TY - JOUR A1 - Tammas-Williams, S. A1 - Zhao, H. A1 - Léonard, Fabien A1 - Derguti, F. A1 - Todd, I. A1 - Prangnell, P.B. T1 - XCT analysis of the influence of melt strategies on defect population in Ti-6Al-4V components manufactured by Selective Electron Beam Melting JF - XCT analysis of the influence of melt strategies on defect population in Ti-6Al-4V components manufactured by Selective Electron Beam Melting N2 - Selective Electron Beam Melting (SEBM) is a promising powder bed Additive Manufacturing technique for near-net-shape manufacture of high-value titanium components. However without post-manufacture HIPing the fatigue life of SEBM parts is currently dominated by the presence of porosity. In this study, the size, volume fraction, and spatial distribution of the pores in model samples have been characterised in 3D, using X-ray Computed Tomography, and correlated to the process variables. The average volume fraction of the pores (b0.2%) was measured to be lower than that usually observed in competing processes, such as selective laser melting, but a strong relationship was found with the differentbeamstrategies used to contour ,and infill by hatching, a part section. The majority of pores were found to be small spherical gas pores, concentrated in the infill hatched region; this was attributed to the lower energy density and less focused beam used in the infill strategy allowing less opportunity for gas bubbles to escape the melt pool. Overall, increasing the energy density or focus of the beam was found to correlate strongly to a reduction in the level of gas porosity. Rarer irregular shaped pores were mostly located in the contour region and have been attributed to a lack of fusion between powder particles. KW - Titanium KW - Additive Manufacture KW - Selective Electron Beam Melting KW - Pores KW - X-ray computed tomography PY - 2015 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-416577 UR - http://www.sciencedirect.com/science/article/pii/S104458031500039X?via%3Dihub DO - https://doi.org/10.1016/j.matchar.2015.02.008 VL - 102 SP - 47 EP - 61 CY - Materials Characterization AN - OPUS4-41657 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 JF - Acta materialia 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 -