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At least 37 records · Page 2

Laser Powder Bed Fusion Microstructure Surrogate Model

SAND2025-11467O The Laser Powder Bed Fusion (LPBF) Microstructure Surrogate Model is a machine-learning-based tool. It predicts statistics of microstructures that are produced by the LPBF additive manufacturing process. It includes a series of codes for training, testing, and analyzing the model as well as utility scripts for handling data. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Moser, Daniel [Sandia National Lab. (SNL-CA), Live↗

Implementation of disruptive designs for gas turbine components using direct energy deposition additive manufacturing

This research aims to develop a framework for establishing the correlation between in-situ monitoring data, process parameters, and microstructure evolution in blown-powder laser-directed energy deposition (DED) additive manufacturing (AM). To achieve this, a comprehensive manufacturing framework has been developed, spanning from in-situ data acquisition, melt-pool simulation, microstructure modeling, and statistical microstructure quantification. A machine learning-based surrogate model is constructed to predict melt pool geometry directly from in-situ coaxial camera data. The surrogate model is trained using outputs from a high-fidelity melt pool simulation, which provides accurate melt pool dimension data under varying process conditions. The predicted melt pool geometry is then used as input to a microstructure model to predict microstructural features. To rigorously compare and analyze microstructures, the project introduces statistical metrics that quantify differences based on key features such as morphology and texture. Microstructures are represented using advanced statistical descriptors including angular chord length distribution, two-point spatial statistics, orientation distribution function, and global spherical harmonic. These representations are used to compute four distinct “dissimilarity scores” that quantitatively capture differences in texture and morphology. This framework is demonstrated to enable automated calibration of simulation parameters by minimizing discrepancies between simulated and target microstructures. The technology developed in this project enables direct correlation between in-situ monitoring data and resulting microstructure, paving the way for adaptive microstructure control in metal AM. This capability strengthens the connection between process parameters and final material properties, facilitating more precise and reliable material design.

36 MATERIALS SCIENCE↗

Microstructure-Sensitive modeling of surface roughness and notch effects on extreme value fatigue response

Fatigue life knockdown due to surface roughness induces local stress concentrations. Physical fatigue experiments are crucial to qualify materials for use but are limited in their capability to examine the mechanisms underlying the fatigue crack formation driving force due to these stress concentrations. Microstructure-sensitive computational models can aid in this regard and are employed in this work to examine the effects of surface roughness in Al 7075-T6. Digital microstructure models are subjected to crystal plasticity finite element method (CPFEM) simulations to examine the combined effects of microstructure and grain scale asperities on the driving force for fatigue crack formation. Following elastic–plastic shakedown, mesoscale volume-averaged fatigue indicator parameters (FIPs) are computed within fatigue damage process zones of grains. An increase in the intensity of realistic surface roughness profiles corresponds accordingly to larger FIPs but it is difficult to precisely interpret the mechanisms and effects of this intensification. We thus parametrically investigate surface asperities that couple with microstructure and lack of constraint on slip at the free surface to describe microstructure-sensitive surface roughness knockdown effects on fatigue resistance. The depth of a single valley (i.e., notch) due to surface roughness is found to be more detrimental to fatigue resistance than the radius (i.e., notch width, not to be confused with notch acuity). Finally, we also find that different crystallographic textures, grain morphologies, and number of sampled grains have a limited effect on the depth of influence of these notches.

36 MATERIALS SCIENCE↗

Latent Pitfalls in Microstructure-Based Modeling for Thermally Aged 9Cr-1Mo-V Steel (Grade 91)

A case study was conducted on a mechanistic model development that predicted tensile strength deterioration with thermal aging of 9Cr-1Mo-V steel in supporting the 60-year design life expected for advanced nuclear reactors. For property prediction beyond practical testing times, mechanistic modeling is highly desired, as it taps into the physics of structure–property relationships and therefore can generate reliable results for extrapolation. Meanwhile, as mechanistic models are often complicated, reflecting the intricacy of microstructure and strengthening mechanisms, pitfalls that are difficult to detect often exist. Here, this paper discusses latent pitfalls that are common in mechanistic modeling or specific in this 9Cr-1Mo-V case development through using the American Society of Mechanical Engineers verification and validation in computational solid mechanics (ASME V&V 10) standard for evaluating credibility of modeling in materials engineering. Suggestions are also made for enhancing reliability of microstructure-based modeling.

36 MATERIALS SCIENCE↗

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↗

An Initial Microstructurally Informed Model of High Burnup Structure Formation in UO 2 Fuel

The microstructure of a UO 2 fuel pellet changes as burnup increases, impacting fuel performance. Predicting and characterizing high burnup structure (HBS) and dark zone formation is a key part of supporting burnup limit extensions for light water reactors. This paper describes a model developed through fitting radially resolved pellet data obtained from recently published microstructural characterization data. The model predicts grain size and grain character, in addition to pore density and size, with fitting dependencies on power history variables. Separately fitting power history variables to microstructural parameters allows for insight into the underlying physical phenomena for future model development. Additionally, experimental data have been correlated to an HBS fraction to facilitate the development of a model capable of predicting a total fuel restructured fraction at the engineering scale. In conclusion, this two-step approach provides a coupling from reactor power history to microstructural data to fractional HBS and creates a basis to model HBS-dependent parameters in a fuel performance code.

High burnup structure↗

Microstructure-sensitive modeling of high temperature creep in grade-91 alloy

Predicting the effects of microstructure on high-temperature creep responses of steel components is critical to minimize the risks of failure and maximize economic viability in the energy sector. In this research, a recently developed advanced mechanistic constitutive model is employed to study the effect of microstructure on the creep responses and to rationalize experimentally reported variability in the performances of grade-91 alloy. Hundreds of experimental creep tests from the literature are used to assess the predictability of the model. The model proposed is shown to accurately predict the steady-state creep rates and also the less commonly considered, yet important, primary-to-secondary transients for a wide range of creep conditions. Further, the model is capable of extrapolating the creep response of grade 91 alloy even outside of the calibration regime. Using this model, the roles of initial microstructure described in terms of grain size, dislocation density, and precipitate content, on the creep responses are investigated. The effect of initial microstructure on the steady-state creep rates and creep-strain is found to vary with stress and temperature. Grain size plays a significant role in the low-stress creep, where the diffusional creep is dominant. On the other hand, the effects of dislocation densities and precipitate content are significant in the dislocation-plasticity-dominated high-stress and temperature regimes. Furthermore, by comparing model predictions against a large experimental creep database, it is found that variability in the creep responses can be rationalized on the basis of differences in the initial microstructure. Overall, this work provides a robust pathway for microstructure-aware engineering design, verification, and validation of metallic components for energy applications.

36 MATERIALS SCIENCE↗

Microstructure-based modeling of inner oxygen pressure in solid oxide electrolysis cells: Analysis of electrode delamination and mitigation

One major degradation mechanism during long-term operation of solid oxide electrolysis cells (SOECs) is delamination of oxygen electrodes (OEs). The driving force for the electrode delamination could be the generated high inner oxygen pressure near the electrode-electrolyte interface during operation. However, the effects of transport properties and electrode thickness on the inner oxygen partial pressure are not well understood. Here a microstructure-based electrochemical model, which includes the conduction of electrons and oxygen ions coupled with Butler-Volmer-type chemical reactions at triple-phase-boundaries (TPBs), is employed to investigate the oxygen pressure in lanthanum strontium manganate (LSM)-based SOECs. The model is applied to both two-dimensional (2D) prototype microstructures and three-dimensional (3D) realistic microstructures, and the oxygen pressure is analyzed as a function of transport properties and electrode thickness under both potentiostatic and galvanostatic operations. The simulation results suggest strategies to suppress electrode delamination. The simulation results are compared to an analytical solution, and the discrepancies are attributed to the Butler-Volmer-type kinetics included in the microstructure-based model.

25 ENERGY STORAGE↗

Microstructure-Based Modeling of Inner Oxygen Pressure in Solid Oxide Electrolysis Cells

One major degradation mechanism during long-term operation of solid oxide electrolysis cells (SOECs) is delamination of oxygen electrodes (OEs). The driving force for the electrode delamination could be the generated high inner oxygen pressure near the electrode-electrolyte interface during operation. However, the effects of transport properties and electrode thickness on the inner oxygen partial pressure are not well understood. Here, a microstructure-based electrochemical model which includes the conduction of electrons and oxygen ions coupled with Butler-Volmer-type chemical reactions at triple-phase-boundaries (TPBs), is employed to investigate the oxygen pressure in lanthanum strontium manganate (LSM)-based SOECs. The model is applied to both two-dimensional (2D) prototype microstructures and three-dimensional (3D) realistic microstructures, and the oxygen pressure is analyzed as a function of transport properties and electrode thickness under both potentiostatic and galvanostatic operations. The simulation results suggest strategies to suppress electrode delamination. The simulation results are compared to an analytical solution, and the discrepancies are attributed to the Butler-Volmer-type kinetics included in the microstructure-based model.

Xue, Fei↗

A Gaussian process autoregressive model capturing microstructure evolution paths in a Ni–Mo–Nb alloy

Additive manufacturing is increasingly being employed to produce components of complex geometries in structural alloys because of the expected energy savings associated with the near-net-shape capability and the ability to build in novel internal features that are not possible with many conventional manufacturing approaches. However, because of the extreme thermal conditions encountered, the non-equilibrium microstructures produced during powder bed-based additive manufacturing processes must be subjected to custom post-heat treatment processes to recover the target mechanical properties. Phase-field models and simulation techniques have matured to a state where the microstructure evolution paths, and the morphologies of the resulting precipitate phases can be predicted reasonably accurately, considering alloy-specific thermodynamic and kinetic aspects of the nucleation and growth processes. However, phase-field simulations are computationally intensive, which precludes the ability to apply the simulations directly to the length scale of the entire component. Therefore, it is highly desirable to develop low-computational-cost surrogate models that effectively capture the physics at the microstructural length scale, while facilitating the design of optimized processing conditions resulting in location-specific targeted microstructures at the component scale. The work presented here demonstrates the application of the materials knowledge system framework to develop a surrogate model that effectively captures the microstructural path during annealing of a Ni–Mo–Nb alloy containing different Mo and Nb compositions known to segregate during solidification under additive manufacturing conditions. Specifically, the surrogate model built in this work is based on a Gaussian process autoregressive model informed by statistical representation of simulated microstructures using two-point correlations and dimensionality reduction through principal component analysis. In conclusion, this surrogate model is shown to capture the bifurcation of the microstructural path during precipitation, which yields a microstructure dominated by the $\gamma^{\prime\prime}$ phase at high Nb concentrations and the $\delta$ phase at low Nb concentrations.

36 MATERIALS SCIENCE↗

GrainPaint: A multi-scale diffusion-based generative model for microstructure reconstruction of large-scale objects

Simulation-based approaches to microstructure generation can suffer from a variety of limitations, such as high memory usage, long computational times, and difficulties in generating complex geometries. Generative machine learning models present a way around these issues, but they have previously been limited by the fixed size of their generation area. Here, we present a new microstructure generation methodology leveraging advances in inpainting using denoising diffusion models to overcome this generation area limitation. We show that microstructures generated with the presented methodology are statistically similar to grain structures generated with a kinetic Monte Carlo simulator, SPPARKS.

36 MATERIALS SCIENCE↗

Modeling Graphite Behavior (Fracture and Oxidation)

Purpose of this work Oxidation modeling Oxidation of full-scale components Local properties in oxidized components Graphite fracture strength Phase field fracture modeling Microstructurally informed fracture model IG-110 tensile results and comparison to experiment Work in progress and future work

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Uncertainty Quantification Framework for Predicting Material Response with Large Number of Parameters: Application to Creep Prediction in Ferritic-Martensitic Steels Using Combined Crystal Plasticity and Grain Boundary Models

This paper presents an uncertainty quantification (UQ) framework for the physics-based model prediction of material response with a large number of parameters. The application problem presented in this work is that of predicting creep in Grade 91 steel at 600°C. The material response is defined with a physically based microstructural model with constitutive equations emulating several observed phenomena in Grade 91 and embodied into an explicit geometry mesoscale finite element model for prior austenite grains and grain boundaries. Creep within the grains and in grain boundaries are represented by crystal plasticity for dislocation motion and a physics-based model for cavity growth and nucleation, respectively. The creep behavior of this material is influenced by several parameters, some of which have a wide range of variation based on experimental data. UQ combined with microstructural modeling can discover the core microstructural causes of experimental variability, leading to improved materials with lower variability in critical long-term material properties. In this study, we investigate the model's uncertainty to identify material properties that may be modified during production to increase creep life and analyze different components of the crystal plasticity model for improvements. For this purpose, a quantity of interest is defined as time to minimum creep rate, which correlates well to the creep failure of the material. A deep neural network model was trained and validated to be used as a surrogate for the finite element model. Then, a variance-based sensitivity analysis is performed on the surrogate model to find the Sobol indices of the input parameters in respect to the output quantity of interest. The Sobol indices are used to reduce the dimensionality of the model. Generalized polynomial chaos expansion is used on the reduced basis models to propagate the uncertainty from the input parameters to the quantity of interest using the deep neural network surrogate model. These results are benchmarked against uncertainty propagation using Monte Carlo simulations. In conclusion, the UQ performed through the reduced basis model captures almost all the uncertainty in the model with significantly fewer simulations, making it possible to perform the UQ directly via simulations with the finite element model rather than surrogate machine-learned models.

36 MATERIALS SCIENCE↗

Microstructure-Based Modeling of Laser Beam Shaping During Additive Manufacturing

Recent experimental studies suggest the use of spatially extended laser beam profiles as a strategy to control the melt pool during laser powder bed fusion (LPBF) additive manufacturing. However, linkages connecting laser beam profiles to thermal fields and resultant microstructures have not been established. Herein, we employ a coupled thermal transport-Monte Carlo model to predict the evolution of temperature fields and grain microstructures during LPBF using Gaussian, ring, and Bessel beam profiles. In addition, simulation results reveal that the ring-shaped beam yields lower temperatures compared with the Gaussian beam. Owing to the small melt pool size when using the Bessel beam, the grains are smaller in size and more equiaxed compared to those using the Gaussian and ring beams. Our approach provides future avenues to predict the impact of laser beam shaping on microstructure development during LPBF.

36 MATERIALS SCIENCE↗

Multi‐scale simulation of rock compaction through breakage models with microstructure evolution

Abstract. Regional subsidence due to fluid depletion includes the interaction among multiple physical processes. Specifically, rock compaction is governed by coupled hydro-mechanical feedbacks involving fluid flow, effective stress change and pore collapse. Although poroelastic models are often used to explain the delay between depletion and subsidence, recent evidence indicates that inelastic effects could alter the rock microstructure, thus exacerbating coupling effects. Here, a constitutive law built within the framework of Breakage Mechanics is proposed to account for the inherent connection between rock microstructure, hydraulic conductivity, and pore compaction. Furthermore, it is embedded into a 1-D hydromechanical coupled finite element analysis (FEA) to explore the effects of micro-structure rearrangement on the development of reservoir compaction. Numerical examples with the proposed model are compared with simulations under constant hydraulic conductivity to illustrate the model capability to capture the non-linear processes of reservoir compaction induced by fluid depletion.

Buscarnera, Giuseppe↗

Resolving crystallographic geometrically necessary dislocations in three dimensions in a hexagonal close packed titanium alloy

Abstract Geometrically necessary dislocation (GND) content is measured from mm 3 -scaled Ti7Al three-dimensional (3D) microstructural data using a theory extended for hexagonal close packed crystals, which accounts for basal, prismatic and pyramidal ⟨ c + a ⟩ type dislocation content. The Ti7Al samples have been mechanically pre-strained to two different strain levels, and will then be strained along the same axis in uniaxial tension during simulation. Both inter- and intragranular GNDs across the microstructures have been characterized, with a large contribution of pyramidal ⟨ c + a ⟩ GNDs, consistent with the relative slip activity involved in pre-straining. The spatially resolved crystallographic GND distributions within the 3D microstructures are used to instantiate a microstructure model for forward modeling deformation simulations by a dislocation density hardening elasto-viscoplastic fast Fourier transform framework. Coarsening the voxel resolution during the initial microstructure construction procedure is shown to strongly impact both the magnitude and spatial distribution of the GNDs and in turn the forward deformation response of the pre-strained material. This study indicates that the voxel resolution desired when transferring from measured to model microstructures need not only be proportionally scaled with the microstructure but also sufficiently fine to capture the subgranular orientation gradients that may already be present in the material.

Materials Science↗