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Results for “Processing and Properties”

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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1984

Saturn's rings - Properties and processes

In the present consideration of the structural and particle properties of Saturn's rings, emphasis is given to spacecraft observations and an attempt is made to relate observed properties to favored causative processes. While the ring particles are primarily icy, there is evidence for compositional variation on both local and regional scales. Ring structure is generally dominated by collisional and gravitational dynamics. On the basis of such features as orbital resonances with various satellites, which drive spiral density and bending waves, the ring mass density and local vertical thickness can be determined.

Cuzzi, J. N.↗
NASA NTRS2002

Morphologies, Processing and Properties of Ceramic Foams from Pre-Ceramic Foams from Pre-Ceramic Polymer Routes

The current research is focused on processing ceramic foams that have potential as a thermal protection material. Ceramic foams with different architectures were formed from the pyrolysis of pre-ceramic polymers at 1200 C in different atmospheres. In some systems a sacrificial polyurethane was used as the blowing agent. We have also processed foams using sacrificial fillers to introduce controlled cell sizes. Each sacrificial filler or blowing agent leads to a unique morphology. The effect of different fillers on foam morphologies will be presented. The presentation will also focus on characterization of these foams in terms of mechanical and thermal properties. Foams processed using these approaches having bulk densities ranging from 0.15 to 0.9 g per cubic centimeter and a cell sizes from 5 to 500 micrometers. Compression strengths ranged from 2 to 7 MPa for these materials.

Stackpoole, Mairead↗
NASA NTRS1977

Investigation of test methods, material properties and processes for solar cell encapsulants

The potentially useful encapsulating materials for Task 3 of the Low-Cost Silicon Solar Array project were studied to identify, evaluate, and recommend encapsulant materials and processes for the production of cost-effective, long-life solar cell modules. Materials for study were chosen on the basis of existing knowledge of generic chemical types having high resistance to environmental weathering. The materials varied from rubbers to thermoplastics and presented a broad range of mechanical properties and processing requirements. Basic physical and optical properties were measured on the polymers and were redetermined after exposure to indoor artificial accelerated aging conditions covering four time periods. Strengths and weaknesses of the various materials were revealed and data was accumulated for the development of predictive methodologies. To date, silicone rubbers, fluorocarbons, and acrylic polymers appear to have the most promising combination of characteristics. The fluorocarbons may be used only as films, however, because of their high cost.

Willis, P. B.↗
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↗
DOE OSTI2023

Process-structure-property relationships for droplet-on-demand liquid-metal-jetted parts

Liquid metal jetting droplet-on-demand additive manufacturing is an attractive alternative to industry-standard, laser-based metal additive manufacturing techniques that use powder feedstocks and produce large heat affected zones. Liquid metal jetting provides a low feedstock footprint, minimal materials waste, less contamination and inclusions from feedstock, and is compatible with a large range of materials. However, little has been reported on the liquid metal jetting process-structure-property relationships that are crucial to understanding how to consistently print high quality metal parts. We investigate the effects of build plate temperature, infill pattern, and track raster rate on the macro- and microstructure, density, hardness, and tensile strength of metal parts created with our custom droplet-on-demand setup using pure tin feedstock as a surrogate for more relevant structural materials. We found that although the build plate temperature had the largest influence on the structure and properties, the infill pattern is an important factor for optimal structural integrity. It was observed that the track raster rate influenced track-to-track porosity and should be considered in toolpath development. Despite printing in ambient air, we could not identify oxide on the surface or in the bulk of the parts and saw no adverse effects on microstructure or tensile performance. The results and methods of this study will help guide future work in optimizing the liquid metal jetting process to reliably print parts using any liquid metal jetting system for a wide variety of metal feedstocks.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Parameters, Properties, and Process: Conditional Neural Generation of Realistic SEM Imagery Toward ML-Assisted Advanced Manufacturing

Abstract The research and development cycle of advanced manufacturing processes traditionally requires a large investment of time and resources. Experiments can be expensive and are hence conducted on relatively small scales. This poses problems for typically data-hungry machine learning tools which could otherwise expedite the development cycle. We build upon prior work by applying conditional generative adversarial networks (GANs) to scanning electron microscope (SEM) imagery from an emerging advanced manufacturing process, shear-assisted processing and extrusion (ShAPE). We generate realistic images conditioned on temper and either experimental parameters or material properties. In doing so, we are able to integrate machine learning into the development cycle, by allowing a user to immediately visualize the microstructure that would arise from particular process parameters or properties. This work forms a technical backbone for a fundamentally new approach for understanding manufacturing processes in the absence of first-principle models. By characterizing microstructure from a topological perspective, we are able to evaluate our models’ ability to capture the breadth and diversity of experimental scanning electron microscope (SEM) samples. Our method is successful in capturing the visual and general microstructural features arising from the considered process, with analysis highlighting directions to further improve the topological realism of our synthetic imagery.

36 MATERIALS SCIENCE↗
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 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 OSTI2025

Establishing a process-structure-property-performance framework for SLS additive manufacturing through integrated multiscale modeling

This study presents a comprehensive suite of high-fidelity computational models that integrate multiscale and multiphysics simulations to capture the full Selective Laser Sintering (SLS) additive manufacturing process—from initial melting and solidification to mechanical response under external loads. Process simulations are linked with mechanical analysis through Representative Volume Elements (RVEs), establishing a process-structure–property-performance framework. The interaction between laser light and polyamide 12 (PA12) powder is modeled, accounting for laser characteristics and the optical, thermal, and geometrical properties of the powder. The heat source is incorporated into a heat transfer model, coupled with crystallization kinetics and densification models to predict material density and crystallinity. The porosity distribution from the densification model and crystallinity interpolated from experimental data are used to construct the RVEs. A multi-mechanism constitutive model is then calibrated using mechanical tests to predict the stress–strain response. Simulation results show good agreement with experimental data in terms of porosity, crystallinity, and mechanical performance when sufficient laser power (62 W or higher) is used. This research supports the inverse design of 3D-printed structures by introducing a high-fidelity framework that combines multiscale and multiphysics modeling with experimental calibration for predictive and performance-driven additive manufacturing.

SLS↗
DOE OSTI2021

Combining In-situ Diagnostics and Data Analytics for Discovery of Process-Structure-Property Relationships in AM parts – A Step Toward Digital Twins

In-situ additive manufacturing (AM) diagnostic tools (e.g., optical/infrared imaging, acoustic, etc.) already exist to correlate process anomalies to printed part defects. This current work aimed to augment existing capabilities by: 1) Incorporating in-situ imaging w/ machine learning (ML) image processing software (ORNL- developed "Peregrine") for AM process anomaly detection 2) Synchronizing multiple in-situ sensors for simultaneous analysis of AM build events 3) Correlating in-situ AM process data, generated part defects and part mechanical properties The key R&D question investigated was to determine if these new combined hardware/software tools could be used to successfully quantify defect distributions for parts build via SNL laser powder bed fusion (LPBF) machines, aiming to better understand data-driven process-structure-property- performance relationships. High resolution optical cameras and acoustic microphones were successfully integrated in two LPBF machines and linked to the Peregrine ML software. The software was successfully calibrated on both machines and used to image hundreds of layers of multiple builds to train the ML software in identifying printed part vs powder. The software's validation accuracy to identify this aspect increased from 56% to 98.8% over three builds. Lighting conditions inside the chamber were found to significantly impact ML algorithm predictions from in-situ sensors, so these were tailored to each machine's internal framework. Finally, 3D part reconstructions were successfully generated for a build from the compressed stack of layer-wise images. Resolution differences nearest and furthest from the optical camera were discussed. Future work aims to improve optical resolution, increase process anomalies identified, and integrate more sensor modalities.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Process-Microstructure-Property Relationships in Low Heat Input Wire-Arc Additive Manufacturing (WAAM) of Ni-Based Superalloy Haynes 282

Conference presentation on WAAM process optimization on Ni-based superalloy Haynes® 282®. This alloy is targeted for advanced power generation systems for its superior high temperature mechanical properties. Wire-arc additive manufacturing (WAAM) offers attractive cost and materials savings with high deposition rates. Based on initial build process optimization, WAAM blocks were made, and screened for defects using high-throughput CT scanning. The microstructural evolution in arc energy range of 250-700 J/mm was characterized in the as-built and aged conditions, using electron microscopy. Results showed self-consistent microstructure across conditions, with grain size dependence on arc energy, MC and M6C carbides in as-built, and blocky grain boundary M23C6 in as-heat-treated condition. Further reductions in as-built porosity were explored through 20+ combinations of H2 and CO2 additions to shielding gas. Initial results show improved wetting behavior, and bead overlap with up to 1% H2 and up to 0.25% CO2. Combination of experimentally determined datasets provided comprehensive understanding of process-structure-property relationships in WAAM Haynes 282.

Haynes 282↗
DOE OSTI2025

Text Mining for Process–Structure–Properties Relationships in Metals

With the advent of large language models (LLMs), the vast unstructured text within millions of academic papers is increasingly accessible for materials discovery—although significant challenges remain. While LLMs offer promising few- and zero-shot learning capabilities, particularly valuable in the materials domain where expert annotations are scarce, general-purpose LLMs often fail to address key materials-specific queries without further adaptation. To bridge this gap, fine-tuning LLMs on human-labeled data is essential for effective structured knowledge extraction (Liu in The Importance of Human-Labeled Data in the Era of LLMs, 2023). Here, in this study, we introduce a novel annotation schema designed to extract generic process–structure–properties relationships from scientific literature. We demonstrate the utility of this approach using a dataset of 128 abstracts, with annotations drawn from two distinct domains: high-temperature materials (Domain I) and uncertainty quantification in simulating materials microstructure (Domain II). Initially, we developed a conditional random field (CRF) model based on MatBERT—a domain-specific BERT variant—and evaluated its performance on Domain I. Subsequently, we compared this model with a fine-tuned LLM (GPT-4o from OpenAI) under identical conditions. Our results indicate that fine-tuning LLMs can significantly improve entity extraction performance over the BERT-CRF baseline on Domain I. However, when additional examples from Domain II were incorporated, the performance of the BERT-CRF model became comparable to that of the GPT-4o model. These findings underscore the potential of our schema for structured knowledge extraction and highlight the complementary strengths of both modeling approaches.

Materials science↗
DOE OSTI2020

Fuel Property-Informed Process Design for the Direct Catalytic Conversion of Cellulosics

The direct catalytic conversion of cellulosics (DC3) with supercritical methanol presents a promising pathway to completely solubilize woody biomass and produce >60% mass yield of C2-C6 oxygenates for light duty vehicle applications. Although the oxygenate composition can be tuned by modifying catalyst and process conditions, limited efforts to date have examined DC3 fuel properties for light duty fuel applications to iteratively inform conversion process design. In this work, we designed and evaluated multi-component surrogate bioblendstocks that represent major DC3 light oxygenates and refined catalyst formulations and distillation parameters to improve the resulting light duty fuel quality. Surrogate design for this novel fuel production pathway was based on a preliminary product slate that was systematically adjusted to determine impacts on fuel properties of interest including heating value, oxidative stability, and heat of vaporization, among others. The resulting predictions and measurements of these surrogates were then used to inform improvement of conversion process, and specifically, reformulation of the catalyst which resulted in production of a higher energy density fuel product primarily comprised of saturated alcohols. Fuel testing outcomes also informed downstream oxygenate separations requirements. This resulted in the production of a promising DC3 light-duty fuel blendstock with >15% greater energy density relative to ethanol, while maintaining high octane number and octane sensitivity.

BIOMASS FUELS↗
DOE OSTI2025

PROCESS-STRUCTURE-PROPERTY RELATIONSHIPS IN LASER POWDER BED FUSION PRODUCED 17-4 PH STEEL

Laser powder bed fusion (LPBF) is a metal additive manufacturing method that produces non-traditional microstructures as a result of the rapid solidification and thermal cycling inherent to the process. When using LPBF-produced material in application, these unique microstructures challenge the applicability of well developed mechanical property databases achieved by conventional heat treatments. For wider adoption of this technology, a more holistic understanding is necessary on how process attributes develop material structure, which dictate mechanical properties. This dissertation explores the process– structure–property relationships in LPBF 17-4 PH steel through systematic evaluation of atmospheric processing and heat treatment effects on microstructure and mechanical performance. Specimens were fabricated under controlled build environments, subjected to a range of solutionizing, homogenizing, and aging treatments, and characterized using optical microscopy, electron back scatter diffraction (EBSD), and X-ray diffraction (XRD) to quantify phase evolution. Tensile testing was performed to directly link heat treatment pathway and nitrogen absorption to mechanical performance. This work demonstrates where conventional heat treatment standards are applicable to LPBF 17-4 PH steel and where modifications are required. By directly correlating phase stability, nitrogen effects, and tensile response, this work provides practical guidelines for tailoring post-processing strategies. These findings underscore that successful application of LPBF 17-4 PH steel requires explicit consideration of both build environment and post-processing. By linking processing conditions to microstructure and performance, this work advances understanding of critical variables that govern reliability of additively manufactured precipitation-hardened stainless steels in demanding applications.

Brown, Benjamin [Kansas City National Security Cam↗
NASA NTRS2001

Processing and Properties Of Refractory Zirconium Diboride Composites For Use In High Temperature Applications

The Thermal Protection Materials and Systems Branch at NASA Ames Research Center is involved in the development of a class of refractory oxidation-resistant diboride composites termed Ultra High Temperature Ceramics or UHTCs. These composites have good high temperature properties making them candidate materials for thermal protection system (TPS) applications. The current research focuses on improving processing methods to develop more reliable composites with enhanced thermal and mechanical properties. This presentation will concentrate on the processing of ZrB2/SiC composites. Some preliminary mechanical properties and oxidation data will also be presented.

Stackpoole, Margaret↗