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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Artificial intelligence inferred microstructural properties from voltage–capacity curves

Abstract The quantification of microstructural properties to optimize battery design and performance, to maintain product quality, or to track the degradation of LIBs remains expensive and slow when performed through currently used characterization approaches. In this paper, a convolution neural network-based deep learning approach (CNN) is reported to infer electrode microstructural properties from the inexpensive, easy to measure cell voltage versus capacity data. The developed framework combines two CNN models to balance the bias and variance of the overall predictions. As an example application, the method was demonstrated against porous electrode theory-generated voltage versus capacity plots. For the graphite|LiMn $$_2$$ 2 O $$_4$$ 4 chemistry, each voltage curve was parameterized as a function of the cathode microstructure tortuosity and area density, delivering CNN predictions of Bruggeman’s exponent and shape factor with 0.97 $$R^2$$ R 2 score within 2 s each, enabling to distinguish between different types of particle morphologies, anisotropies, and particle alignments. The developed neural network model can readily accelerate the processing-properties-performance and degradation characteristics of the existing and emerging LIB chemistries.

25 ENERGY STORAGE↗
DOE OSTI2023

Microstructure-Property Relationships in Epitaxial Cu(In, Ga)Se2 Solar-Cell Absorbers

Epitaxially grown Cu(In,Ga)Se 2 (CIGS) absorber layers were analyzed by various techniques in scanning electron microscopy in order to reveal microstructure-property relationships in these thin films. Owing to their epitaxial nature, these CIGS absorber layers do not contain any grain boundaries, but only anti-phase domains (APDs) and dislocations. By combining electron channeling-contrast imaging, electron backscatter diffraction, and cathodoluminescence (CL), in some cases on identical specimen positions of polished cross-sections of CIGS/Mo/glass stacks, it was possible to correlate the presence and orientations of APDs and dislocations with the lateral distributions of the CL intensity and emission-peak energy. We studied CIGS layers with three different [Ga]/([Ga]+[In]) ratios as well as with and without NaF/KF treatments. Considerable differences between the CIGS layer properties in the microstructure-property relationships were found, depending on the growth parameters. Dislocations in the epitaxial CIGS layers do not tend to exhibit strong CL intensity decreases, which contrasts with the situation in numerous other semiconductor materials.

anti-phase domains↗
NASA NTRS2023

Handbook for Additively Manufactured Materials: Microstructure, Properties, and Fractography

As the additive manufacturing (AM) industry has grown and the processes have matured more alloys have been adapted to the various AM processes. Understanding the microstructure and properties of these AM alloys is important in designing parts that perform appropriately in application. For decades materials handbooks have been informative references for designers and engineers alike to know what properties and microstructures to expect in alloys produced by other forms of manufacturing. AM alloy references for properties and microstructures have been largely limited to literature that may not be subject to the same industry standard heat treatments or in standard process parameters. NASA has worked with partners to characterize various commercially available AM alloys with a high degree of traceability. This work aims utilize industry-relevant, traceable datasets to develop a new handbook for AM alloys to better inform designers and engineers of the microstructures, fractures, and first order properties from over 30 AM alloys produced across the industry.

Additive Manufacturing↗
DOE OSTI2025

Establishing model credibility for process-microstructure-property relationships in additive manufacturing using exascale computing

Additive Manufacturing (AM) of alloys holds significant promise as a disruptive technology in various industries, yet its adoption is often hindered by challenges in achieving consistent part quality. These issues are primarily due to the complex process-microstructure-property (PSP) relationships inherent to AM. Computational models can greatly aid in understanding these relationships, but their widespread impact and adoption has been limited by a lack of validated, open-source, and computationally efficient PSP modeling frameworks and hardware limitations. Here, this study leverages the ExaAM software suite and data from the AMBench-2018 series of laser powder bed fusion (LPBF) benchmark experiments to perform a comprehensive model assessment, including verification, validation, sensitivity analysis, and uncertainty quantification. The RADICAL-EnTK workflow manager was used to perform an ensemble of heat transport, solidification, and mechanical response simulations on the exascale computer Frontier, considering uncertainties in critical model inputs such as laser spot size and nucleation parameters, and consisting of 125 explicit grain structure simulations and 7875 crystal plasticity simulations. For a selected location within the Inconel 625 AMBench-2018 test artifact, sensitivity analysis and uncertainty quantification were performed using the predicted distributions of grain structure and mechanical properties. Qualitative agreement was found between the predicted grain size and texture and the observed AMBench-2018 microstructure, the mean predicted yield stress was within 5% of the experimental measurement mean, and the mean predicted engineering stress at 5% strain was within 10% of the experimental measurement mean. The insights gained from development and validation of the ExaAM PSP modeling framework will help guide future directions for enhancing the credibility and reliability of PSP models in AM, thereby accelerating the adoption of AM technologies in various industries.

Additive manufacturing↗
DOE OSTI2022

Unveiling microstructure-property correlations in nuclear materials with high-energy synchrotron X-ray techniques

Understanding the microstructure-property correlation is critical for the performance evaluation of in-service materials and the development of advanced materials for nuclear reactor applications. Experimental studies are challenging for nuclear materials which are often hazardous. Recent developments in high-energy synchrotron X-ray (HEX) techniques offer the potential to address this challenge, by providing direct observations of internal responses to external stimuli in bulk-like materials, through in situ or 3D measurements. Here, in this review, developments in HEX techniques are introduced and recent applications in nuclear materials are presented. The results offer unprecedented insights into materials performance and provide unique input to computational models.

36 MATERIALS SCIENCE↗
DOE OSTI2024

High-throughput, Ultra-fast Laser Sintering of Ceramics and AI Based Prediction on Processing-Microstructure-Property Relationships

We report high-throughput, ultra-fast laser sintering of alumina sample array and characterization of sample units’ microstructure and hardness, as a fast exploration of laser processing parameters, microstructure, and property. These experimental data were used to train machine-learning (ML) models. Accurate ML predictions were demonstrated for the processing-microstructure-property relationship, specifically in (1) prediction of the microstructure of alumina under arbitrary laser power and (2) prediction of the expected microstructure from the desired hardness. An independent neural network was developed and showed that ML-predicted microstructure had less than 10% error from real ones, in terms of projected hardness. To monitor the microstructure during laser sintering, we demonstrated an ML model that can instantaneously predict ceramic’s microstructure at the laser spot, based on the laser spot brightness. The ML model can generate more than 10 predictions per second, and the error in average grain size was less than 5% from the experimental observations.

36 MATERIALS SCIENCE↗
NASA NTRS1979

Layered synthetic microstructures - Properties and applications in X-ray astronomy

Refinements in vacuum deposition technology have made it possible to produce structures in which two materials are arranged in alternating layers of uniform thickness and as thin as 5 A. Such structures act as Bragg diffractors or 'artificial crystals' for X-rays, as well as they may be regarded as multilayer interference coatings. A dynamical theory is used to show how the properties of layered synthetic microstructures are dependent on the layer materials and thicknesses and how these properties can be tailored to specific applications. Laboratory results at various X-ray wavelengths are presented. In particular, specific X-ray astronomy applications in spectroscopy, imaging, polarimetry and laboratory calibration are discussed.

Underwood, J. H.↗
DOE OSTI2023

Understanding process-microstructure-property relationships in laser powder bed fusion of non-spherical Ti-6Al-4V powder

Powder feedstock is a major cost driver in metal additive manufacturing (AM). Replacing the spherical powder with the cost-efficient non-spherical one can reduce the feedstock cost up to 50% and attract more interest to adopt AM in production and new alloy development. Here, for this paper, a comprehensive study was conducted to understand process-microstructure-property relationships in laser powder bed fusion of hydride-dehydride Ti-6Al-4V powder. We demonstrated that variation of laser scan speed had a significant impact on the grain structure, pore evolution and properties compared to laser power. Dynamic X-ray radiography showed that with decreasing scan speed at a constant laser power, a transition from conduction to keyhole mode laser processing occurred, in which a deeper melt pool at lower scan speed intensified texture. In other words, an increase in laser scan speed resulted in formation of the refined prior β grains with shape factor of ~5, lowering the anisotropy. Furthermore, the degree of variant selection was evaluated based on the analyzed texture as a function of laser power and scan speed. With increasing laser scan speed, the dominant α/α boundary type was altered from type 2 to 4 and the degree of variant selection was noticeably decreased. On the other hand, increasing laser power left the morphology of prior β grains, their size, and the dominant α/α boundary (type 4) unchanged, while the texture and anisotropy were intensified, and the degree of variant selection was slightly decreased. Finally, dependency of surface roughness and microhardness were discussed as a function of laser processing parameters.

variant selection↗
DOE OSTI2021

Elaboration of Metallic Materials by SPS: Processing, Microstructures, Properties, and Shaping

After a few decades of increasing interest, spark plasma sintering (SPS) has now become a mature powder metallurgy technique, which allows assessing its performances toward fabricating enhanced materials. Here, the case of metals and alloys will be presented. The main advantage of SPS lies in its rapid heating capability enabled by the application of high intensity electric currents to a metallic powder. This presents numerous advantages balanced by some limitations that will be addressed in this review. The first section will be devoted to sintering issues, with an emphasis on the effect of the electric current on the densification mechanisms. Then, typical as-SPS microstructures and properties will be presented. In some cases, they will be compared with that of materials processed by conventional techniques. As such, examples of nanostructured materials, intermetallics, metallic glasses, and high entropy alloys, will be presented. Finally, the implementation of SPS as a technique to manufacture complex, near-net shape industrial parts will be discussed.

36 MATERIALS SCIENCE↗
NASA NTRS1991

Microstructure-property relationships in Al-Cu-Li-Ag-Mg Weldalite (tm) alloys, part 2

The microstructure and mechanical properties of the ultrahigh strength Al-Cu-Li-Ag-Mg alloy, Weldalite (tm) 049, were studied. Specifically, the microstructural features along with tensile strength, weldability, Young's modulus and fracture toughness were studied for Weldalite (tm) 049 type alloys with Li contents ranging from 1.3 to 1.9 wt. pct. The tensile properties of Weldalite 049 and Weldalite 049 reinforced with TiB2 particles fabricated using the XD (tm) process were also evaluated at cryogenic, room, and elevated temperatures. In addition, an experimental alloy, similar in composition to Weldalite 049 but without the Ag+Mg, was fabricated. The microstructure of this alloy was compared with that of Weldalite 049 in the T6 condition to assess the effect of Ag+Mg on nucleation of strengthening phases in the absence of cold work.

Langan, T. J.↗
NASA NTRS1986

Microstructure-property relationships in directionally solidified single crystal nickel-base superalloys

Some of the microstructural features which influence the creep properties of directionally solidified and single crystal nickel-base superalloys are discussed. Gamma precipitate size and morphology, gamma-gamma lattice mismatch, phase instability, alloy composition, and processing variations are among the factors considered. Recent experimental results are reviewed and related to the operative deformation mechanisms and to the corresponding mechanical properties. Special emphasis is placed on the creep behavior of single crystal superalloys at high temperatures, where directional gamma coarsening is prominent, and at lower temperatures, where gamma coarsening rates are significantly reduced. It can be seen that very subtle changes in microstructural features can have profound effects on the subsequent properties of these materials.

Mackay, R. A.↗
DOE OSTI2022

Effect of nitriding on mechanical and microstructural properties of Direct Metal Laser Sintered 17-4PH stainless steel

In this work, the effect of the nitriding process on microstructure and mechanical properties of additively manufactured (AM) 17-4PH stainless steel is investigated. The nitriding was performed at 530 °C, 560 °C, and 580 °C for 2 h. The nitriding process improves the hardness and surface roughness of the AM 17-4PH steel. Detailed microstructural characterizations of both as-built and nitride samples are performed using an optical microscope, scanning electron microscope (SEM) equipped with energy-dispersive X-ray spectroscopy (EDS), and X-ray diffraction technique. It reveals that the nitride layer thickness increases with nitriding temperature. A distinct transition layer between the substrate and nitride layer is observed in the 560 °C and 580 °C nitride samples. The nitriding process develops almost equiaxed grain microstructure with new secondary phase precipitates, whereas in the as-built material, the grains are primarily columnar along the AM process build direction. Specifically, the nitriding process introduces γ-Fe4N, ε-Fe3N, CrN, and Ni3N precipitates. The increase in Ni- and Cu-rich precipitates with the nitriding temperature explains the observed improvement in the hardness and surface roughness. Furthermore, the nitriding process does not alter the substrate's initial weak crystallographic texture.

17-4PH steel↗
DOE OSTI2025

SOC Microstructural Property Estimator

This pre-trained ML model is a tool that uses basic compositional parameters for porous solid oxide cell (SOC) electrodes - the phase fractions and mean particle/pore diameters – as inputs and uses them to estimate additional electrochemical performance parameters: active (i.e., connected) TPB density, all tortuosity factors, and phase pair specific interfacial areas. The electrode is assumed to be composed of two solid phases and a pore phase. The property calculations are performed using neural network regression models trained on a large bank of synthetic electrode microstructural data that NETL has generated using the program DREAM3D (that bank is also hosted on EDX: https://edx.netl.doe.gov/dataset/soc-synthetic-microstructure-bank). This means the generated parameters are based on training from actual measured properties from 3D microstructures, not estimated from geometric simplifications. This tool was developed and is intended to replace percolation theory calculations in models that use hypothetical electrode properties. An example use case would be running SOC performance simulations across a parametric sweep of electrode designs (e.g., varying phase fractions and particle sizes) and assessing how it impacts the electrochemical performance of the SOC. Within the parameter space of the training data (statistics of that parameter space is provided in the readme file), this model achieves sub-5% mean absolute percent errors, an order of magnitude less error than percolation theory across the same parameter space. However, be aware that this tool was developed with parametric simulations in mind, and users are encouraged to assess accuracy for their own specific use case rather than taking accuracy metrics at face value. More info, including a usage guide, is in the included readme file. This tool should be cited with the DOI number provided.

Electrode Microstructure↗