TY - JOUR A1 - Lorenzoni, Renata A1 - Fritsch, Tobias A1 - Kruschwitz, Sabine A1 - Bruno, Giovanni A1 - Schmidt, Wolfram T1 - Mechanical analysis of cement-biochar composites using in-situ X-ray microtomography and digital volume correlation N2 - This study addresses biochar as a potential carbon-sequestering filler in cement and examines its effect on mechanical properties using X-ray computed tomography (XCT) and digital volume correlation (DVC). DVC was reliably used to measure global displacement and has proven to be an effective method for correcting displacement data obtained from mechanical tests conducted without traditional instrumentation, such as extensometer. This made it possible to measure strain and Young’s modulus accurately. The results demonstrate that while 5 vol% biochar replacement had minimal effect on mechanical properties, a 25 vol% biochar replacement caused a 35 % reduction in Young’s modulus and 40 % reduction in the ultimate compressive strength. Additionally, DVC detected strain concentrations and predicted material failure locations even when cracks could not be quantified using XCT alone. Moreover, the study reveals that biochar particles, due to their sharp geometry, increase internal shear strain during uniaxial compression, unlike round phases such as pores. KW - Cementitious materials KW - Biochar KW - Mechanical proprieties KW - In-situ X-ray microtomography KW - Digital volume correlation PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-638065 DO - https://doi.org/10.1016/j.conbuildmat.2025.142511 SN - 1879-0526 VL - 490 PB - Elsevier Ltd. AN - OPUS4-63806 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Chand, Keerthana A1 - Fritsch, Tobias A1 - Oster, Simon A1 - Ulbricht, Alexander A1 - Bruno, Giovanni T1 - Review on image registration methods for the quality control in additive manufacturing N2 - A critical challenge in Additive Manufacturing is to ensure the safety and dimensional accuracy of produced parts. Since quality control is made by means of different online and offline imaging techniques (e.g. Thermography, X-ray and Optical Computer Tomography), image registration plays an important role in addressing these challenges. This paper introduces the main motivation, challenges, and research gaps of image registration in Additive Manufacturing. Furthermore, it introduces the main transformations, registration methods, similarity matrices and accuracy measurement. The main aim of the paper is to present a comprehensive review on the available methods for image registration in Additive Manufacturing based on the measurement techniques. Various registration methods, including landmark-based, point cloud-based, image intensity-based, and shape-based techniques, are examined in their applications for quality control, defect detection, and distortion quantification. KW - Image processing KW - Image fusion KW - Computed tomography KW - Computer-aided design KW - Additive manufacturing PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-624495 DO - https://doi.org/10.1007/s40964-024-00932-2 SN - 2363-9520 SP - 1 EP - 27 PB - Springer Science and Business Media LLC AN - OPUS4-62449 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Schröder, Jakob A1 - Fritsch, Tobias A1 - Ferrari, Bruno A1 - Altmann, Mika León A1 - Bruno, Giovanni A1 - Toenjes, Anastasiya T1 - Laser powder bed fusion: Defect type influences critical porosity re-growth during reheating after hot isostatic pressing N2 - Despite the remarkable product design flexibility offered by additive manufacturing (AM) techniques, such as laser powder bed fusion, AM processes are susceptible to the formation of defects. In this context, the control of process parameters and the application of post-processing treatments, such as hot isostatic pressing (HIP), are of paramount importance to achieve the desired mechanical properties. The present study investigates the effectiveness of HIP as a function of process parameters in laser powder bed fused Ti-6V-4Al (PBF-LB/Ti64) using X-ray computed tomography. The process parameters are modified to obtain reference samples with low porosity, lack of fusion defects, or keyhole porosity. In all instances, subsurface keyhole porosity was observed in the as-built parts. Moreover, it was found that the efficacy of pore closure is dependent on the specific defect type. In the case of low porosity and keyhole pores, HIP resulted in effective closure. Conversely, larger lack of fusion defects were not closed due to their interconnectivity and the entrapment of argon gas. Subsequent heat treatments above the β-transus temperature allowed the investigation of the impact of defect type on porosity re-growth. For the first time, we reveal that lack of fusion defects are affected by considerable pore re-growth during post-HIP heat treatments of PBF-LB/Ti64. Such phenomenon is driven by the increasing internal pore pressure and local creep deformation at high temperatures. In contrast, re-growth is limited in samples with low porosity or keyhole pores. KW - Additive Manufacturing KW - Hot Isostatic Pressing KW - Titanium KW - Laser Powder Bed Fusion KW - X-ray Computed Tomography KW - Heat Treatment KW - Porosity PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-629289 DO - https://doi.org/10.1016/j.jmatprotec.2025.118839 VL - 340 SP - 1 EP - 9 PB - Elsevier B.V. AN - OPUS4-62928 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Chand, Keerthana A1 - Fritsch, Tobias A1 - Oster, Simon A1 - Ulbricht, Alexander A1 - Poka, Konstantin A1 - Bruno, Giovanni T1 - A Comparative Study of Rigid Three-Dimensional Image Registration Methods for Powder Bed Fusion with Laser Beam of Metals Using a Gold Standard Approach N2 - In Additive Manufacturing (AM), precise rigid three-dimensional (3D) image registration between X-ray Computed Tomography (XCT) scans and Computer-Aided Design (CAD) models is an important step for the quantification of distortions in produced parts. Given the absence of standardized benchmarks for image registration in AM, we introduce a gold standard for 3D image registration, using a reference base plate beneath the build structure. This gold standard is used to quantify the accuracy of rigid registration, with a proof of concept demonstrated in PBF-LB/M. In this study, we conduct a comparative analysis of various rigid 3D registration methods useful for quality assurance of PBF-LB/M parts including feature-based, intensity-based, and point cloud-based approaches. The performance of each registration method is evaluated using measures of alignment accuracy based on the gold standard and computational efficiency. Our results indicate significant differences in the efficacy of these methods, with point cloud based Coherent Point Drift (CPD) showing superior performance in both alignment and computational efficiency. The rigidly registered 3D volumes are used to estimate the deformation field of the printed parts relative to the nominal CAD design using Digital Volume Correlation (DVC). The quality of the estimated deformation field is assessed using the Dice score metric. This study provides insights into methods for enhancing the precision and reliability of AM process. KW - Digital volume correlation KW - 3D image registration KW - 3D image processing KW - X-ray computed tomography KW - Computer-aided design KW - Displacement field estimation PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-627043 DO - https://doi.org/10.1007/s10921-025-01174-0 SN - 1573-4862 VL - 44 IS - 30 SP - 1 EP - 20 PB - Springer AN - OPUS4-62704 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Schröder, Jakob A1 - Han, Ying A1 - Fritsch, Tobias A1 - Hejazi, Bardia A1 - Ulbricht, Alexander A1 - Skrotzki, Birgit A1 - Bruno, Giovanni T1 - X-ray computed tomography quantifies primary phases and reveals crack morphology in high-cycle fatigue of aluminum alloy EN AW-2618A N2 - Since the introduction of high-strength aluminum alloys understanding their fatigue behavior is of high interest for the structural integrity of engineering components, in this context, the alloy EN AW-2618A gains its high strength from both nanometer size precipitates and micrometer size primary phases. The latter phases are often identified as crack initiation sites. In this study, it uses a combination of synchrotron and laboratory-based X-ray computed tomography to image and quantify such primary phases and the fatigue cracks appearing in interrupted tests. Based on the gray-level differences in the synchrotron X-ray computed tomography scans, this study is able to distinguish low- and high-absorbing particles. The dominant (volume fraction >99%) high absorbing primary phase can be quantified in good agreement with results of Thermo-Calc calculations. To image the fatigue crack, laboratory X-ray computed tomography scans are performed at different tensile loads to open the crack. The results show that with an appropriate crack opening tensile load, the fatigue crack morphology can be reliably revealed. Based on these results, the influence of the primary phases on the fatigue crack initiation and propagation are discussed. KW - X-ray computed tomography KW - Fatigue KW - Aluminum KW - Primary phases PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-632724 DO - https://doi.org/10.1002/adem.202500235 SN - 1527-2648 SP - 1 EP - 9 PB - Wiley-VCH CY - Weinheim AN - OPUS4-63272 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 - JOUR A1 - Bruson, Danilo A1 - Serrano-Munoz, Itziar A1 - Fritsch, Tobias A1 - Markötter, Henning A1 - Galati, Manuela T1 - Revealing the (positive) role of porosity within polymeric additively manufactured lattices via X-ray computed tomography N2 - The mechanical properties of lattice geometries are known to be significantly influenced by a variety of manufacturing defects. This study investigates the influence of porosity on the mechanical behaviour of strut-based body-centred cubic (BCC) lattice structures produced with powder bed fusion with laser beam PBF-LB/P using PA2200 nylon powder. The study combines advanced techniques, including in-situ laboratory X-ray computed tomography (XCT), synchrotron XCT to visualise pores and roughness in high resolution at a single-cell level and image-based finite element analysis (FEA). The findings show that failure in thin-walled AM lattices is governed by the combined effects of porosity morphology, location, surface roughness, and cross-section reduction. The presence of internal porosity is found to attenuate both the amplitude of elastic modulus fluctuations and the severity of stress concentrations induced by surface irregularities. KW - Nylon Pa2200 KW - Laser powder bed fusion (PBF-LB) KW - Processing defects KW - Synchrotron X-ray computed tomography (XCT) KW - Digital volume correlation (DVC) KW - Image-based finite element simulations PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-633245 DO - https://doi.org/10.1016/j.ijsolstr.2025.113488 SN - 0020-7683 VL - 320 SP - 1 EP - 16 PB - Elsevier B.V. AN - OPUS4-63324 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 - TY - CONF A1 - Hejazi, Bardia A1 - Fritsch, Tobias A1 - Benz, Christopher A1 - Radtke, Lars A1 - Sander, Manuela A1 - Bruno, Giovanni T1 - In-situ very high cycle fatigue experiments of additively manufactured Ti-6Al-4V using X-ray computed tomography N2 - X-ray computed tomography (XCT) is an invaluable method for evaluating the properties and performance of components both during service and after failure in a non-destructive manner. XCT is particularly useful for the investigation of additively manufactured (AM) components, which often have production defects that are inherent to the manufacturing process, such as lack of fusion defects. Understanding the mechanisms of fatigue crack growth throughout the life cycle of such components is crucial and so to address this need, we designed and performed experiments to investigate the fatigue life and fatigue crack growth behavior of Ti-6Al-4V components under very high cycle fatigue (VHCF) testing. The titanium samples were additively manufactured with intentional internal defects to control crack initiation location. XCT of the component was carried out to identify crack initiation sites and characterize the dynamics of crack growth. The findings from this work will benefit industries that rely on the AM of titanium alloys, aiding in the improvement of component design and manufacturing processes. T2 - Alloys for additive manufacturing 2025 (AAMS 2025) CY - Neuchâtel, Switzerland DA - 02.09.2025 KW - X-ray computed tomography KW - Deep learning KW - Titanium alloy KW - Very high-cycle fatigue PY - 2025 DO - https://doi.org/10.5281/zenodo.15261296 AN - OPUS4-64096 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -