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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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At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Laser‐Powder Melt Pool Solidification Dynamics and Microstructural Engineering of Ti‐5553 Microlattices

The fine geometric and topological control afforded by additive manufacturing technologies has enabled the manufacture of architected materials across length scales, and enabling tunable mechanical performance as a function of local and global design. Progress has been made to tune the mechanical response of architected materials through geometry, but understanding how the geometry and processing conditions will inform the microstructure remains a challenge due to the rapid solidification in laser powder bed fusion. This study uses in situ X-ray imaging and electron backscatter diffraction microscopy to demonstrate that the melt pool size, microstructure morphology, and elastic strain distribution is influenced by a combination of lattice geometry and laser processing conditions. These results indicate that within larger melt pools the local thermal gradients are sufficient to enable a columnar-to-equiaxed transition across the melt pool. Furthermore, the solidification mechanisms producing these microstructures are examined across the first 5 ms of melting and solidification, described via in situ high-speed X-ray imaging and mirrored via multiphysics simulation.

additive manufacturing↗
DOE OSTI2026

Anomalous Spin‐Optical Helical Effect in Ti‐Based Kagome Metal

The kagome lattice stands as a rich platform for hosting a wide array of correlated quantum phenomena, ranging from charge density waves and superconductivity to electron nematicity and loop current states. Direct detection of loop currents in kagome systems has remained a formidable challenge due to their intricate spatial arrangements and the weak magnetic field signatures they produce, and this has made their identification experimentally subtle. This has left their existence and underlying mechanisms a topic of intense debate. In this work, we uncover signatures compatible with loop currents: spin handedness-selective signals that surpass conventional dichroic, spin, and spin-dichroic responses. We observe this phenomenon in the kagome metal CsTi 3 ⁢Bi 5 and we call it the anomalous spin-optical helical effect. This effect arises from the coupling of light's helicity with spin-orbital electron correlations, thereby providing an indirect yet sensitive approach to probe loop-current–related electronic correlations in quantum materials. Our discovery not only enriches the debate surrounding loop currents but also offers new experimental strategies to exploit the electronic phases of quantum materials via light–matter interaction.

anomalous spin-optical helical effect↗
DOE OSTI2025

Dissolution of Nb(C,N) and (Ti,V)N in the Coarse-Grained Heat-Affected Zone of Electric Resistance Welded X65 Line Pipe

Nb-rich and Ti/V-rich precipitates were introduced into the X65 linepipe via thermomechanical controlled processing. Their dissolution kinetics were investigated in the coarse-grained heat-affected zone after electric resistance welding. Nb-rich precipitates were found to dissolve completely, whereas Ti/V-rich precipitates were found to dissolve partially, because of slower dissolution kinetics. In conclusion, a complete dissolution of Nb-rich precipitates was associated with significant austenite grain coarsening, which promoted bainite as a quench product, which might have reduced the impact toughness.

microstructure↗
DOE OSTI2020

Leveraging Solute Segregation in Laser Powder Bed Fusion to Achieve Superior Strength and Ductility Via Single-Step Heat Treatment in Ti-Free Grade 300 Maraging Steel

Maraging steels are Fe-Ni precipitation-strengthened steels that can be heat treated for achieving high strength with an accompanying loss in ductility, a trend common to most metals. Here, we show that the segregation of Ni during laser powder bed fusion, commonly considered a nuisance that is dealt with via solutionizing heat treatments, can be leveraged via direct aging to achieve higher strength and ductility simultaneously, compared to a solutionized and aged condition. The high dislocation density in combination with solute segregation in the as-fabricated material promotes the precipitation of Mo-rich intermetallic phases that are responsible for the high strength, and the Ni segregated regions facilitate austenite reversion during direct aging. The reverted austenite enhances the elongation via transformation-induced plasticity. While overcoming the strength–ductility conundrum, direct aging can significantly reduce the energy and emissions associated with multistep heat treatments, especially in the global tool and die industry, while laser powder bed fusion (LPBF) can enable tools with integrated cooling channels for increased downstream production efficiency.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Image-Based Fracture Surface Defect Characterization Methods for Additively Manufactured Ti-6Al-4V Tested in Fatigue

Abstract Fatigue initiation in additively manufactured samples/parts often occurs at processed-induced defects such as lack-of-fusion (LoF), keyhole, or other morphological/microstructural defects that have unique characteristics and measurable qualities. Attempts at identifying and minimizing such defects have utilized optimized processing conditions along with in situ and ex situ characterization that includes metallography and/or X-ray computed tomography (XCT). This paper highlights the benefits of using fracture surface analyses to detect and quantify defects that may not be detected by metallography/XCT due to sectioning and resolution limits. In addition to using manual quantification of fatigue initiating LoF and keyhole defects on fracture surfaces, image-based machine learning using convolutional neural networks such as U-Net were also used to automate the process. Statistical analyses were used to identify the extreme cases of defects that initiated and accelerated fatigue and to model the distribution of defect size and shape characteristics to distinguish the type of defect. Initial results show agreement between trained machine learning models and ground truth data in defect segmentation, and the distributions of defect characteristics are distinguishable to particular process-induced defect types.

Materials Science↗