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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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214 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

High-Throughput Microstructural Characterization and Process Correlation Using Automated Electron Backscatter Diffraction

The need to optimize the processing conditions of additively manufactured (AM) metals and alloys has driven advances in throughput capabilities for material property measurements such as tensile strength or hardness. High-throughput (HT) characterization of AM metal microstructure has fallen significantly behind the pace of property measurements due to intrinsic bottlenecks associated with the artisan and labor-intensive preparation methods required to produce highly polished surfaces. This inequality in data throughput has led to a reliance on heuristics to connect process to structure or structure to properties for AM structural materials. In this study, we show a transformative approach to achieve laser powder bed fusion (LPBF) printing, HT preparation using dry electropolishing and HT electron backscatter diffraction (EBSD). This approach was used to construct a library of > 600 experimental EBSD sample sets spanning a diverse range of LPBF process conditions for AM Kovar. This vast library is far more expansive in parameter space than most state-of-the-art studies, yet it required only approximately 10 labor hours to acquire. Build geometries, surface preparation methods, and microscopy details, as well as the entire library of >600 EBSD data sets over the two sample design versions, have been shared with intent for the materials community to leverage the data and further advance the approach. Using this library, we investigated process–structure relationships and uncovered an unexpected, strong dependence of microstructure on location within the build, when varied, using otherwise identical laser parameters.

Characterization and Analytical Technique↗
DOE OSTI2026

In-situ sensor monitoring of multi-class gas porosity formation in laser powder bed fusion using convolutional neural network

In-situ monitoring of defect formation remains a significant challenge in the laser powder bed fusion (LPBF) process. Recent advances have enabled real-time defect detection with machine learning and in-situ sensing technologies; however, most studies focus on binary classification of keyhole pores, limiting nuanced multi-class pore differentiation and formation mechanisms. This work introduces a multi-class pore detection framework (no pore, small pores < 15 µm, and large pores > 15 µm) by leveraging photodiode sensor data alongside high-fidelity synchrotron X-ray imaging. The 15 µm threshold is selected to distinguish between two fundamentally different defect mechanisms, following the physical size-mechanism boundary established by prior high-resolution synchrotron X-ray characterization of Al6061 LPBF. Distinguishing these classes is critical because large keyhole pores are structurally detrimental, whereas small gas pores are often benign, requiring different process control strategies. Thermal emission monitoring data collected simultaneously with high-speed X-ray imaging at the Stanford Synchrotron Radiation Lightsource (SSRL), are correlated with subsurface melt pool dynamics to establish ground truth. Continuous Wavelet Transform (CWT) with optimized parameters converts the photodiode time-series signals into time–frequency images, facilitating feature extraction. Convolutional Neural Networks (CNN) are then applied for real-time multi-class pore classification in an average inference time of 1 ms per signal window. It achieves 79% accuracy and an Area Under the Receiver Operating Characteristic curve (AUC ROC) score of 0.89 with five-fold cross-validation. The results demonstrate that coupling CWT-based feature engineering with CNN architecture enables reliable multi-class pore detection in Al6061 builds using affordable in-situ sensors. This approach advances scalable and affordable quality assurance in additive manufacturing by moving beyond binary defect detection toward more nuanced classification of porosity mechanisms with in-situ sensors and machine learning.

Laser powder bed fusion, Multi-class pores, In-sit↗
DOE OSTI2024

Clarifying the formation of equiaxed grains and microstructural refinement in the additive manufacturing of Ti-Cu

Controlling microstructural evolution in metallic additive manufacturing (AM) is difficult, especially in producing refined as-built grains instead of coarse, directional grains. Traditional solutions involve adding inoculants to AM feedstocks, but titanium (Ti) alloys cannot employ this approach without producing detrimental secondary phases. Ti-Cu (Ti-copper) alloys offer a solution through constitutional supercooling and/or solid state thermal cycling under AM conditions. This work analyzes a compositionally graded directed energy deposition (DED) Ti-Cu build, single-melt laser tracks, and dilatometric heat treatments to evaluate if, when, and by what mechanism(s) microstructural refinement occurs. Refinement by inoculation of unmelted powder particles was also considered. Constitutional supercooling produced no net microstructural refinement as any equiaxed dendrites which form are remelted with new deposition. This finding agreed with solidification modeling of powder bed fusion-laser beam (PBF-LB) and DED builds. Solid state thermal cycling refined microstructures only during ex-situ dilatometric heat treatments, suggesting build parameter optimization is needed to achieve refinement in-situ. Accidental heterogeneous nucleation on unmelted Ti powder, originating from the different thermophysical properties of Ti and Cu, provided the most significant microstructural refinement. This work systematically assesses the microstructural refinement mechanisms of Ti-Cu in AM builds and offers insights into microstructural control in eutectoid alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2024

ExaCA grain structure predictions for laser powder bed fusion processing

This dataset contains simulated cross-sections of laser powder bed fusion grain structures produced using the microstructure model ExaCA, in turn using time-temperature history data produced using the heat transport model AdditiveFOAM. These predictions show the grain structure using various permutations of hatch spacing and nucleation density. Also contained in the dataset are the input files necessary to reproduce the results. AdditiveFOAM (https://github.com/ORNL/AdditiveFOAM) and ExaCA (https://github.com/LLNL/ExaCA), both open source software, are required to reproduce the results in this dataset using the given input files. This DOI was updated 12/09/2025.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Computer vision models and advanced TEM imaging for microstructures of irradiated AM316 stainless steels

Advancements were made in automating microscopy-based material characterization, particularly in studying irradiation effects on additively manufactured (AM) materials using machine learning (ML) and computer vision (CV). These automation efforts address the challenges of analyzing complex microstructures, accelerating the detection of irradiation-induced defects. Two CV models were developed at Argonne National Laboratory (ANL) to enhance transmission electron microscopy (TEM) analysis of irradiated AM 316 stainless steel. The first model focused on the detection of irradiation-induced dislocation loops, which contribute to material hardening and embrittlement. These loops, categorized as faulted or perfect, were automatically detected and classified using a Mask R-CNN model trained on TEM images from both in-situ and ex-situ ion irradiation experiments. The model achieved high accuracy, with precision, recall, and F1 scores of 0.839, 0.734, and 0.776, respectively, demonstrating its effectiveness in analyzing dislocation loops in irradiated AM materials. The second CV model was developed to analyze the size and wall thickness of dislocation cells in laser powder bed fusion (LPBF) 316 stainless steel. Using a U-Net++ architecture with EfficientNet as the encoder, the model was trained on TEM images to segment and measure cell size and wall thickness.

36 MATERIALS SCIENCE↗
DOE OSTI2025

High-resolution in-situ characterization of laser powder bed fusion via transmission X-ray microscopy at X-ray free electron lasers

In this work, we describe the instrumentation used to perform the first operando transmission X-ray microscopy (TXM) and simultaneous X-ray diffraction of laser melting simulating laser powder bed fusion on the XCS instrument at the Linac Coherent Light Source (LCLS) X-ray free-electron laser (XFEL). Our TXM with 40× magnification in the X-ray regime at 11 keV gave spatial resolutions down to 940 nm per line pair, with effective pixel sizes down to 206 nm, image integration times of <100 fs, and frame rates tunable between 2.1 and 119 ns for two probe frames (0.48 GHz to 8.4 MHz). Images were recorded on Zyla and Icarus (UXI) detectors to trade off between spatial resolution and time dynamics. A 1 kW CW IR laser was coupled into the interaction point to conduct pump–probe studies of laser melting and solidification dynamics. Our temporal and spatial resolution with attenuation-based contrast exceeds that currently possible with synchrotron-based high-speed radiography. This system was sensitive to feature velocities of 10–12000 m s −1 but we did not observe any motion in this range in the laser melting of Al6061 alloy. Shockwaves were not observed and hot cracking proceeded at velocities below the detection limits. Pore accumulation was observed between successive shots, indicating that bubble escape mechanisms were not active. With proper experimental design, the spatial resolution, contrast and field of view could be further improved or modified. The increased brightness and narrower bandwidth of the XFEL allowed for this imaging technique and it lays the groundwork for a wide range of operando techniques to study additive manufacturing.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Parametric Optimization of Nanostructured Alumina Forming Austenitic Alloys

Alumina-forming austenitic (AFA) alloys provide excellent oxidation resistance through the formation of a stable alumina scale, but their relatively high Ni content might limit application in core structural components because of helium generation and swelling under irradiation. Increasing the density of nanoscale features in the microstructure is a promising route to improve sink strength and mitigate these effects. Conventional oxide dispersion–strengthened (ODS) steels achieve this through mechanical alloying (MA), but the approach is costly and difficult to scale. In this milestone, additive manufacturing was applied as an alternative pathway to fabricate nanostructured AFA (NAFA) alloys with dual precipitation of oxides and nitrides enabled by reactive atmosphere processing. In FY25, two compositions, NAFA-1 (AFA-05) and NAFA-2, were produced and compared. Both alloys achieved densities above 99% of theoretical values, but NAFA-2 exhibited microcracking associated with Nb-rich intermetallic formation, indicating the need for further chemistry optimization. Nitrogen and oxygen uptake was confirmed on selected builds, demonstrating the intended dual-precipitate dispersion. Mechanical testing showed that AM-processed NAFA alloys exceed conventionally produced ODS steels in high-temperature tensile strength and fracture toughness. Preliminary Cu ion irradiation results further suggest improved resistance compared to wrought austenitic alloys. While the current alloys are promising for their compatibility in high-temperature air or liquid Pb environments, modifications to composition will be required for long-term operation in high-temperature impure sodium systems. Future work will focus on refining alloy chemistry to suppress laser powder bed fusion–induced cracking and scaling production to reactor-relevant dimensions using directed energy deposition as a pathway toward net-shape cladding fabrication.

36 MATERIALS SCIENCE↗
DOE OSTI2024

On the Mechanical Behavior of LP-DED C103 Thin-Wall Structures

Laser Powder Directed Energy Deposition (LP-DED) can produce thin-wall features on the order of 1 mm. These features are essential for large structures operating in extreme environments such as regeneratively cooled nozzles and heat exchangers, which often make use of refractory metals. In this work, the mechanical behavior of LP-DED C103 was investigated via quasi-static tensile testing and low cycle fatigue (LCF) testing. The effects of vacuum stress relief (SR) and hot isostatic pressing (HIP) heat treatments were investigated for specimens in the vertical and horizontal build orientations during tensile testing. The AB and SR properties were lower than literature values for wrought and laser powder bed fusion (L-PBF) bulk components but higher than electron beam powder bed fusion (EB-PBF). The application of a HIP cycle improved strength by 7% and ductility by 27% past the initial as-built condition. Fracture images reveal that interlayer stress concentration sites are responsible for fracture in specimens in the vertical orientation. Meanwhile, fracture in the horizontal specimens mainly propagates at a slanted angle typical of plane stress conditions. The LCF results show cycles to failure ranging from 100 cycles to 8000 cycles for max strain levels of 2% and 0.5%, respectively. Fractography on the fatigue specimens reveals an increasing propagation zone as max strain levels are increased. The impact of these findings and future work are discussed in detail.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Multiscale Modeling of Nanoparticle Precipitation in Oxide Dispersion-Strengthened Steels Produced by Laser Powder Bed Fusion

Laser Powder Bed Fusion (LPBF) enables the efficient production of near-net-shape oxide dispersion-strengthened (ODS) alloys, which possess superior mechanical properties due to oxide nanoparticles (e.g., yttrium oxide, Y-O, and yttrium-titanium oxide, Y-Ti-O) embedded in the alloy matrix. To better understand the precipitation mechanisms of the oxide nanoparticles and predict their size distribution under LPBF conditions, we developed an innovative physics-based multiscale modeling strategy that incorporates multiple computational approaches. These include a finite volume method model (Flow3D) to analyze the temperature field and cooling rate of the melt pool during the LPBF process, a density functional theory model to calculate the binding energy of Y-O particles and the temperature-dependent diffusivities of Y and O in molten 316L stainless steel (SS), and a cluster dynamics model to evaluate the kinetic evolution and size distribution of Y-O nanoparticles in as-fabricated 316L SS ODS alloys. The model-predicted particle sizes exhibit good agreement with experimental measurements across various LPBF process parameters, i.e., laser power (110–220 W) and scanning speed (150–900 mm/s), demonstrating the reliability and predictive power of the modeling approach. The multiscale approach can be used to guide the future design of experimental process parameters to control oxide nanoparticle characteristics in LPBF-manufactured ODS alloys. Additionally, our approach introduces a novel strategy for understanding and modeling the thermodynamics and kinetics of precipitation in high-temperature systems, particularly molten alloys.

Wang, Zhengming (ORCID:0000000241627112)↗
DOE OSTI2025

Strain-rate hardening enhances fatigue resistance of AlSi10Mg alloy at 350°C

The high cycle fatigue behavior of laser powder bed fusion processed AlSi10Mg has been investigated at 350 °C (T/T m ∼ 0.7). The alloy exhibited a fatigue strength of 30 MPa defined by runout after 10 7 cycles at a conventional loading frequency of 20 Hz, corresponding to a notable fatigue strength to ultimate tensile strength ratio of 0.73. The surprising fatigue resistance was attributed to the strain-rate hardening effect at high fatigue loading frequency relative to tensile loading rates at 350 °C. The strain-rate hardening effect was validated by performing ultrasonic fatigue tests (20 kHz loading frequency) with three orders of magnitude higher strain-rates than those at conventional loading frequency. The higher strain-rates in ultrasonic fatigue increased the magnitude of strain-rate hardening resulting in longer AlSi10Mg fatigue lives compared to fatigue at conventional frequency, thus confirming the strain-rate hardening effect. The fatigue crack initiation mechanism was strain-rate dependent. Post-mortem microstructural examination revealed intergranular cavitation inside clusters of fine equiaxed grains. The cavities interlinked with each other to initiate near-surface fatigue cracks at conventional frequency. Cavitation occurred to a lesser extent at the ultrasonic frequency. As a result, fatigue cracks initiated at pre-existing processing defects near the surface in ultrasonic fatigue samples. Finally, this investigation underscores the role of fine grain clusters in promoting high-temperature fatigue crack initiation and indicates a possible trade-off between printability via grain refinement and high-temperature fatigue resistance of additively manufactured alloys.

Additive manufacturing↗
DOE OSTI2024

L-PBF High-Throughput Data Pipeline Approach for Multi-modal Integration

Abstract Metal-based additive manufacturing requires active monitoring solutions for assessing part quality. Multiple sensors and data streams, however, generate large heterogeneous data sets that are impractical for manual assessment and characterization. In this work, an automated pipeline is developed that enables feature extraction from high-speed camera video and multi-modal data analysis. The framework removes the need for manual assessment through the utilization of deep learning techniques and training models in a weakly supervised paradigm. We demonstrate this pipeline’s capability over 700,000 high-speed camera frames. The pipeline successfully extracts melt pool and spatter geometries and links them to corresponding pyrometry, radiography, and processparameter information. 715 individual prints are examined to reveal melt pool areas that exceeds 0.07 mm 2 and pyrometry signal over a threshold (375 pyrometry units) were more likely to have defects. These automated processes enable massive throughput of characterization techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Nondestructive Modular Leak Detection in 3D Printed 316L Stainless Steel Pipes via Laser Powder Bed Fusion

This research investigates the leak detection features of 316L Stainless Steel pipe structures manufactured via Laser Powder Bed Fusion (LPBF). This work involves the design of a modular sensor system integrating nondestructive evaluation (NDE) methods, including thermal imaging and ultrasonic frequency detection to detect and characterize leaks in components. This aims to improve leak detection sensitivity within medium-pressure gas systems, during continuous operation without halting flow or introducing safety risks. The system could be adaptable for use on unmanned aerial vehicles (UAVs), enabling remote leak detection in active environments. A custom pneumatic system incorporating temperature and pressure sensors was assembled to detect leaks in LPBF-printed 316L SS tee pipes. Experimental results and simulations confirm the system’s effectiveness in leak detection and material evaluation. This research program also integrated a Python-based image recognition platform based on a metallography and optical microscopy to assess the porosity and complement the leak detection data on the printed structures. This allows a detailed analysis of pore distribution and internal leak paths, which could compromise structural integrity, critical for quality control during manufacturing. Findings suggest that the investigated approach holds potential for enhancing leak detection technologies and adapt them for advanced manufactured parts.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Part-scale microstructure prediction for laser powder bed fusion Ti-6Al-4V using a hybrid mechanistic and machine learning model

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.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The effect of heat treatment on the defect evolution in LPBF 316H stainless steel

This work investigates the effect of processing and heat treatment on defect evolution and irradiation response of laser powder bed fusion (LPBF) 316H stainless steel, with comparisons to LPBF 316L and wrought 316L/316H. Using in-situ and ex-situ ion irradiations across a wide parameter space—temperature (300–675 °C), dose (0.2–25 dpa), dose rate (10 -3 –10 -5 dpa/s), and helium co-implantation (20–2500 appm)—we correlated void swelling, dislocation loop evolution, and segregation behavior with pre-irradiation microstructures (as-built, stress-relieved, solution-annealed, and cold-worked). Results show that swelling is strongly controlled by dislocation density: intermediate densities maximize swelling, while solution annealing or cold working reduce susceptibility to levels comparable to wrought alloys. Low dose rates and helium both promote cavity nucleation and lower the incubation barrier, with helium suppressing the role of dislocation density and driving swelling behavior toward wrought-like response. Loop evolution in SA LPBF 316H resembles wrought 316L but shows localized denuded zones near low-angle grain boundaries. STEM-EDS mapping further revealed Ni segregation at voids and sparse Al-rich oxides, without evidence of Ni–Si precipitates. Collectively, these findings identify dislocation density, helium content, and dose rate as the factors governing swelling in LPBF 316H and provide mechanistic datasets for model validation, supporting LAIN and the qualification of AM austenitic steels for nuclear service.

316 stainless steel↗
DOE OSTI2025

Collaborative or competitive phase transformation processes in an additively manufactured-maraging steel M300

Although retained austenite is present in minor amounts in as-built additively manufactured-maraging steels, its evolution during ageing is key to controlling the final microstructure. This study investigates the relationship between austenite reversion and precipitation evolution during ageing, particularly whether these processes compete or cooperate. Using Scanning/Transmission Electron Microscopy, Atom Probe Tomography and Thermo-Calc® PRISMA simulations, the presence and evolution of retained austenite, Ni 3 Ti and Fe 7 Mo 6 intermetallic phases was characterised across different ageing conditions. Experimental results revealed the presence of non-enriched austenite in the as-built condition. Nevertheless, with increasing ageing temperature, Ni and Mo segregation became evident. High-Resolution Transmission Electron Microscopy revealed that Ni 3 Ti precipitation primarily occurred within the martensitic matrix, while Fe 7 Mo 6 nucleated preferentially at the austenite-martensite interface. PRISMA simulations indicated early and rapid precipitation in solute-enriched areas (i.e., intercellular regions), with Ni 3 Ti forming prior to Fe 7 Mo 6 . Both experimental and simulation results suggested Ni diffusion controls retained austenite growth, while Ti and Mo drive Ni 3 Ti and Fe 7 Mo 6 precipitation, respectively. The evidence reported in this work supports a both collaborative and independent phase transformation mechanism, where austenite reversion and precipitation co-evolve.

42 ENGINEERING↗
DOE OSTI2026

Microstructural evolution, defect mitigation, and precipitation behavior in AA6061 via laser powder bed fusion with high-temperature substrate heating

Defects, particularly solidification cracking, remain persistent challenges in the laser powder bed fusion (LPBF) processing of AA6061 aluminum alloy. This study systematically investigates defect mitigation, microstructural evolution, and mechanical properties associated with high-temperature substrate preheating at 500 °C. Comprehensive microstructural analyses, including characterization of defects, grain structures, and precipitation behavior, were performed on samples in both as-built and T6 heat-treated states. Elevated preheating substantially reduced solidification cracking across a wide processing window, while demonstrating decreased crack sensitivity to laser parameters. Columnar cracks along the build direction were observed despite substrate preheating. Lack-of-fusion and keyhole porosity were effectively eliminated, though gas-induced microporosity persisted at higher powers. In-depth characterization of two distinct laser power and speed conditions confirmed the formation of micron-sized, non-coherent Mg 2 Si precipitates under heated substrate conditions, alongside α-AlFeCrMnSi intermetallic phases indicative of in-situ thermal effects during fabrication. Subsequent T6 heat treatment revealed the formation of fine, coherent needle-shaped β″ strengthening precipitates. Despite substantial differences in processing parameters, comparable mechanical properties were measured in the as-built samples (∼52 MPa yield strength, ∼130 MPa tensile strength), primarily due to reduced strain hardening effects and consistent precipitation characteristics. Meanwhile, the T6 heat treatment led to significant improvement in properties, enhancing yield strength by over 400%, aligning closely with the performance of conventional wrought AA6061-T6. These findings underscore that high-temperature substrate preheating offers an effective means to suppress cracks, control precipitation, and enhance mechanical performance in LPBF-processed AA6061.

36 MATERIALS SCIENCE↗