Strain and Microstructural Evolution in Architected Lattices: A Comparison of Electron Beam and Laser Powder Bed Fusion
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Materials in service and during processing are often subjected to plastic deformation. For multi-phase metallic alloys, simple geometric models and atomistic simulations suggest that two distinctive regimes in these materials’ evolution during deformation exist. At low strains, evolutions are often dominated by the kinetic roughening of interfaces, which results from the superdiffusive transport of matter in sheared crystals. Furthermore, at high strains and temperatures where thermal diffusion is sluggish, on the other hand, shearing- induced forced atomic mixing dominates these evolutions, resulting in significantly enhanced solubility. Distinguishing these two regimes is shown to provide a convenient framework for rationalizing and analyzing recent experiments and simulations on wear of layered structures in the low strain-regime and nonequilibrium phase co-existence and self-organization in highly immiscible or reactive alloy systems in the high-strain regime.
In this work, we propose instance segmentation as a useful tool for image analysis in materials science. Instance segmentation is an advanced technique in computer vision which generates individual segmentation masks for every object of interest that is recognized in an image. Using an out-of-the-box implementation of Mask R-CNN, instance segmentation is applied to images of metal powder particles produced through gas atomization. Leveraging transfer learning allows for the analysis to be conducted with a very small training set of labeled images. As well as providing another method for measuring the particle size distribution, we demonstrate the first direct measurements of the satellite content in powder samples. After analyzing the results for the labeled data dataset, the trained model was used to generate measurements for a much larger set of unlabeled images. The resulting particle size measurements showed reasonable agreement with laser scattering measurements. The satellite measurements were self-consistent and showed good agreement with the expected trends for different samples. Finally, we present a small case study showing how instance segmentation can be used to measure spheroidite content in the UltraHigh Carbon Steel DataBase, demonstrating the flexibility of the technique.
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Infiltration of mixed ionic/electronic conducting (MIEC) nanocatalysts into the anodes of solid oxide fuel cells (SOFCs) allow the hydrogen oxidation reaction (HOR) to occur at double-phase boundaries (DPBs), rather than being confined only to the triple phase boundaries (TPBs). In this research, we use a combination of cross-sectional scanning electron microscopy (SEM), three-dimensional (3-D) reconstruction, and electrochemical methods to characterize both baseline and gadolinium-doped ceria (GDC)-infiltrated symmetric cells, with the goal of establishing a comparison between the density of electrochemically active sites in uninfiltrated and GDC-infiltrated cells. Here, electrochemical impedance spectroscopy (EIS) and distribution of relaxation times (DRT) analysis showed GDC infiltration decreased the overall polarization of the anode by a factor of 8.78 X, and decreased the resistance associated with the reactions at the TPBs/DPBs by a factor of over 21 X. By quantifying expected GDC deposition morphology in the Ni/YSZ anode it is estimated that reactions occur in the baseline cell up to 84 nm from the active TPB lines.
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