface roughness KW - Surface finishing KW - Material removal PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-640522 DO - https://doi.org/10.1016/j.jmatprotec.2025.119055 SN - 0924-0136/ VL - 345 SP - 1 EP - 12 PB - Elsevier B.V. AN - OPUS4-64052 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CHAP A1 - Hejazi, Bardia A1 - Chand, Keerthana A1 - Fritsch, Tobias A1 - Bruno, Giovanni T1 - D-CNN and VQ-VAE Autoencoders for Compression and Denoising of Industrial X-Ray Computed Tomography Images N2 - The ever-growing volume of data in imaging sciences stemming from advancements in imaging technologies, necessitates efficient and reliable storage solutions for such large datasets. This study investigates the compression of industrial X-ray computed tomography (XCT) data using deep learning autoencoders and examines how these compression algorithms affect the quality of the recovered data. Two network architectures with different compression rates were used, a deep convolution neural network (D-CNN) and a vector quantized variational autoencoder (VQ-VAE). The XCT data used was from a sandstone sample with a complex internal pore network as a good test case for the importance of feature preservation. The quality of the decoded images obtained from the two different deep learning architectures with different compression rates were quantified and compared to the original input data. In addition, to improve image decoding quality metrics, we introduced a metric sensitive to edge preservation, which is crucial for three-dimensional data analysis. We showed that different architectures and compression rates are required depending on the specific characteristics needed to be preserved for later analysis. The findings presented here can aid scientists in determining the requirements and strategies needed for appropriate data storage and analysis. T2 - 28th European Conference on Artificial Intelligence – Including 14th Conference on Prestigious Applications of Intelligent Systems (PAIS 2025) CY - Bologna, Italy DA - 25.10.2025 KW - Data Compression KW - Deep Learning KW - X-ray Computed Tomography PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-644758 UR - https://ebooks.iospress.nl/doi/10.3233/FAIA251480 DO - https://doi.org/10.3233/FAIA251480 SN - 0922-6389 SP - 1 EP - 8 PB - IOS Press AN - OPUS4-64475 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -