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Results for “laser remelting experiments”

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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

LPBF Processability of NiTiHf Alloys: Systematic Modeling and Single-Track Studies

Research into the processability of NiTiHf high-temperature shape memory alloys (HTSMAs) via laser powder bed fusion (LPBF) is limited; nevertheless, these alloys show promise for applications in extreme environments. This study aims to address this limitation by investigating the printability of four NiTiHf alloys with varying Hf content (1, 2, 15, and 20 at. %) to assess their suitability for LPBF applications. Solidification cracking is one of the main limiting factors in LPBF processes, which occurs during the final stage of solidification. To investigate the effect of alloy composition on printability, this study focuses on this defect via a combination of computational modeling and experimental validation. To this end, solidification cracking susceptibility is calculated as Kou’s index and Scheil–Gulliver model, implemented in Thermo-Calc/2022a software. An innovative powder-free experimental method through laser remelting was conducted on bare NiTiHf ingots to validate the parameter impacts of the LPBF process. The result is the processability window with no cracking likelihood under diverse LPBF conditions, including laser power and scan speed. This comprehensive investigation enhances our understanding of the processability challenges and opportunities for NiTiHf HTSMAs in advanced engineering applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Effect of Cooling Rate on Nano-Eutectic Formation in Laser Surface Remelted and Rare Earth Modified Hypereutectic Al-20Si Alloys

Laser Surface Remelting (LSR) was applied to arc-melted Al-20Si-0.2Sr, Al-20Si-0.2Ce, and Al-20Si hypereutectic alloys to refine microstructures. Experiments revealed that microstructures in the melt pool varied from fully eutectic to a mixture of Al dendrites and inter-dendritic eutectic. We calculated cooling rates using the Eagar-Tsai model and correlated cooling rates with characteristic microstructures, revealing that a cooling rate on the order of 104 K/s could lead to maximized fully eutectic microstructure morphology. Due to rapid solidification, the Si composition in the LSR eutectic was measured at 18.2 wt.%, higher than the equilibrium eutectic composition of 12.6 wt.%Si. Compared to Al-20Si, Ce addition had no significant effect on the volume fraction of the fully eutectic structure but refined Si fibers to approximately 30 nm in diameter. Sr addition did not further refine the diameter of eutectic Si fibers compared to Al-20Si but increased the volume fraction of the fully eutectic microstructure morphology. The refinement ratio (φ) of the Si fiber diameter from the bottom of the melt pool to the surface for the three alloys was similar, at around 28%. The established correlation between the cooling rate and the size and morphology of the microstructure within the melt pool will enable tailoring of the microstructure in laser-processed as well as deposited alloys for high strength and plasticity.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Accurate and efficient predictions of keyhole dynamics in laser materials processing using machine learning-aided simulations

The keyhole phenomenon has been widely observed in laser materials processing, including laser welding, remelting, cladding, drilling, and additive manufacturing. Keyhole-induced defects, primarily pores, dramatically affect the performance of final products, impeding the broad use of these laser-based technologies. The formation of these pores is typically associated with the dynamic behavior of the keyhole. So far, the accurate characterization and prediction of keyhole features, particularly keyhole depth, as a function of time, has been a challenging task. In situ characterization of keyhole dynamic behavior using the synchrotron X-ray technique is informative but complicated and expensive. Current simulations are generally hindered by their poor accuracy and generalization abilities in predicting keyhole depths due to the lack of accurate laser absorptance data. In this study, we develop a machine learning-aided simulation method that accurately predicts keyhole dynamics, especially in keyhole depth fluctuations, over a wide range of processing parameters. In two case studies involving titanium and aluminum alloys, we achieve keyhole depth prediction with a mean absolute percentage error of 10 %, surpassing those simulated using the ray-tracing method with an error margin of 30 %, while also reducing computational time. This exceptional fidelity and efficiency empower our model to serve as a cost-effective alternative to synchrotron experiments. Our machine learning-aided simulation method is affordable and readily deployable for a large variety of materials, opening new doors to eliminate or reduce defects for a wide range of laser materials processing techniques.

Computational fluid dynamics↗