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An, Ke (ORCID:000000026093429X)

Publications and source records attributed to An, Ke (ORCID:000000026093429X).

8 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Understanding the High-Temperature Deformation Behaviors in Additively Manufactured Al6061+TiC Composites via In Situ Neutron Diffraction

Aluminum matrix composites (AMCs) are designed to enhance the performance of conventional aluminum alloys for engineering applications at both room and elevated temperatures. However, the dynamic phase-specific deformation behavior and load-sharing mechanisms of AMCs at elevated temperatures have not been extensively studied and remain unclear. Here, in situ neutron diffraction experiments are employed to reveal the phase-specific structure evolution of additively manufactured Al6061+TiC composites under compressive loading at 250 °C. It is found that the addition of a small amount of nano-size TiC significantly alters the deformation behavior and increases the strength at 250 °C in comparison to the as-printed Al6061. Unlike the two-stage behavior observed in Al6061, the Al6061+TiC composites exhibit three stages during compression triggered by changes in the interphase stress states. Further analysis of Bragg peak intensity and broadening reveals that the presence of TiC alters the dislocation activity during deformation at 250 °C by influencing dislocation slip planes and promoting dislocation accumulation. These findings provide direct experimental observations of the phase-specific dynamic process in AMCs under deformation at an elevated temperature. The revealed mechanisms provide insights for the future design and optimization of high-performance AMCs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The 650 °C Tensile Deformation of Graded IN718-René41 Superalloy Fabricated by Laser Blown-Powder Directed Energy Deposition

The microstructure and 650 °C tensile properties of a compositionally graded IN718-René41 (718-R41) superalloy fabricated by laser blown-powder directed energy deposition (DED-LB/M) are investigated to understand structure–property relationships and baseline tensile properties. Digital Image Correlation (DIC), in situ neutron diffraction, and conventional characterization techniques are performed to study the as-built and heat-treated states. The applied heat treatment generates static recrystallization and equiaxed grains in 718-rich compositions, while R41-rich compositions remain partially or un-recrystallized possibly influenced by a higher MC carbide fraction (>0.5%). The yield strengths of the 718 and R41 sections in the heat-treated state are comparable to wrought forms but the graded compositions show weakness due to unoptimized heat treatment. Diffraction elastic constants first decrease and then increase along the 718-R41 composition gradient, while a small difference is observed between the as-built and heat-treated states and γ, γ′ phases. Overall, the compositionally graded region shows a smooth transition in the elastic properties. Grain-level load transfer from the (220) to (200) grains shows compositional dependence, and qualitatively agrees with DIC-measured macroscopic yield strength. Within the (200) grains, the γ/γ′ phases deform elastically until the γ phase yields and afterwards, the γ′ phase takes load from the γ phase.

Huang, Shenyan (ORCID:0000000196528114)↗
DOE OSTI2023

Adaptive sampling for accelerating neutron diffraction-based strain mapping *

Abstract Neutron diffraction is a useful technique for mapping residual strains in dense metal objects. The technique works by placing an object in the path of a neutron beam, measuring the diffracted signals and inferring the local lattice strain values from the measurement. In order to map the strains across the entire object, the object is stepped one position at a time in the path of the neutron beam, typically in raster order, and at each position a strain value is estimated. Typical dwell times at neutron diffraction instruments result in an overall measurement that can take several hours to map an object that is several tens of centimeters in each dimension at a resolution of a few millimeters, during which the end users do not have an estimate of the global strain features and are at risk of incomplete information in case of instruments outages. In this paper, we propose an object adaptive sampling strategy to measure the significant points first. We start with a small initial uniform set of measurement points across the object to be mapped, compute the strain in those positions and use a machine learning technique to predict the next position to measure in the object. Specifically, we use a Bayesian optimization based on a Gaussian process regression method to infer the underlying strain field from a sparse set of measurements and predict the next most informative positions to measure based on estimates of the mean and variance in the strain fields estimated from the previously measured points. We demonstrate our real-time measure-infer-predict workflow on additively manufactured steel parts—demonstrating that we can get an accurate strain estimate even with 30%–40% of the typical number of measurements—leading the path to faster strain mapping with useful real-time feedback. We emphasize that the proposed method is general and can be used for fast mapping of other material properties such as phase fractions from time-consuming point-wise neutron measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗