Process Modeling Microstructure Measurements and Residual Stresses in Additively Manufacutred Austenitic Stainless Steels.
Abstract not provided.
Sugar, Joshua Daniel↗
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3D CHARACTERIZATION MICROSTRUCTURAL MODELING & SERVICE TO COMMUNITY.
Abstract not provided.
Madison, Jonathan D↗
A Bezier Curve Informed Melt Pool Geometry to Model Additive Manufacturing Microstructures Using SPPARKS
Additive manufacturing is a transformative technology with the potential to manufacture designs which traditional subtractive machining methods cannot. Additive manufacturing offers fast builds at near final desired geometry; however, material properties and variability from part to part remain a challenge for certification and qualification of metallic components. AM induced metallic microstructures are spatially heterogeneous and highly process dependent. Engineering properties such as strength and toughness are significantly affected by microstructure morphologies resulting from the manufacturing process Linking process parameters to microstructures and ultimately to the dynamic response of AM materials is critical to certifying and qualifying AM built parts and components and improving the performance of AM materials. The AM fabrication process is characterized by building parts layer by layer using a selective laser melt process guided by a computer. A laser selectively scans and melts metal according to a designated geometry. As the laser scans, metal melts, fuses, and solidifies forming the final geometry in a layerwise fashion. As the laser heat source moves away, the metal cools and solidifies forming metallic microstructures. This work describes a microstructure modeling application implemented in the SPPARKS kinetic Monte Carlo computational framework for simulating the resulting microstructures. The application uses Bzier curves and surfaces to model the melt pool surface and spatial temperature profile induced by moving the laser heat source; it simulates the melting and fusing of metal at the laser hot spot and microstructure formation and evolution when the laser moves away. The geometry of the melt pool is quite flexible and we explore effects of variances in model parameters on simulated microstructures.
36 MATERIALS SCIENCE↗
Reduced-order model for microstructure evolution prediction in the electrodes of solid oxide fuel cell with dynamic discrepancy reduced modeling
Microstructure evolution in the electrodes of solid oxide fuel cell is an important degradation mechanism which reduces active sites for redox reaction and the electric conductivity. Phase field models for microstructure evolution simulation are usually expensive for large scale simulations. In this paper, a reduced-order coarsening model is developed using dynamic discrepancy reduced modeling, which reduces the model order by inserting Gaussian process stochastic functions into the dynamic equations of Ostwald ripening. The reduced order model has been calibrated on a dataset generated by a phase field model that has been well validated to experiments. A validating dataset has also been generated with which the model prediction show good agreement. This model is further applied to predict long term microstructure evolution in different SOFC electrodes. Finally, this work is the first attempt of building a degradation model of SOFC using data science techniques.
36 MATERIALS SCIENCE↗
Microstructure-based model of nonlinear ultrasonic response in materials with distributed defects
Nonlinear ultrasonic technique is one of promising NDE methods for monitoring defect evolution in irradiated nuclear materials. In this work, we have studied the effect of distributed defects on nonlinear ultrasonic response, in particular, the nonlinear parameter ß from the 2nd harmonic wave. A three-dimensional microstructure-based model has been developed for investigating the dynamic interaction between distributed defects and a propagating longitudinal sound wave. Both linear and nonlinear materials are considered. It is known that the 2nd harmonic wave is generated when a monochromatic wave propagates in a nonlinear elastic material. The results show that second phase particles with elastic heterogeneity and lattice mismatch have strong impact on the nonlinear parameter ß. Lattice mismatches have more effect on the nonlinear parameter no matter they are expansion or contraction. The developed model enables one to explore the contributions of different types of defects to the nonlinear ultrasonic signals and shed light on the development of nonlinear ultrasound nondestructive detection of material defects in nuclear reactors.
Second harmonic generation, Scattered distributed ↗
A microstructure-based modeling approach to predict the mechanical properties of Zr alloy with hydride precipitates
In nuclear reactors, hydrides can form in fuel cladding due to hydrogen absorption in ZIrcaloy and cause embrittlement. This work presents a microstructure-based finite element model to predict the stress-strain response of Zircaloy containing hydrides. Quantitative microstructural details extracted from scanning electron microscopy (SEM) images were used to generate heterogeneous microstructures including the morphology and spatial distribution of hydrides. The constitutive material model for zircaloy in this study is based on crystal plasticity theory which considers the hexagonal close-packed (HCP) atomic structure of Zircaloy material. The hydrides were modeled as brittle material along with a damage model. Hydride formation inside the zircoloy matrix results in residual stress. This phenomenon is also captured in this model. A parametric study has been conducted to understand the effect of volume fraction, orientation, and lamellae thickness of the hydride phase on the mechanical properties of the overall material.
Kulkarni, Shank S.↗
Data-Driven Selection of Tessellation Models Describing Polycrystalline Microstructures
Tessellation models have proven to be useful for the geometric description of grain microstructures in polycrystalline materials. With the use of a suitable tessellation model, the complex morphology of grains can be represented by a small number of parameters assigned to each grain, which not only entails a significant reduction in complexity, but also facilitates the investigation of certain geometric features of the microstructure. However, for a given set of microstructural data, the choice of a particular geometric model is traditionally based on researcher intuition. The model should provide a sufficiently good approximation to the data, while keeping the number of parameters small. In this paper, we discuss general aspects of the process of model selection and suggest several criteria for selecting an appropriate candidate from a certain set of tessellation models. The choice of candidate represents a trade-off between accuracy and complexity of the model. Here, the selected model is used solely to approximate given data samples, but it also provides guidance for developing stochastic tessellation models and generating virtual microstructures. Model fitting is carried out by simulated annealing, applied in a consistent manner to twelve different tessellation models.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Identify the influence of microstructure on mesoscale creep and fatigue damage
This report describes the development of a microstructural model that can quantify the uncertainty in the observed rupture life of Grade 91 steel. The model is microstructural, meaning it relates microstructural characteristics of the material to the resulting material response. As such, one of the uses of this model is to identify the key microstructural parameters controlling the development of damage in Grade 91 operating at elevated temperatures. The report describes two veins of work: improvements to the crystal plasticity model required to run the uncertainty quantification analysis and the results of that UQ analysis. For creep, the model identifies the grain boundary diffusivity as the critical parameter controlling the rupture life of the material. The report demonstrates that a reasonable microstructural distribution of grain boundary diffusivity can account for the observed macroscale variation in rupture life at fixed temperature and load.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
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↗
Characteristic constitution model and microstructure of an Al-3.5Cu-1.5Li alloy subjected to thermal deformation
The constitution model and microstructure of 2A97 Al-Li alloy during thermal deformation were investigated. The thermal deformation behavior was dominated by the interaction between work hardening and dynamic softening. The constitution model was constructed to predict the rheology behavior, in which Zener-Holloman equation with a fifth-order polynomial was set to describe the effect of various strains on the material parameters. Processing maps displayed to exist two unstable domains in microstructure (one domain with the temperature range from 300 °C to 370 °C and the strain rate range from 10{sup 0.5} s{sup −1} to 10s{sup −1}, and the other with the temperature range from 380 °C to 500 °C and the strain rate range from 0 s{sup −1} to 10 s{sup −1}). Microstructural characteristics of dynamic recovery (DRV) and dynamic recrystallization (DRX) in the stable domain and local rheology in the unstable domain were observed, respectively. The dynamic precipitation (DPN) of T{sub 1} phases and the interaction between dislocations and Al{sub 20}Cu{sub 2}Mn{sub 3} and Al{sub 3}Zr phases were concluded by high resolution transmission electron microscopy (HRTEM), which was the first experimental evidence for the DPN in 2A97 Al-Li alloy. All these confirmed the occurrence of the continuous dynamic recrystallization during thermal deformation.
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↗
Reduced-order model for microstructure evolution simulation in solid oxide fuel cell with dynamic discrepancy reduced modeling
Presented by NETL at TMS 2019 Annual Meeting, 03/19
Lei, Yinkai↗
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↗