Algorithm to Generate Synthetic 3D Microstructures from 2D Exemplars
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Post Guillen, Donna↗
SEARCH · Engineering Papers
Results for “microstructure generation”
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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Stress localization investigation of additively manufactured GRCop-42 thin-wall structure
A full-field crystal plasticity (CP) framework is presented for the GRCop-42 alloy to study microscopic mechanical behavior and local stress heterogeneities. The microstructures of additively manufactured (AM) materials are often unique relative to conventionally processed materials, and the local thermal histories drive these differences during the build process. These thermal histories depend on the process parameters (laser power, scan speed, and scan strategy) and the part geometry. Prior research has shown that the mechanical properties of thin-walled structures can vary significantly with wall thickness due to changes in the thermal boundary conditions during manufacturing. It is, therefore, desirable to perform CP simulations based on the phenomenological constitutive model to predict the local mechanical responses induced by microstructural heterogeneities. This work generates representative microstructures based on experimentally collected grain information (i.e., texture) for grain scale stress analysis, and the material constitutive parameters are calibrated using the experimental mechanical testing data. Here, we specifically investigated the effect of crystallographic texture and grain morphologies on the size-dependent mechanical properties of AM GRCop-42. The selection of appropriate material properties for implementing an effective free surface boundary condition and the influence of adjacent buffer layers are also discussed. Analysis of local field results reveals a strong correlation between stress localization and the initial grain orientation. However, no significant relationship between the misorientation of the individual adjacent grains and the average misorientation is observed.
36 MATERIALS SCIENCE↗
A crystal plasticity finite element method modeling of Zircaloy with hydride phases based on scanning electron micrographs
This work developed a microstructure-based finite element model to predict the stress state of alloys with second phase inclusions. Quantitative microstructural details extracted from scanning electron microscopy (SEM) images were used to generate heterogeneous microstructures including the morphology and spatial distribution of hydrides. Generation of digital microstructure was achieved through two steps of tessellations using software Neper and Matlab. The process is demonstrated using an example of Zircaloy material with secondary phases of hydrides dispersed within and stress-strain response of Zircaloy containing hydrides was predicted. The constitutive material model for Zircaloy in this study was based on crystal plasticity theory which considers the hexagonal close-packed (HCP) atomic structure of Zircaloy material. The hydrides were modeled as isotropic elasto-plastic material. A parametric study had been conducted to understand the effect of volume fraction, and lamellae thickness of the hydride phase on the mechanical properties of the overall material. Results can help designers to alter the manufacturing process to obtain the enhanced mechanical properties for components used in nuclear applications made by Zircaloy material.
Kulkarni, Shank S.↗
Zwitterionic Photocurable Resin for High‐Resolution 3D Printing of Ultralow‐Fouling Microstructures
High‐resolution 3D printing technologies are enabling a new generation of microstructured materials for applications where biocompatibility is critical. However, most conventional 3D‐printable resins yield materials that exhibit trade‐offs between antifouling properties and mechanical robustness, limiting their applicability in living systems. In nature, zwitterionic surface groups form tightly bound hydration layers that act as effective barriers against protein and cell attachment. Inspired by this strategy, a zwitterionic acrylamide‐based photoresist—carboxybetaine di‐methacrylamide (CBDA)—is developed for projection‐based vat photopolymerization, enabling the fabrication of complex microarchitectures with exceptional antifouling properties. The bifunctional monomer allows the formation of dense, cross‐linked networks that resist swelling while maintaining a high density of zwitterionic groups. Printed structures exhibit strong resistance to protein and cell adhesion, as confirmed by porcine blood assays, alongside robust mechanical performance. As a demonstration, a tubular structure featuring a negative Poisson's ratio lattice is printed to showcase structural fidelity and versatility. This resin formulation offers a broadly applicable strategy for fabricating microscale devices and surfaces where antifouling performance and structural integrity are both essential—spanning biomedical interfaces, soft robotics, and beyond.
3D printing↗
Exascale granular microstructure reconstruction in 3D volumes of arbitrary geometries with generative learning
Reconstructing 3D granular microstructures within volumes of arbitrary geometries from limited 2D image data is crucial for predicting the material properties, as well as performances of structural components accounting for material microstructural effects. We present a novel generative learning framework that enables exascale reconstruction of granular microstructures within complex 3D geometric volumes. Building upon existing transfer learning techniques using pre-trained convolutional neural networks (CNN), we introduce several key innovations to overcome the difficulties inherent in arbitrary geometries. Our framework incorporates periodic boundary conditions using circular padding techniques, ensuring continuity and representativeness of the reconstructed microstructures. We also introduce a novel seamless transition reconstruction (STR) method that creates statistically equivalent transition zones to integrate multiple pre-existing 3D microstructure volumes. Based on STR, we propose a cost-effective strategy for reconstructing microstructures within complex geometric volumes, minimizing computational waste. Validation through numerical experiments using kinetic Monte Carlo simulations demonstrates accurate reproduction of grain statistics, including grain size distributions and morphology. A case study involving the reconstruction of a 4-blade propeller microstructure illustrates the method’s capability to efficiently handle complex geometries. In conclusion, the proposed framework significantly reduces computational demands while maintaining high reconstruction quality, paving the way for scalable microstructure reconstruction in materials design and analysis.
36 MATERIALS SCIENCE↗
Generating Realistic Synthetic Polycrystalline Microstructures
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Buzzy, Michael↗
Tailoring composition and deformation modes at the microstructural level for next generation low-cost high-strength austenitic stainless steels
The objective of this project is to enable deliberate development of cost-effective, hydrogen resistant alloys by establishing detailed relationships specific to the effects of alloy composition, short-range order (SRO), and microsegregation in the presence of hydrogen on the transition between homogeneous deformation and localized plasticity in shear bands. In collaboration with the International Institute for Carbon-Neutral Energy Research, I2CNER, at Kyushu University in Japan, we conceptualized, designed, and manufactured four austenitic alloys that maintain corrosion resistance and ensure lower cost relative to baseline commercial alloys. The mechanical properties and deformation modes of the novel alloys (KU alloys) were assessed in the presence of hydrogen (H). Correlations between composition and performance revealed that two of the KU alloys are suitable replacements for 316 steel, while another is a viable replacement for 304 steel at room temperature. We found that, in the presence of other austenite stabilizing elements namely Mn and N, replacing Ni with Cu does not lead to martensite formation as has been previously reported.1–3 Furthermore, we found that the addition of Cu leads to an earlier onset of multiple slip resulting in an relative earlier onset of a higher work hardening rate (WHR). Greater understanding of the relationships between alloy composition and SRO required the development of a novel advanced electron diffraction methodology to characterize SRO in complex FCC alloys. This innovative approach, which combines fluctuation and correlation analyses of diffuse-scattering signals, successfully differentiated between SRO and long-range ordering (LRO). Further investigations into annealed austenitic stainless steels could provide insights into manipulating SRO and its effects on material properties. Atomistic simulations provided understanding of SRO behavior that was difficult to capture experimentally. This project created the first spin cluster expansion model that is able to capture and describe SRO effects in Fe-Ni-Cr FCC alloys, accounting for the non-negligible effects of magnetism. An automated computational workflow was established to provide reliable predictions of SRO in Fe-Ni-Cr austenitic alloys, both with and without the presence of H atoms. Analysis of the propensity for SRO in Fe-Ni-Cr alloys revealed that H tends to cluster with specific, well-defined SRO domains. The computational framework is general purpose and can be extended to realistic stainless steels across diverse composition ranges. With confidence that SRO is possible in austenitic stainless steels, we developed a discrete dislocation finite element code to understand the interaction of dislocations with SRO in the presence of H. By incorporating H effects on the dislocation emission and SRO stress field we show that the critical stress for the dislocation pileup to breakthrough the SRO domain decreases in the presence of H, which directly contributes localized deformation at the macroscale. Through the simulation of a uniaxial tension test, we demonstrated that H-induced weakening of SRO stress field and H-enhanced dislocation emission can lead to the onset of shear localization at lower macroscopic strains. As a whole, this project identified three novel alloys that show improvements in performance and cost efficiency for H-facing applications by studying correlations between alloy chemistry and deformation behavior. We also made significant advancements to experimental and computational methodologies necessary to study the chemistry and distribution of SRO across a range of alloys, which in turn allowed us to demonstrate how deformation mechanisms change due to the contributions of SRO in austenitic alloys in the presence of H. The combined advancements in fundamental understanding with novel alloy development in this project has increased the viability of next generation H-technologies for the broader public through accessible low-cost alloys and accelerated development towards future H-infrastructure.
08 HYDROGEN↗
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↗
Simulated Microstructures for Laser Powder Bed Fusion Additive Manufacturing Using Myna, AdditiveFOAM, and ExaCA
This dataset provides sample datasets containing voxelized, three-dimensional representations of simulated grain structures and crystallographic orientations that can result from laser powder bed fusion additive manufacturing. The six microstructure files each contain approximately 1 cubic millimeter of material (1 mm x 1 mm cross-section over 26 simulated layers of deposition). Some of the microstructures have columnar grains that extend across nearly the entire simulation domain, while others have more equiaxed or truncated columnar grains. The process conditions to generate these microstructures were from the Peregrine v2023-10 dataset (10.13139/ORNLNCCS/2008021). The codes used are publicly available and released under open-source licenses. Myna (https://github.com/ORNL-MDF/Myna) was used for configuration of the cases from the Peregrine v2023-10 HDF5 dataset and to run the simulation workflow. AdditiveFOAM (https://github.com/ORNL/AdditiveFOAM) was used to simulate the melt pool and generate solidification conditions. And ExaCA (https://github.com/LLNL/ExaCA ) was used to simulate the three-dimensional microstructures.
36 MATERIALS SCIENCE↗
A computational study of the effects of graphene additions on electrical properties of polycrystalline copper
The addition of graphene has recently shown promise as a route for the significant improvement of the bulk electrical properties of metallic materials. Here, we explore the effects these additions have on the net electrical conductivity of fabricated copper-graphene (Cu-Gr) nanocomposites as a function of grain structure and grain boundary properties. Synthetic 3D microstructures were generated to represent polycrystalline copper with different average grain diameters and twinned grain boundary fractions. Then, the Poisson equation of electrical transport was solved using a finite difference method in order to predict the net electrical conductivity of each microstructure. In this context, the potential effect of graphene on the conductivity of the composite was evaluated as a function of the number of affected grain boundaries. The results of these calculations indicate that 1.) as supported by literature, net electrical conductivity decreases with decreasing grain size, 2.) the presence of twinned grain boundaries results in smaller loss of conductivity than would otherwise be expected, and 3.) the presence of graphene on the grain boundaries can be expected to lead to improvements in net electrical conductivity. However, we also find that 4.) when the Cu grain structure becomes sufficiently refined, the addition of graphene could conceivably result in significant improvements in electrical conductivity over and above coarse-grained Cu. It is estimated from our calculations that, assuming microstructures with average grain sizes between 100 nm and 100 μm and graphene conductivity 1000 to 10,000 that of a typical Cu grain boundary, an improvement in electrical conductivity of approximately 17% over that of bulk Cu may be attainable. Therefore, by performing this study we suggest a possible route for the improvement of Cu electrical properties through the addition of graphene.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Mesoscale Modeling Approach for Quantifying Microstructure-Aware Micromechanical Responses in Metal Hydrides
Metal hydrides can undergo significant volume changes upon hydrogen uptake and release, which induce a mechanical response that depends not only on the evolving hydrogen composition but also on the microstructure. We present a comprehensive mesoscale modeling framework based on microelasticity theory to quantify the micromechanical responses of metal hydrides, specifically focusing on a hydrogenating polycrystalline MgH 2x particle within a host material as a model micromechanical system. Utilizing digitally generated realistic microstructures and density-functional-theory-derived parameters, we analyzed highly nonuniform local stress profiles in the polycrystalline hydrides under the clamping force exerted by the host during hydrogenation. Our framework also allows us to predict the corresponding strain energy accumulation and mechanical hot spots formation in the hydrides, highlighting their roles in thermodynamic destabilization and mechanical failure, respectively. Through extensive parametric simulations, we further quantified the influence of interface type, crystallinity, grain size, loading ratio, and host stiffness, providing practical guidance for optimizing microstructural design and host material selection. This proposed approach is broadly applicable to micromechanical systems with complex microstructural features involving chemical reaction- and/or phase-transformation-induced deformation.
36 MATERIALS SCIENCE↗
Effect of vanadium on phase composition and hard magnetic properties of as-solidified and heat-treated Sm–Fe–(Ti,V) alloys
Although the intrinsic magnetic properties of Ti-stabilized Sm(Fe,Co,Ti) 12 compounds exhibit potential of excellent rare-earth-lean permanent magnets, it has been much easier to realize large coercivities with the isostructural compounds stabilized by either V or by certain combinations of Ti and V. To elucidate the influence of V on the microstructure and magnetic properties, a series of Sm 8.1 Fe 78.4 (Ti 1-x V x ) 13.5 alloys was studied after arc-melting and annealing at 850–1000 °C. The alloys were found to fall into three groups. For x ≤ 0.2, solidification generates mostly the Sm(Fe,Ti,V) 12 phase, but annealing converts at least part of it into the non-magnetic Sm(Fe,Ti,V) 11 and the magnetically soft Sm 2 (Fe,Ti,V) 17 phases. For 0.2 < x < 0.6, the alloys solidify into a near-equilibrium mixture of the Sm(Fe,Ti,V) 12 , TiFe 2 and Sm-rich phases. For x ≥ 0.6, solidification generates large fractions of α-Fe solid solution and Sm-rich phases; an annealing step is necessary to complete the formation of Sm(Fe,Ti,V) 12 phase. Also, for x ≥ 0.6 the temperature below which the Sm(Fe,Ti,V) 12 phase is stable decreases with x, as does the fraction of this phase formed during solidification. Here, the differences between these three groups of alloys suggest different strategies for developing hard magnetic properties, with the likelihood of a success increasing with increasing x. For x ≥ 0.6, heat treatment alone is demonstrated to generate a microstructure of micron and submicron Sm(Fe,Ti 1-x V x ) 12 crystallites separated by a Sm-rich phase and exhibiting a coercivity with values up to 3.5 and 5.7 kOe for x = 0.8 and 1.0, respectively.
36 MATERIALS SCIENCE↗
Gradient nanostructuring via compositional means
Nanocrystalline metals are inherently unstable against thermal and mechanical stimuli, commonly resulting in significant grain growth. Also, while these metals exhibit substantial Hall-Petch strengthening, they tend to suffer from low ductility and fracture toughness. With regard to the grain growth problem, alloying elements have been employed to stabilize the microstructure through kinetic and/or thermodynamic mechanisms. And to address the ductility challenge, spatially-graded grain size distributions have been developed to facilitate heterogeneous deformation modes: high-strength at the surface and plastic deformation in the bulk. In the present work, we combine these two strategies and present a new methodology for the fabrication of gradient nanostructured metals via compositional means. Here, we have demonstrated that annealing a compositionally stepwise Pt-Au film with a homogenous microstructure results in a film with a spatial microstructural gradient, exhibiting grains which can be twice as wide in the bulk compared to the outer surfaces. Additionally, phase-field modeling was employed for the comparison with experimental results and for further investigation of the competing mechanisms of Au diffusion and thermally induced grain growth. This fabrication method offers an alternative approach for developing the next generation of microstructurally stable gradient nanostructured films.
36 MATERIALS SCIENCE↗
Dependent scattering and fractal microstructure determine the transparency of aerogel monoliths
This study reveals how dependent scattering and microstructure significantly affect electromagnetic wave propagation through aerogel monoliths, contributing to their transparency. Light scattering by particle ensembles is considered “dependent” when the scattering properties rely not only on particle size and optical constants but also on their spatial distribution, typically occurring when the average interparticle distance is small in comparison with the wavelength of incident radiation. Addressing dependent scattering requires solving Maxwell’s equations for complex heterogeneous structures, which is computationally demanding and usually limited to sample thicknesses on the same scale as the wavelength. This study combines computer-generated ambigel microstructures of fractal aggregates of polydisperse nanoparticles and the radiative transfer with reciprocal transaction method to predict the transmittance of thick ambigel slabs. Transmittance measurements of ambiently dried aerogel monoliths (ambigels) with porosities from about 50% to 90% closely matched the predicted values for their digital twins. However, ignoring dependent scattering or particle aggregation led to inaccurate predictions. This study validated the computational framework, and its findings offer insights for designing photonic metamaterials and analyzing their interactions with electromagnetic waves.
Yalcin, Refet A. (ORCID:0000000339973494)↗
Radiation effects on materials for electrochemical energy storage systems
Batteries and electrochemical capacitors (ECs) are of critical importance for applications such as electric vehicles, electric grids, and mobile devices. However, the performance of existing battery and EC technologies falls short of meeting the requirements of high energy/high power and long durability for increasing markets such as the automotive industry, aerospace, and grid-storage utilizing renewable energies. Therefore, improving energy storage materials performance metrics is imperative. In the past two decades, radiation has emerged as a new means to modify functionalities in energy storage materials. There exists a common misconception that radiation with energetic ions and electrons will always cause radiation damage to target materials, which might potentially prevent its applications in electrochemical energy storage systems. But in this review, we summarize recent progress in radiation effects on materials for electrochemical energy storage systems to show that radiation can have both beneficial and detrimental effects on various types of energy materials. Prior work suggests that fundamental understanding toward the energy loss mechanisms that govern the resulting microstructure, defect generation, interfacial properties, mechanical properties, and eventual electrochemical properties is critical. Here, we discuss radiation effects in the following categories: (1) defect engineering, (2) interface engineering, (3) radiation-induced degradation, and (4) radiation-assisted synthesis. We analyze the significant trends and provide our perspectives and outlook on current research and future directions in research seeking to harness radiation as a method for enhancing the synthesis and performance of battery materials.
25 ENERGY STORAGE↗
Damage Accumulations Predictions for Boiler Components Via Microstructurally Informed Material Models
The goal of the project was to model material behavior and degradation during cyclic plasticity— with and without hold time—for nickel-based superalloys used in USC (ultra-super-critical) and A-USC (advanced-ultra-super-critical) boiler components. The study provided physically informed models, capturing the microstructural changes taking place in the industrial components under cyclic loading and long duration stress (up to 300,000 hours) and high temperature exposure (1100°F/593°C to 1400°F/760°C). The major developments were: 1) Qualitative and quantitative understanding of microstructure evolution (gamma prime precipitates), deformation (dislocation density), and damage mechanisms of Haynes ® 282 alloy. 2) Qualitative understanding of microstructural features generating local strain variations. 3) A continuum damage mechanics model (CDM) for Haynes ® 282 alloy at 1100°F to 1400°F capturing cyclic behavior with and without hold time. 4) Structural analysis for creep and LCF life predictions of an USC thick-wall Grade 91 superheater steel header and understanding life sensitivity to wall thickness of an AUSC Haynes ® 282 header.
20 FOSSIL-FUELED POWER PLANTS↗
Physically constrained 3D diffusion for inverse design of fiber-reinforced polymer composite materials
Designing fiber-reinforced polymer composites (FRPCs) with a tailored nonlinear stress-strain response is crucial for applications such as energy absorption in crash structures, flexible robotics, and impact-resistant protective gear. However, the inherent complexities of composite materials and the multitude of parameters involved, render traditional design and optimization methods inadequate for achieving effective inverse design of composites. In this paper, we present an AI-based inverse design framework that effectively and efficiently generates FRPCs with targeted nonlinear stress-strain responses. We introduce a physically constrained diffusion model (PC3D_Diffusion) capable of managing the complexities of composite materials and producing detailed, high-quality designs. We propose a loss-guided, learning-free approach to generate physically feasible microstructure designs by explicitly enforcing physical constraints during the generation process. For training purposes, 1.35 million FRPC samples were created, and their corresponding stress-strain curves were computed using established physics-based computational models. The results show that PC3D_Diffusion consistently generates high-quality designs with tailored mechanical behaviors, while guaranteeing compliance with the physical constraints. PC3D_Diffusion advances FRPC inverse design and may facilitate the inverse design of other 3D materials, offering potential applications in industries reliant on materials with custom mechanical properties.
Xu, Pei [Clemson Univ., SC (United States)]↗
Microscopy modality transfer of steel microstructures: Inferring scanning electron micrographs from optical microscopy using generative AI
Scanning electron microscopy (SEM) is resource intensive, which limits its throughput in some applications. As an alternative, we propose applying computer vision and machine learning to generate high-quality synthetic SEM micrographs from micrographs obtained using light optical microscopy (LOM). Working with a correlated LOM/SEM dataset of dual-phase steel images, we test generative models of various architectures, including encoder-decoder networks, generative adversarial networks (GANs), and diffusion-based models. We find that the diffusion models significantly outperform other methods on both qualitative and quantitative assessments, while preserving key metallurgical meaning. This work establishes diffusion as the state-of-the-art for microscopy modality transfer and demonstrates the potential of AI-powered microscopy to enhance LOM with micron scale structural recreation.
Computer vision↗