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Results for “Material microstructure”

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
NASA NTRS1986

Transfer function concept for ultrasonic characterization of material microstructures

The approach given depends on treating material microstructures as elastomechanical filters that have analytically definable transfer functions. These transfer functions can be defined in terms of the frequency dependence of the ultrasonic attenuation coefficient. The transfer function concept provides a basis for synthesizing expressions that characterize polycrystalline materials relative to microstructural factors such as mean grain size, grain-size distribution functions, and grain boundary energy transmission. Although the approach is nonrigorous, it leads to a rational basis for combining the previously mentioned diverse and fragmented equations for ultrasonic attenuation coefficients.

Vary, A.↗
DOE OSTI2023

Tailoring material microstructure and property in wire-laser directed energy deposition through a wiggle deposition strategy

Developing effective strategies to directly control material microstructure, property, and anisotropy is an active research area in metal additive manufacturing. This work develops a wiggle deposition pattern for wire-laser directed energy deposition (DED) of 316L stainless steel (SS) to modify the solidification texture, particularly in the building direction, in as-deposited samples. Through multi-physics simulation, operando near-infrared imaging, and synchrotron x-ray characterization, it is found that the wiggle deposition strategy induces highly dynamic melt flow and oscillating thermal gradient in the melt pool, which is responsible for the variation of preferable grain growth direction and crystallographic texture in the sample. The specific texture reduces the anisotropy in the tensile strength of as-printed 316L SS samples cut along different directions. Also, it largely increases the ductility along the build direction. Crystal plasticity simulation is performed to correlate the sample texture with mechanical property. In conclusion, this work offers a unique approach for tailoring local properties through the control of melt pool instability by applying different tool paths.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Discovering mechanisms for materials microstructure optimization via reinforcement learning of a generative model

Abstract The design of materials structure for optimizing functional properties and potentially, the discovery of novel behaviors is a keystone problem in materials science. In many cases microstructural models underpinning materials functionality are available and well understood. However, optimization of average properties via microstructural engineering often leads to combinatorically intractable problems. Here, we explore the use of the reinforcement learning (RL) for microstructure optimization targeting the discovery of the physical mechanisms behind enhanced functionalities. We illustrate that RL can provide insights into the mechanisms driving properties of interest in a 2D discrete Landau ferroelectrics simulator. Intriguingly, we find that non-trivial phenomena emerge if the rewards are assigned to favor physically impossible tasks, which we illustrate through rewarding RL agents to rotate polarization vectors to energetically unfavorable positions. We further find that strategies to induce polarization curl can be non-intuitive, based on analysis of learned agent policies. This study suggests that RL is a promising machine learning method for material design optimization tasks, and for better understanding the dynamics of microstructural simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design

Abstract There are two broad modeling paradigms in scientific applications: forward and inverse. While forward modeling estimates the observations based on known causes, inverse modeling attempts to infer the causes given the observations. Inverse problems are usually more critical as well as difficult in scientific applications as they seek to explore the causes that cannot be directly observed. Inverse problems are used extensively in various scientific fields, such as geophysics, health care and materials science. Exploring the relationships from properties to microstructures is one of the inverse problems in material science. It is challenging to solve the microstructure discovery inverse problem, because it usually needs to learn a one-to-many nonlinear mapping. Given a target property, there are multiple different microstructures that exhibit the target property, and their discovery also requires significant computing time. Further, microstructure discovery becomes even more difficult because the dimension of properties (input) is much lower than that of microstructures (output). In this work, we propose a framework consisting of generative adversarial networks and mixture density networks for inverse modeling of structure–property linkages in materials, i.e., microstructure discovery for a given property. The results demonstrate that compared to baseline methods, the proposed framework can overcome the above-mentioned challenges and discover multiple promising solutions in an efficient manner.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Revealing the evolution of order in materials microstructures using multi-modal computer vision

The development of high-performance materials for microelectronics, energy storage, and extreme environments depends on our ability to describe and direct property-defining microstructural order. Our present understanding is typically derived from laborious manual analysis of imaging and spectroscopy data, which is difficult to scale, challenging to reproduce, and lacks the ability to reveal latent associations needed for mechanistic models. Here, we demonstrate a multi-modal machine learning (ML) approach to describe order from electron microscopy analysis of the complex oxide La 1−x Sr x FeO 3 . We construct a hybrid pipeline based on fully and semi-supervised classification, allowing us to evaluate both the characteristics of each data modality and the value each modality adds to the ensemble. We observe distinct differences in the performance of uni- and multi-modal models, from which we draw general lessons in describing crystal order using computer vision.

36 MATERIALS SCIENCE↗
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↗
NASA NTRS1982

Interrelation of material microstructure, ultrasonic factors, and fracture toughness of two phase titanium alloy

The pivotal role of an alpha-beta phase microstructure in governing fracture toughness in a titanium alloy, Ti-662, is demonstrated. The interrelation of microstructure and fracture toughness is demonstrated using ultrasonic measurement techniques originally developed for nondestructive evaluation and material property characterization. It is shown that the findings determined from ultrasonic measurements agree with conclusions based on metallurgical, metallographic, and fractographic observations concerning the importance of alpha-beta morphology in controlling fracture toughness in two phase titanium alloys.

Vary, A.↗
NASA NTRS1992

Composite materials microstructure for radiation shielding

Shielding against radiation is a concern for applications on earth, in space, and on extraterrestrial surfaces. On earth EMI is an important factor, while in space and on extraterrestrial surfaces particle (high charge-Z and high energy-E) radiation is a critical issue. Conventional metallic materials currently used for EMI shielding incur large weight penalties. To overcome this weight penalty, ultra-lightweight composite materials utilizing fillers ranging from carbon microballoons to silver coated ceramic microballoons are proposed. The crucial shielding requirement is conductivity of the constituent materials, while the hollow microballoon geometry is utilized to yield low weight. Methods of processing and composition effects are examined and these results are compared to the effectiveness of varying the conductive microballoon material. The resulting ultralightweight materials, developed for EMI shielding, can be tailored through the application of the understanding of the relative effects of variables such as those tested. Initial experimental results reveal that these tailored ultralightweight composite materials are superior to traditional aluminum shielding at only a small fraction of the weight.

Radford, Donald W.↗
NASA NTRS1986

Scaling attenuation data characterizes changes in material microstructure

Ultrasonic attenuation was measured for polycrystalline samples of nickel and copper with various grain-size distributions produced by heat treatment. Attenuation as a function of frequency was determined for a sample having a known mean grain diameter D. It was found that, once this function is determined, it can be scaled to determine the mean grain size of other samples of the same material with different mean grain diameters. These results were obtained using broadband pulse-echo ultrasound in the 25 to 100 MHz frequency range. The results suggest an ultrasonic, nondestructive approach for verifying heat treatment of metals.

Generazio, E. R.↗
DOE OSTI2025

Development of a scalable, robust electrocatalytic technology for conversion of CO 2 to formic acid via microstructured materials

This project was funded by the Bioenergy Engineering for Product Synthesis (BEEPS) program under the Funding Opportunity Announcement (FOA) DE-FOA-0001916 Topic Area 5 “Rewiring Carbon Utilization”. This FOA sought projects that would electrocatalytically reduce CO 2 to a carbon intermediate and then upconvert to a multi-carbon product or fuel via non-photosynthetic biological system engineering. The project sought to combine the expertise of OCO Chem, whose chief scientist had previously developed an efficient electrocatalytic reactor for conversion of CO 2 to formate with potential to scale, Montana State University investigators who had recently patented a method for laterally grading membranes with the potential of improving reactant distribution and more uniform efficiency across membrane-based reactors, and University of South Florida which had recently discovered a novel C-C bond forming reaction not known to naturally occur in biological systems and which uses C1 substrates and circumvents the central microbial metabolism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Simultaneous Development and Robust Optimization of a Microstructure Dependent Material

Recent microstructure characterization techniques combined with Symbolic Regression(SR)analysis has been proven to generate white box plasticity models well suited for incorporation into FEA software.The current work builds upon those efforts and demonstrates the applicability of Sequential Monte-Carlo (SMC) methods within SR analysis to condense model development and robust optimization into a single, co-dependent process. In this project, SMC methods provide a mechanism through which the observed microstructure features and associated variability can be incorporated into the discovery phase of model development and simultaneously recover approximate parameter distributions through SR analysis. The demonstration utilized a data set consisting of tensile test results from a limited number of sample specimens with corresponding EBSD data from which microstructure features were characterized.The maximum threshold stress model in the Visco-Plastic Self-Consistent (VPSC) code developed by Los Alamos National Laboratories was calibrated using mechanical test data.Synthetic volume elements with statistically equivalent microstructure were generated with DREAM3Dbased on the observed EBSD data. VPSC was used to simulate the corresponding tensile test response for each of the synthetic volume elements. The simulated microstructure and tensile test data was used astraining datafor SMC-SR algorithm and the resulting model was validated with data from the original empirical data set.

Karl Garbrecht↗
DOE OSTI2023

Towards inverse microstructure-centered materials design using generative phase-field modeling and deep variational autoencoders

The field of Integrated Computational Materials Engineering (ICME) combines a broad range of methods to study materials’ responses over a spectrum of length scales. A relatively unexplored aspect of microstructure-sensitive materials design is uncertainty propagation and quantification (UP/UQ) of materials’ microstructure, as well as establishing process-structure–property (PSP) relationships for inverse material design. In this study, an efficient UP technique built on the idea of changing probability measures and a deep generative unsupervised representative machine learning method for microstructure-based design of thermal conductivity of materials is proposed. Probability measures are used to represent microstructure space, and Wasserstein metrics are used to test the efficiency of the UP method. By using deep Variational AutoEncoder (VAE), we identify the correlations between the material/process parameters and the thermal conductivity of heterogeneous dual-phase microstructures. Through high-throughput screening, UP, and the deep-generative VAE method, PSP relationships that are too complex can be revealed by exploiting the materials’ design space with an emphasis on microstructures. As a last point, we demonstrate generative machine learning serves as a useful tool for inverse microstructure-centered materials design, and we demonstrate this by examining the inverse design of thermal conductivity in nano-structured materials. Here, the results reveal the effects of morphology, volume fraction, characteristic length scale, and the individual thermal diffusivity of phases on the thermal conductivity of dual-phase alloys. Our findings emphasize the advantages of high-throughput phase-field modeling and generative deep learning for linking PSP and inverse microstructure-centered materials design.

36 MATERIALS SCIENCE↗
NASA NTRS2016

Microstructural and Material Quality Effects on Rolling Contact Fatigue of Highly Elastic Intermetallic Ball Bearings

Rolling element bearings made from highly-elastic intermetallic materials (HIM)s, such as 60NiTi, are under development for applications that require superior corrosion and shock resistance. Compared to steel, intermetallics have been shown to have much lower rolling contact fatigue (RCF) stress capability in simplified 3-ball on rod (ASTM STP 771) fatigue tests. In the 3-ball tests, poor material quality and microstructural flaws negatively affect fatigue life but such relationships have not been established for full-scale 60NiTi bearings. In this paper, 3-ball-on-rod fatigue behavior of two quality grades of 60NiTi are compared to the fatigue life of full-scale 50mm bore ball bearings made from the same materials. 60NiTi RCF rods with material or microstructural flaws suffered from infant mortality failures at all tested stress levels while high quality 60NiTi rods exhibited no failures at lower stress levels. Similarly, tests of full-scale bearings made from flawed materials exhibited early surface fatigue and through crack type failures while bearings made from high quality material did not fail even in long-term tests. Though the full-scale bearing test data is yet preliminary, the results suggest that the simplified RCF test is a good qualitative predictor of bearing performance. These results provide guidance for materials development and to establish minimum quality levels required for successful bearing operation and life.

tribology↗
NASA NTRS2017

Microstructural and Material Quality Effects on Rolling Contact Fatigue of Highly Elastic Intermetallic NiTi Ball Bearings

Rolling element bearings made from highly-elastic intermetallic materials (HIM)s, such as 60NiTi, are under development for applications that require superior corrosion and shock resistance. Compared to steel, intermetallics have been shown to have much lower rolling contact fatigue (RCF) stress capability in simplified 3-ball on rod (ASTM STP 771) fatigue tests. In the 3-ball tests, poor material quality and microstructural flaws negatively affect fatigue life but such relationships have not been established for full-scale 60NiTi bearings. In this paper, 3-ball-on-rod fatigue behavior of two quality grades of 60NiTi are compared to the fatigue life of full-scale 50mm bore ball bearings made from the same materials. 60NiTi RCF rods with material or microstructural flaws suffered from infant mortality failures at all tested stress levels while high quality 60NiTi rods exhibited no failures at lower stress levels. Similarly, tests of full-scale bearings made from flawed materials exhibited early surface fatigue and through crack type failures while bearings made from high quality material did not fail even in long-term tests. Though the full-scale bearing test data is yet preliminary, the results suggest that the simplified RCF test is a good qualitative predictor of bearing performance. These results provide guidance for materials development and to establish minimum quality levels required for successful bearing operation and life.

metals↗
DOE OSTI2024

Microstructural and material property changes in severely deformed Eurofer-97

Severe plastic deformation changes the microstructure and properties of steels, which may be favourable for their use in structural components of nuclear reactors. In this study, high-pressure torsion (HPT) was used to refine the grain structure of Eurofer-97, a ferritic/martensitic steel. Electron microscopy and X-ray diffraction were used to characterise the microstructural changes. Following HPT at room temperature to a maximum shear strain of 230, the average grain size reduced by a factor of ~30, with a marked increase in high-angle grain boundaries. Dislocation density also increased by more than one order of magnitude. The thermal stability of the deformed material was investigated via in-situ annealing during synchrotron X-ray diffraction. This revealed substantial recovery between 450 K – 800 K. Irradiation with 20 MeV Fe-ions to ~0.1 dpa caused a 20% reduction in dislocation density compared to the as-deformed material. However, HPT deformation prior to irradiation only had a minor effect in mitigating the irradiation-induced reductions in thermal diffusivity and surface acoustic wave velocity of the material. Microstructural and material property changes are dominated by deformation compared to irradiation. In light of this, the benefits of using HPT to improve the irradiation resistance of Eurofer-97 are limited. These results provide a multi-faceted view of the changes in ferritic/martensitic steels due to severe plastic deformation, and how these changes can be used to alter material properties.

ion-irradiation↗