Explore the vast range of titles available.

Coherent diffraction imaging (CDI) is an advanced non-destructive 3D X-ray imaging technique for measuring a sample’s electron density. The main challenge of CDI is loss of phase information in diffraction intensity measurements, resulting in lengthy iterative reconstruction processes that can return non-unique solutions, which pose challenges for experiments attempting to track dynamic sample evolution through multiple states. As the increased brightness of fourth-generation light sources enables faster sample measurements and drives operando experiments with Bragg CDI, there is a growing need for faster reconstruction techniques that can keep pace. We have developed an adaptive generative autoencoder approach for uniquely tracking a sample’s electron density as it dynamically evolves. Our approach adaptively tunes the low-dimensional latent embedding of a generative autoencoder, enabling a computationally efficient manner to account for time-varying shifting distributions in real-time. Analytic proof of convergence is provided as well as numerical demonstration of sample tracking with noisy measurements.

The nucleation and propagation of dislocations is an ubiquitous process that accompanies the plastic deformation of materials. Consequently, following the first visualization of dislocations over 50 years ago with the advent of the first transmission electron microscopes, significant effort has been invested in tailoring material response through defect engineering and control. To accomplish this more effectively, the ability to identify and characterize defect structure and strain following external stimulus is vital. Here, using X-ray Bragg coherent diffraction imaging, we describe the first direct 3D X-ray imaging of the strain field surrounding a line defect within a grain of free-standing nanocrystalline material following tensile loading. By integrating the observed 3D structure into an atomistic model, we show that the measured strain field corresponds to a screw dislocation. Identifying atomic defects during deformation is crucial to understand material response but remains challenging in three dimensions. Here, the authors couple X-ray Bragg coherent diffraction imaging and atomistic simulations to correlate a strain field to a screw dislocation in a single copper grain.

This study utilized high energy synchrotron x-ray diffraction to probe microstructural evolution during uniaxial deformation of conventionally manufactured and additively manufactured (AM) 304L stainless steel with and without internal hydrogen. The objective of this effort is to highlight the effect of hydrogen on deformation-induced martensite phase transformations in austenitic stainless steels. Solute hydrogen depresses the required applied strain to initiate austenite transformation to ε-martensite and α’-martensite in both forged and AM stainless steel. Similarly, the total fraction of transformation product is larger when the microstructure is saturated with hydrogen. Deformation induced phase transformations also lead to a variation in strain partitioning behavior, which is linked to the chemical composition and stacking fault energy of the starting and hydrogen-charged materials.

In this work, we investigated the effects of alloying elements on plastic deformation and microstructure evolution in polycrystalline copper (Cu) and Cu alloyed with 1 wt. % lead (Cu-1 % Pb). These materials were selected due to the size mismatch between Cu and Pb, with the latter forming precipitates at grain boundaries. Multi-modal characterization techniques, including neutron diffraction, electron backscatter diffraction (EBSD), and transmission electron microscopy (TEM), along with finite element simulations were employed to study the deformation behavior across multiple length scales. While both Cu and Cu-1 % Pb exhibited similar macroscale response and final deformation textures, both dislocation line profile analysis and TEM revealed increased dislocation density in deformed Cu-1 % Pb specimens. The presence of lead precipitates also significantly affected local plastic deformation during compression, with their influence diminishing with increasing strain. These results demonstrate the complex relationships between alloying elements, plastic deformation, microstructural evolution, and material behavior under load. The insights gained from this multi-scale and multi-technique approach contribute to the fundamental understanding of microstructural evolution in immiscible alloys and are valuable for tailoring the properties of structural materials for specific engineering applications.

Non-destructive evaluation of plastically deformed metals, particularly diffraction line profile analysis (DLPA), is valuable both to estimate dislocation densities and arrangements and to validate microstructure-aware constitutive models. To date, the interpretation of whole line diffraction profiles relies on the use of semi-analytical models such as the extended convolutional multiple whole profile (eCMWP) method. This study introduces and validates two data-driven DLPA models to extract dislocation densities from experimentally gathered whole line diffraction profiles. Using two distinct virtual diffraction models accounting for both strain and instrument induced broadening, a database of virtual diffraction whole line profiles of Ta single crystals is generated using discrete dislocation dynamics. The databases are mined to create Gaussian process regression-based surrogate models, allowing dislocation densities to be extracted from experimental profiles. The method is validated against 11 experimentally gathered whole line diffraction profiles from plastically deformed Ta polycrystals. The newly proposed model predicts dislocation densities consistent with estimates from eCMWP. Advantageously, this data driven LPA model can distinguish broadening originating from the instrument and from the dislocation content even at low dislocation densities. Finally, the data-driven model is used to explore the effect of heterogeneous dislocation densities in microstructures containing grains, which may lead to more accurate data-driven predictions of dislocation density in plastically deformed polycrystals.

The evolution of stress during damage initiation and accumulation in a two-phase alloy consisting of a ductile copper (Cu) matrix with a randomly dispersed brittle tungsten (W) phase was studied using multiple non-destructive experimental probes. Neutron diffraction measurements were performed to examine the macroscopic strain partitioning between the two phases during a uniaxial tension test. The same material was then examined with high-energy x-ray diffraction microscopy (HEDM) and micro-computed tomography ( μ -CT ) measurements to monitor micromechanical field evolution. The neutron diffraction data indicated a redistribution of load between the Cu and W phases as deformation proceeds. Using HEDM to monitor individual grain micromechanical behavior, an increase followed by decrease in hydrostatic stress and a similar stress triaxiality behavior were found to occur in a subset of W grains. These same W grains were found to be in close proximity to voids observed via tomography at later stages of deformation. From these observations, we conclude that high stress triaxiality development in the W particles leads to decohesion of the interface between the Cu and W phases. The debonded regions eventually grew and coalesced with neighboring voids leading to material failure.

We developed a copper/tungsten (Cu/W) composite for mesoscale Materials Science applications using the novel High-Energy Diffraction Microscopy (HEDM) technique. Argon-atomized copper powder was selected as the starting raw powder and screened to remove the extremely large particle fraction. Tungsten particles were collected by milling and screening the −325 mesh tungsten powder between 500 and 635 mesh sieves. Hot pressing of screened Cu powder was performed at 900 °C in Ar/4 %H 2 atmosphere. XRD and ICP results show that the hot-pressed Cu sample consists of about 5 vol% Cu 2 O, which is caused by the presence of oxygen on the surface of the starting Cu powder. Hot pressing the copper powder in a pure hydrogen atmosphere was successful in removing most of the surface oxygen. This process was also implemented for hot pressing the Cu/W composite. The density of the Cu/W composites hot pressed at 950 °C in pure hydrogen was about 94 % of the theoretical density (TD). The hot-pressed Cu/W composites were further hot isostatic pressed at 1050 °C in argon atmosphere, which results in 99.6 % of the TD with the designed Cu grain size and W particle distribution. Tensile specimens with D-notch were machined using the wire EDM method. The processing and consolidation of these materials will be discussed in detail. The HEDM images are also showed and discussed.

We have developed a machine learning-based crystal plasticity surrogate model (CP-SM) that can directly learn highly nonlinear material behavior during plastic deformation. CP-SM provides fast inference of spatially resolved three-dimensional (3D) microstructure and micromechanical fields and their evolution during plastic deformation, predicting the 22-dimensional material characteristics including a four-dimensional (4D) quaternion-based representation of crystal orientation, six-dimensional (6D) elastic and plastic strain tensors, and 6D stress at each location in the 3D structure. The predictions from CP-SM are orders of magnitude faster than and show good agreement with the deformation fields predicted by performing direct numerical simulations using spectral solvers. The fidelity of the CP-SM is further tested by assessing how well physics-based constraints are satisfied by the predicted 3D fields. We demonstrate our results on numerical simulations of uniaxially loaded copper.

We have developed a machine learning-based interatomic potential (MLIP) for the quaternary MoNbTaW (R4) and quinary MoNbTaTiW (R5) high-entropy alloys (HEAs). The MLIP enabled accurate high-throughput calculations of the elastic and mechanical properties of various non-equimolar R4 and R5 alloys, which are otherwise very time-consuming calculations when performed using density functional theory (DFT). We demonstrate that the MLIP predicted properties compare well with the DFT results on various test cases, and are consistent with the available experimental data. MLIPs are also utilized for high-throughput screening of non-equimolar R4 candidates by guided iterative tuning of R4 compositions, to discover candidate materials with promising hardness–ductility combinations. We also used this approach to study the effect of Ti concentration on the elastic and mechanical properties of R4, by statistically averaging the properties of over 100 random structures. The MLIPs predicted the hardness and bulk modulus of equimolar R4 and R5 HEAs which were validated using experimentally measured Vicker’s hardness and modulus. This approach opens a new avenue for employing MLIPs for screening HEA candidates.

Microstructure-aware models are necessary to predict the behavior of material based on process knowledge or to extrapolate mechanical properties of materials to environmental conditions which are not easily reproduced in the laboratory, e.g., nuclear reactor environments. Elemental Ta provides a relatively simple BCC system in which to develop a microstructural understanding of deformation processes which can then be applied to more complicated BCC alloys. In situ neutron diffraction during compressive deformation and subsequent heat treatment have been used to monitor the evolution of microstructural features in Ta throughout simulated processing steps. Crystallographic texture and dislocation density are determined as a function of first plastic strain, then temperature. Lattice strains are determined and attributed to stresses at macroscopic, grain and dislocation length scales. The increase of the dislocation density through deformation and subsequent recovery during heat treatment is monitored through the changing diffraction line profile. Also, randomization of the texture is used as a signature of recrystallization. The recovery of dislocations through annihilation is not observed to depend on the initial dislocation density in the range studied here. In contrast, recrystallization is observed to depend strongly on the initially dislocation density.