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 JF - Materials Science & Engineering A 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, R. A1 - Faglioni, F. A1 - Bruno, Giovanni T1 - Synthetic Data Generation for Automatic Segmentation of X-ray Computed Tomography Reconstructions of Complex Microstructures JF - Journal of Imaging N2 - The greatest challenge when using deep convolutional neural networks (DCNNs) for automatic segmentation of microstructural X-ray computed tomography (XCT) data is the acquisition of sufficient and relevant data to train the working network. Traditionally, these have been attained by manually annotating a few slices for 2D DCNNs. However, complex multiphase microstructures would presumably be better segmented with 3D networks. However, manual segmentation labeling for 3D problems is prohibitive. In this work, we introduce a method for generating synthetic XCT data for a challenging six-phase Al–Si alloy composite reinforced with ceramic fibers and particles. Moreover, we propose certain data augmentations (brightness, contrast, noise, and blur), a special in-house designed deep convolutional neural network (Triple UNet), and a multi-view forwarding strategy to promote generalized learning from synthetic data and therefore achieve successful segmentations. We obtain an overall Dice score of 0.77. Lastly, we prove the detrimental effects of artifacts in the XCT data on achieving accurate segmentations when synthetic data are employed for training the DCNNs. The methods presented in this work are applicable to other materials and imaging techniques as well. Successful segmentation coupled with neural networks trained with synthetic data will accelerate scientific output. KW - Automatic segmentation KW - 3D deep convolutional neural network (3D DCNN) KW - Dice score KW - Metal matrix composite (MMC) KW - Modified U-Net architectures KW - Multi-phase materials PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-571243 DO - https://doi.org/10.3390/jimaging9020022 VL - 9 IS - 2 SP - 1 EP - 23 PB - MDPI AN - OPUS4-57124 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Mehta, B. A1 - Mishurova, Tatiana A1 - Evsevleev, Sergei A1 - Markötter, Henning A1 - Bruno, Giovanni A1 - Hryha, E. A1 - Nyborg, L. A1 - Virtanen, E. T1 - Microstructure, mechanical properties and fracture mechanisms in a 7017 aluminium alloy tailored for powder bed fusion – laser beam JF - Materials and Design N2 - This study addressed a 7017 Al-alloy tailored for powder bed fusion – laser beam (PBF-LB) process. The alloy was prepared by mixing 3 wt% Zr and 0.5 wt% TiC powder to standard pre-alloyed 7017 grade aluminium powder. This made printing of the alloys possible avoiding solidification cracking in the bulk and achieving high relative density (99.8 %). Such advanced alloys have significantly higher Young’s modulus (>80 GPa) than conventional Al-alloys (70–75 GPa), thus making them attractive for applications requiring high stiffness. The resulting microstructure in as-printed condition was rich in particles originating from admixed powders and primary precipitates/inclusions originating from the PBF-LB process. After performing a T6-like heat treatment designed for the PBF-LB process, the microstructure changed: Zr-nanoparticles and Fe- or Mg/Zn- containing precipitates formed thus providing 75 % increase in yield strength (from 254 MPa to 444 MPa) at the cost of decreasing ductility (∼20 % to ∼9 %). In-situ tensile testing combined with SXCT, and ex-situ tensile testing combined with fracture analysis confirmed that the fracture initiation in both conditions is highly dependent on defects originated during printing. However, cracks are deflected from decohesion around Zr-containing inclusions/precipitates embedded in the Al-matrix. This deflection is seen to improve the ductility of the material. KW - Additive manufacturing KW - Powder bed fusion Laser beam KW - X-ray computed tomography KW - Strengthening mechanisms KW - Crack propagation KW - Zirconium PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-568243 DO - https://doi.org/10.1016/j.matdes.2023.111602 SN - 0264-1275 VL - 226 SP - 1 EP - 14 PB - Elsevier Science CY - Amsterdam [u.a.] AN - OPUS4-56824 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Mishurova, Tatiana A1 - Evsevleev, Sergei A1 - Piault, Pierre A1 - King, Andrew A1 - Henry, Laura A1 - Bruno, Giovanni T1 - Understanding the hot isostatic pressing efectiveness of laser powder bed fusion Ti‑6Al‑4V by in‑situ X‑ray imaging and difraction experiments JF - Scientific reports N2 - In the present study, in-situ observation of Hot Isostatic Pressure (HIP) procedure of laser powder bed fusion manufactured Ti-6Al-4V parts was performed to quantitatively estimate the densifcation rate of the material and the infuence of the defect initial size and shape on such rate. The observations were performed in-situ using the Ultrafast Tomography Paris-Edinburgh Cell and the combination of fast phase-contrast synchrotron X-ray tomography and energy dispersive difraction. With this strategy, we could quantify how the efectiveness of HIP depends on the characteristics of a defect. Smaller defects showed a higher densifcation rate, while the defect shape did not have signifcant efect on such rate. KW - Additive manufacturing KW - Laser powder bed fusion KW - X-ray computed tomography KW - Hot isostatic pressing PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-587702 DO - https://doi.org/10.1038/s41598-023-45258-1 SN - 2045-2322 VL - 13 IS - 1 SP - 1 EP - 11 AN - OPUS4-58770 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) JF - Tomography of Materials and Structures 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 - 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 JF - Advanced Engineering Materials 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 - 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) JF - Computational Materials Science 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 - 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) JF - Advanced Engineering Materials 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 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-572417 DO - https://doi.org/10.1002/adem.202201034 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 - Neuschaefer-Rube, U. A1 - Illemann, J. A1 - Sturm, M. A1 - Bircher, B. A1 - Meli, F. A1 - Bellon, Carsten A1 - Evsevleev, Sergei T1 - Validation of a fast and traceable radiographic scale calibration of dimensional computed tomography JF - Measurement Science and Technology N2 - A fast and highly precise method of determining the geometrical scale factor of computed tomography (CT) measurements has been validated successfully by Bundesanstalt für Materialforschung und -prüfung (BAM), the Federal Institute of Metrology (METAS) and Physikalisch-Technische Bundesanstalt (PTB) within the scope of AdvanCT (Advanced Computed Tomography for dimensional and surface measurements in industry), a project funded in the European Metrology Programme for Innovation and Research (EMPIR). The method has been developed by PTB and requires only two radiographic images of a calibrated thin 2D standard (hole grid standard) from two opposite directions. The mean grid distance is determined from both radiographs. From this and with the help of the calibration result, the radiographic scale and therefore the voxel size is determined. The procedure takes only a few minutes and avoids a time-consuming CT scan. To validate the method, the voxel sizes determined via this method were compared with voxel sizes determined from CT scans of calibrated objects. Relative deviations between the voxel sizes in the range of 10−5 were achieved with minimal effort using cone-beam CT systems at moderate magnifications. KW - Dimensional metrology KW - Voxel size KW - Industrial CT KW - Geometrical magnification PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-553271 DO - https://doi.org/10.1088/1361-6501/ac74a3 SN - 0957-0233 VL - 33 IS - 9 SP - 1 EP - 9 PB - IOP Publishing CY - Bristol, UK AN - OPUS4-55327 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Mishurova, Tatiana A1 - Evsevleev, Sergei A1 - Artzt, K. A1 - Haubrich, J. A1 - Sevostianov, I. A1 - Requena, G. A1 - Bruno, Giovanni T1 - Micromechanical behavior of annealed Ti-6Al-4V produced by Laser Powder Bed Fusion JF - European Journal of Materials N2 - The micromechanical behavior of an annealed Ti-6Al-4V material produced by Laser Powder Bed Fusion was characterized by means of in-situ synchrotron X-ray diffraction during a tensile test. The lattice strain evolution was obtained parallel and transversal to the loading direction. The elastic constants were determined and compared with the conventionally manufactured alloy. In the plastic regime, a lower plastic anisotropy exhibited by the lattice planes was observed along the load axis (parallel to the building direction) than in the transverse direction. Also, the load transfer from α to β phase was observed, increasing global ductility of the material. The material seems to accumulate a significant amount of intergranular strain in the transverse direction. KW - Additive manufacturing KW - Ti-6Al-4V KW - Anisotropy KW - Intergranular strain KW - Synchrotron X-ray diffraction PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-547406 DO - https://doi.org/10.1080/26889277.2022.2063763 VL - 2 IS - 1 SP - 186 EP - 201 PB - Taylor & Francis AN - OPUS4-54740 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -