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High moderation per unit volume solid moderator materials like yttrium hydride (YH_x) are necessary for compact nuclear microreactors. However, the phase stability and hydrogen transport processes of YH_x under high-temperature irradiation are largely unknown. Proton irradiation was conducted on YH_x at 300 °C and 580 °C to 0.2 dpa using 1 MeV or 2 MeV protons in a high-vacuum environment. The hydrogen concentration was determined before and after irradiation using elastic recoil detection analysis, and microstructural evolution was examined via post-irradiation scanning transmission electron microscopy and Raman spectroscopy. Dislocation loops and cavities were observed in all conditions; their distribution was correlated with the bombarding proton energy and ion irradiation temperature. This work revealed that hydrogen retention is proportional to the formation of traps for hydrogen gas atoms and identified pathways for hydrogen release. The relative contributions of bulk or fast diffusion paths, such as grain boundaries, delamination boundaries, and stacking faults are discussed; the primary mechanisms of hydrogen loss are likely based on diffusion, ruling out artefacts of the experimental design. In conclusion, the study suggests proton irradiation may be a strong surrogate to study hydrogen transport in hydride moderator materials under irradiation.

Large-scale 3D printing of polymer composite structures has gained popularity and seen extensive use over the last decade. Much of the research related to improving the mechanical properties of 3D-printed parts has focused on exploring new materials and optimizing print parameters to improve geometric control and minimize voids between printed beads. However, porosity at the microstructural level (within the printed bead) has been much less studied although it is almost universally observed at levels of 4 %-10 % when using fiber reinforced materials. This study introduces a vacuum-assist approach that minimizes internal porosity by removing ambient air from the interstitial space between pellets in the hopper and acts as a negative pressure vent for gases that evolve during the initial stages of single-screw extrusion. Vacuum-assisted extrusion was able to reduce porosity below 2 % across a wide range of processing parameters, moisture content, fiber reinforcements, and printing platforms. Specifically, when printing on a large-format extruder (Strangpresse Model-30), the vacuum-assisted extrusion reduced internal porosity by 35–75 % compared to conventional non-vacuum extrusion, and only pores with length scale > 2 microns are affected. The success of this approach prompted the design of a patent-pending continuous vacuum hopper relevant for large-scale 3D printing on commercial systems.

Transferring the excellent intrinsic magnetic properties of SmFe₁₂-based compounds to their extrinsic properties remains the main challenge in the development of high-performance SmFe12-based permanent magnets. Twin formation is one of the reasons for the inability to achieve high coercivity and remanence. Here we have shown that the addition of Co in Sm(Fe_1-xCo_x)_10–11M_1–2 alloys, where M=Ti and V, leads to an increase in twin density. Microstructural characterizations revealed that the atomic arrangement in the twin boundary changes depending on the stabilizing element, which directly influences the local intrinsic magnetic properties. Theoretical investigations showed that the critical grain size at which twin formation can be hindered by grain size reduction decreases when the stabilizer changes from V to Ti. Furthermore, this study shows that the alloy composition influences not only the intrinsic magnetic properties but also the twin formation energy and its grain size dependence, crucial for the design of SmFe12-based permanent magnets.

Laser-wire metal additive manufacturing (AM) is one of the ideal direct energy deposition (DED) processes for creating large-scale parts with a medium level of complexity. However, the DED process involves complex thermal signatures and wide length scales making the fabrication of realistic AM components and part qualification often reliant on experimental trial-and-error optimization. While experimental measurements over the full volume of a part are valuable and necessary, measuring the entire area of a part is significantly laborious and practically infeasible, particularly for large parts in terms of cost and rapid qualification. Therefore, in this work, we developed an effective thermal and microstructure modeling framework based on the Johnson–Mehl-Avrami-Kolmogorov (JMAK) and Koistinen & Marburger (KM) models through a top-down approach that considers plate distortion-affected thermal profiles. A voxel-by-voxel simulation method is used to predict individual phase fractions of Ti-6Al-4 V. The predicted results were validated through detailed metallurgical measurements. A combined voxel-by-voxel approach with a sparse data reconstruction technique produced a near-perfect reconstruction of the original data. This approach anticipates a significant reduction in data points and computation time and resources. Lastly, we conclude with potential extensions of this work to other modeling efforts.

AISI 422 martensitic stainless steel with superior high temperature performance (oxidation resistance and strength) is under evaluation for replacing current heavy-duty piston crown materials, AISI 4140 martensitic steel and micro alloyed steel (MAS) 38MnSiVS5, to fabricate a multimaterial piston. This multimaterial piston concept further improved power density and fuel economy by allowing heavyduty diesel engines to operate at higher temperatures and pressures. Joining AISI 422 steel piston crowns with AISI 4140 steel piston skirts is a key manufacturing step for this multimaterial piston. However, the significant differences in strength, elevated temperature flow stress, alloy chemistry, and temper resistance between these two martensitic steels cause some weldability issues (cracking) and metallurgical challenges (alloying element migration/segregation) when using conventional fusion-based welding processes. Rotary inertia friction welding (RIFW), a solid-state welding process, has been the preferred method to join 4140 crowns to 4140 skirts (and MAS crowns to MAS skirts) in high-volume production of current heavy-duty diesel engine pistons. It has been used to join these two materials with relatively comparable alloy chemistry to fabricate pistons with MAS skirts and 4140 crowns. Meanwhile, RIFW has also been a preferred method of dissimilar metal welding. However, RIFW of dissimilar high-strength martensitic steels has yet to be widely pursued. The interfacial microstructure complexities created by the thermomechanical process and highly nonequilibrium phase transformations during RIFW are a significant challenge for understanding and predicting their joining behavior and have not been reported in detail. Here, in this work, defect-free AISI 422 steel-AISI 4140 multimaterial pistons were successfully fabricated using the RIFW process. The interfacial microstructure and mechanical properties of dissimilar 422/4140 steel RIFW in the as-welded condition were experimentally studied in detail. The results provide critical baseline information for understanding RIFW mechanisms and guiding subsequent postweld heat treatment (PWHT) practice.

Laser powder bed fusion (LPBF) additive manufacturing makes near-net-shaped parts with reduced material cost and time, rising as a promising technology to fabricate Ti-6Al-4V, a widely used titanium alloy in aerospace and medical industries. However, LPBF Ti-6Al-4V parts produced with 67° rotation between layers, a scan strategy commonly used to reduce microstructure and property inhomogeneity, have varying grain morphologies and weak crystallographic textures that change depending on processing parameters. Here, this study predicts LPBF Ti-6Al-4V solidification at three energy levels using a finite difference-Monte Carlo method and validates the simulations with large-area electron backscatter diffraction (EBSD) scans. The developed model accurately shows that a <001> texture forms at low energy and a <111> texture occurs at higher energies parallel to the build direction but with a lower strength than the textures observed from EBSD. A validated and well-established method of combining spatial correlation and general spherical harmonics representation of texture is developed to calculate a difference score between simulations and experiments. The quantitative comparison enables effective fine-tuning of nucleation density (N₀) input, which shows a nonlinear relationship with increasing energy level. Future improvements in texture prediction code and a more comprehensive study of N₀ with different energy levels will further advance the optimization of LPBF Ti-6Al-4V components. These developments contribute a novel understanding of crystallographic texture formation in LPBF Ti-6Al-4V, the development of robust model validation and calibration pipeline methodologies, and provide a platform for mechanical property prediction and process parameter optimization.

Laser powder bed fusion (LPBF) Ti-6Al-4V is widely studied for use in structural applications in aerospace and medical industries, but mechanical anisotropy and microstructural inhomogeneity prohibits its wider adoption. Although successful microstructure prediction models have been developed, a remaining challenge is their limited integration across length/time scales and validation by experimental studies. Here, this work proposes a physics-augmented machine learning surrogate model to unite predictions of LPBF temperature, β phase morphology and texture, and α/α’ formation into a single framework that is calibrated and validated with experiments. First, a phase field (PF) model of the martensitic β→α’ transformation is developed and calibrated using data from in-situ synchrotron cyclic heating/cooling studies quantifying the variation of α phase fraction with time. In parallel, an established finite difference-Monte Carlo (FDMC) model predicts the part-scale temperature profile and β grain formation during solidification. A dataset is developed using LPBF cyclic temperature descriptors from the FDMC model as inputs and corresponding α/α’ phase fraction and width from the PF model as outputs. Five machine learning (ML) regression models are tested and optimized, having mean absolute error in testing ≤ 4 %, and the k-nearest neighbors (KNN) model is selected as the best performing. The KNN model is called at the nodal level during post-processing of the FDMC model to replace and downscale the response of the PF model. The combined agility and accuracy of the hybrid FDMC-ML model enables part-scale microstructure predictions that can be further used for property predictions to accelerate AM process optimization.

The passive film on a dual-phase Al_0.3Cr_0.5Fe₂Mn_0.25Mo_0.15Ni_1.5Ti_0.3 FCC + Heusler (L2₁) compositionally concentrated alloy formed during extended exposure to an applied potential in the passive range in dilute chloride solution was characterized. Each phase, with its own distinct composition of passivating elements, formed unique passive films separated by a heterophase interface. High-resolution, surface sensitive characterization enabled chemical analysis of the passive film formed over individual phases. The film formed over the L2₁ phase had a higher concentration of Al, Ni, and Ti, while the film formed over FCC phase was of similar thickness but contained comparatively higher Cr, Fe, and Mo concentrations, consistent with the differences in bulk microstructure composition. The passive film was continuous across phase boundaries and the distribution of passivating elements (Al, Cr, and Ti) indicated both phases were independently passivated. Spatially resolved analysis of the surface chemistry of the dual-phase CCA revealed that the cation with the highest composition in passive film formed on the FCC phase was Cr (52.4 at. pct) and for the L2₁ phase was Ti (53.1 at. pct) despite the bulk concentration of each element being below 20 at. pct in their respective phases. Al, Cr, and Ti were enriched in both phases within the passive film relative to their respective bulk compositions. In parallel studies, single-phase alloys with compositions representative of the FCC and L2₁ phases were synthesized to evaluate the corrosion behavior of each phase in isolation. The corrosion behavior of the dual-phase alloy showed passivity evidenced by a pitting potential of 0.615 V_SCE in 0.01 M NaCl. The pitting potential and other electrochemical parameters suggested a combination of behaviors of both single-phase samples, suggesting that the global corrosion behavior may be represented by a composite theory applied to phases, their area fractions, and interphase length. However, the interphase in the dual-phase CCA was a local corrosion initiation site and may limit localized corrosion protectiveness. The alloy design implications for optimization of second phase structure and morphology are discussed.

A deep operator network (DeepONet) has been constructed that generates accurate representations of phase-field model simulations for evolving two dimensional metal grain morphology growing from melt. These representations serve as lower resolution, computationally efficient stand-ins for quick parameter space exploration of solutions to the the Allen-Cahn equations that dictate the phase-field model simulations. The experimental target for the phase-field model is a uranium casting system cooling a 434 g uranium charge from a maximum temperature of 1400° C at an average rate of 30° ^C/_min, traversing the crystallographic phases of the pure metal. Experimental parameters inform the phase-field model, whose higher resolution computational model solutions are used to train the DeepONet in a given parameter space with the aim of developing a faster, more efficient method for predicting the solidifying metal's microstructure at different potential experimental values. The final DeepONet generates high accuracy, lower resolution predictions with cumulative relative approximation error over all timesteps of less than 0.5%, while ensuring solutions remain within physically feasible ranges. Further, these relative error values are comparable with other state-of-the-art DeepONet models for microstructure evolution, while significantly reducing the amount of training data required. Training a convolutional neural network simultaneously with the DeepONet, enforcing realistic values at the complex metal grain boundaries, and mathematically encoding boundary conditions into the structure of the DeepONet improved prediction accuracy and computational efficiency over a standard DeepONet model.

Image segmentation is a critical enabler for tasks ranging from medical diagnostics to autonomous driving. However, the correct segmentation semantics — where are boundaries located? what segments are logically similar? — change depending on the domain, such that state-of-the-art foundation models can generate meaningless and incorrect results. Moreover, in certain domains, fine-tuning and retraining techniques are infeasible: obtaining labels is costly and time-consuming; domain images (micrographs) can be exponentially diverse; and data sharing (for third-party retraining) is restricted. To enable rapid adaptation of the best segmentation technology, we propose the concept of semantic boosting: given a zero-shot foundation model, guide its segmentation and adjust results to match domain expectations. Here, we apply semantic boosting to the Segment Anything Model (SAM) to obtain microstructure segmentation for transmission electron microscopy. Our booster, SAM-I-Am, serves as a post-processing engine that extracts geometric and textural features of various intermediate masks to perform mask removal and mask merging operations. We demonstrate a zero-shot performance increase of (absolute) +21.35%, +12.6%, +5.27% in mean IoU, and a -9.91%, -18.42%, -4.06% drop in mean false positive masks across images of three difficulty classes over vanilla SAM (ViT-L).