On Open and Strong-Scaling Tools for Atom Probe Crystallography: High-Throughput Methods for Indexing Crystal Structure and Orientation

Volumetric crystal structure indexing and orientation mapping are key data processing steps for virtually any quantitative study of spatial correlations between the local chemistry and the microstructure of a material. For electron and X-ray diffraction methods it is possible to develop indexing tools which compare measured and analytically computed patterns to decode the structure and relative orientation within local regions of interest. Consequently, a number of numerically efficient and automated software tools exist to solve the above characterisation tasks. For atom probe tomography (APT) experiments, however, the strategy of making comparisons between measured and analytically computed patterns is less robust because many APT datasets may contain substantial noise. Given that general enough predictive models for such noise remain elusive, crystallography tools for APT face several limitations: Their robustness to noise, and therefore, their capability to identify and distinguish different crystal structures and orientation is limited. In addition, the tools are sequential and demand substantial manual interaction. In combination, this makes robust uncertainty quantifying with automated high-throughput studies of the latent crystallographic information a difficult task with APT data. To improve the situation, we review the existent methods and discuss how they link to those in the diffraction communities. With this we modify some of the APT methods to yield more robust descriptors of the atomic arrangement. We report how this enables the development of an open-source software tool for strong-scaling and automated identifying of crystal structure and mapping crystal orientation in nanocrystalline APT datasets with multiple phases.Comment: 36 pages, 19 figures, preprin

Viscoelastoplastic Deformation and Damage Response of Titanium Alloy, Ti-6Al-4V, at Elevated Temperatures

Time-dependent deformation and damage behavior can significantly affect the life of aerospace propulsion components. Consequently, one needs an accurate constitutive model that can represent both reversible and irreversible behavior under multiaxial loading conditions. This paper details the characterization and utilization of a multi-mechanism constitutive model of the GVIPS class (Generalized Viscoplastic with Potential Structure) that has been extended to describe the viscoelastoplastic deformation and damage of the titanium alloy Ti-6Al-4V. Associated material constants were characterized at five elevated temperatures where viscoelastoplastic behavior was observed, and at three elevated temperatures where damage (of both the stiffness reduction and strength reduction type) was incurred. Experimental data from a wide variety of uniaxial load cases were used to correlate and validate the proposed GVIPS model. Presented are the optimized material parameters, and the viscoelastoplastic deformation and damage responses at the various temperatures

A Framework for Modeling Discrete Deformation Twinning in Hexagonal Crystals

Modeling the plastic deformation of metals has historically been achieved by considering only crystallographic slip, the dominant mode of plastic deformation. While sufficient for materials that deform primarily by means of slip, many metals may exhibit other modes of plastic deformation, and thus may not be accurately modeled by slip alone. Deformation twinning is another mode of plastic deformation, characterized by a rapid, large uniform shear of a discrete region of material, coupled with a reorientation of the crystal lattice within said region. While witnessed in metals of various crystal symmetries, metals comprised of hexagonal crystals are especially prone to exhibit twinning, as they may require twinning to accommodate generalized plasticity. Due in large part to limitations in computational capabilities, models have often ignored deformation twinning. Existing models rely on the homogenization of the responses due to both slip and twinning via a modified Taylor hypothesis, and thus fail to predict accurate local states. Additionally, these models consider twin systems as modified slip systems, obscuring the discrete nature of deformation twinning, as well as the disparity in relative speeds at which each deformation mode propagates. Advances in computational capabilities and model frameworks have allowed for the possibility to study this deformation mode in more detail. A parallelized finite element framework is uniquely suited to approach this problem, as a proven platform for modeling high fidelity, finely discretized representations of polycrystalline aggregates. A framework is presented, in which grains within a microstructure are pre-discretized - based on their crystallographic orientation - into discrete regions that may deform by deformation twinning. A boundary value problem is solved, in which the displacement of the nodes within a twin region are rapidly mapped to their twinned location, the region's crystal lattice is reoriented via three separate schemes, and the remainder of the body deforms by means of crystallographic slip to accommodate this deformation. In this way, the extended framework retains the characteristic differences between crystallographic slip and deformation twinning in a way that existing models do not. Work is calculated due to the changes in local environments due to twinning. Changes in local stress states are discussed in light of global and local work measures and various parameters, including twin size and reorientation schemes

Graph Neural Network Modeling of Grain-scale Anisotropic Elastic Behavior using Simulated and Measured Microscale Data

Here we assess the applicability of graph neural networks (GNNs) for predicting the grain-scale elastic response of polycrystalline metallic alloys. Using GNN surrogate models, grain-averaged stresses during uniaxial elastic tension in Low Solvus High Refractory (LSHR) Ni Superalloy and Ti 7wt%Al (Ti-7Al), as example face centered cubic and hexagonal closed packed alloys, are predicted. A transfer learning approach is taken in which GNN surrogate models are trained using crystal elasticity finite element method simulations and then the trained surrogate models are used to predict the mechanical response of microstructures measured using high-energy X-ray diffraction microscopy. The performance of using various microstructural and micromechanical descriptors for input nodal features to the GNNs is explored. The effects of elastic anisotropy on GNN model performance and outlooks for extension of the framework are discussed