TY - JOUR A1 - Vogel, F. A1 - Ngai, S. A1 - Zhou, X. Y. A1 - Zaiser, E. A1 - Manzoni, Anna Maria A1 - Wu, Y. A1 - Zheng, W. W. A1 - Zhang, P. A1 - Thompson, G. B. T1 - Tracking maze-like hierarchical phase separation behavior in a Fe-Si-V alloy N2 - Optimizing the properties of next-generation high-temperature and corrosion-resistant alloys is rooted in balancing structure-property relationships and phase chemistry. Here, we implement a complementary approach based on transmission electron microscopy (TEM) and atom probe tomography (APT) to ascertain aspects of hierarchical phase separation behavior, by understanding the microstructural evolution and the three-dimensional (3D) nanochemistry of a single crystal Fe79.5Si15.5V5.0 (at%) alloy. A maze-like hierarchical microstructure forms, in which a complex network of metastable disordered α plates (A2 phase) emerges within ordered α1 precipitates (D03 phase). The supersaturation in α1 (D03) precipitates with Fe and V drives the formation of α (A2) plates. The morphology of α (A2) plates is discussed concerning crystal structure, lattice misfit, and elastic strain. Phase compositions and a ternary phase diagram aid the thermodynamic assessment of the hierarchical phase separation mechanism via the Gibbs energy of mixing. A perspective on the stabilization of hierarchical microstructures beyond Fe79.5Si15.5V5.0 is elaborated by comparing hierarchical alloys. We find that the ratio of elastic anisotropy (Zener ratio) serves as a predictor of the hierarchical particles’ morphology. We suggest that the strengthening effect of hierarchical microstructures can be harnessed by improving the temporal and thermal stability of hierarchical particles. This can be achieved through phase-targeted alloying aiming at the hierarchical particles phase by considering the constituents partitioning behavior. Beyond Fe79.5Si15.5V5.0, our results demonstrate a potential pathway for improving the properties of high-temperature structural materials. KW - Atom probe tomography KW - Transmission electron microscopy KW - Hierarchical microstructure KW - Phase separation PY - 2023 DO - https://doi.org/10.1016/j.jallcom.2023.172157 SN - 0925-8388 VL - 968 SP - 1 EP - 17 PB - Elsevier B.V. AN - OPUS4-58343 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Niederleithinger, Ernst A1 - Gardner, S. A1 - Kind, Thomas A1 - Kaiser, R. A1 - Grunwald, Marcel A1 - Yang, G. A1 - Redmer, Bernhard A1 - Waske, Anja A1 - Mielentz, Frank A1 - Effner, Ute A1 - Köpp, Christian A1 - Clarkson, A. A1 - Thompson, F. A1 - Ryan, M. A1 - Mahon, D. T1 - Muon Tomography of the Interior of a Reinforced Concrete Block: First Experimental Proof of Concept N2 - Quality assurance and condition assessment of concrete structures is an important topic world-wide due to the aging infrastructure and increasing traffic demands. Common topics include, but are not limited to, localisation of rebar or tendon ducts, geometrical irregularities, cracks, voids, honeycombing or other flaws. Non-destructive techniques such as ultrasound or radar have found regular, successful practical application but sometimes suffer from limited resolution and accuracy, imaging artefacts or restrictions in detecting certain features. Until the 1980s X-ray transmission was used in case of special demands and showed a much better resolution than other NDT techniques. However, due to safety concerns and cost issues, this method is almost never used anymore. Muon tomography has received much attention recently. Novel detectors for cosmic muons and tomographic imaging algorithms have opened up new fields of application, such as the investigation of freight containers. Muon imaging also has the potential to fill some of the gaps currently existing in concrete NDT. As a first step towards practical use and as a proof of concept we used an existing system to image the interior of a reference reinforced 600 kg concrete block. Even with a yet not optimized setup for this kind of investigation, the muon imaging results are at least of similar quality compared to ultrasonic and radar imaging, potentially even better. The data acquisition takes more time and signals contain more noise, but the images allowed to detect the same important features that are visible in conventional high energy X-ray tomography. In our experiment, we have shown that muon imaging has potential for concrete inspection. The next steps include the development of mobile detectors and optimising acquisition and imaging parameters. KW - Concrete KW - Muon KW - Radar KW - Ultrasound KW - X-ray PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-529974 DO - https://doi.org/10.1007/s10921-021-00797-3 VL - 40 IS - 3 SP - 1 EP - 14 PB - Springer Nature AN - OPUS4-52997 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Zhou, X. A1 - Wei, Y. A1 - Kühbach, M. A1 - Zhao, H. A1 - Vogel, F. A1 - Darvishi Kamachali, Reza A1 - Thompson, G. B. A1 - Raabe, D. A1 - Gault, B. T1 - Revealing in-plane grain boundary composition features through machine learning from atom probe tomography data N2 - Grain boundaries (GBs) are planar lattice defects that govern the properties of many types of polycrystalline materials. Hence, their structures have been investigated in great detail. However, much less is known about their chemical features, owing to the experimental difficulties to probe these features at the atomic length scale inside bulk material specimens. Atom probe tomography (APT) is a tool capable of accomplishing this task, with an ability to quantify chemical characteristics at near-atomic scale. Using APT data sets, we present here a machine-learning-based approach for the automated quantification of chemical features of GBs. We trained a convolutional neural network (CNN) using twenty thousand synthesized images of grain interiors, GBs, or triple junctions. Such a trained CNN automatically detects the locations of GBs from APT data. Those GBs are then subjected to compositional mapping and analysis, including revealing their in-plane chemical decoration patterns. We applied this approach to experimentally obtained APT data sets pertaining to three case studies, namely, Ni-P, Pt-Au, and Al-Zn-Mg-Cu alloys. In the first case, we extracted GB specific segregation features as a function of misorientation and coincidence site lattice character. Secondly, we revealed interfacial excesses and in-plane chemical features that could not have been found by standard compositional analyses. Lastly, we tracked the temporal evolution of chemical decoration from early-stage solute GB segregation in the dilute limit to interfacial phase separation, characterized by the evolution of complex composition patterns. This machine-learning-based approach provides quantitative, unbiased, and automated access to GB chemical analyses, serving as an enabling tool for new discoveries related to interface thermodynamics, kinetics, and the associated chemistry-structure-property relations. KW - Machine learning KW - Digitalization KW - Alloy microstructure PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-543049 DO - https://doi.org/10.1016/j.actamat.2022.117633 SN - 1359-6454 VL - 226 SP - 1 EP - 15 PB - Elsevier CY - Amsterdam AN - OPUS4-54304 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -