Simulation Of Grain Growth Research Articles

Monte Carlo Simulation of Recrystallization and Grain Growth in Annealed Al-4% Cu Alloy: Validation through Experimental Microstructure Comparison

Monte Carlo Simulation of Recrystallization and Grain Growth in Annealed Al-4% Cu Alloy: Validation through Experimental Microstructure Comparison

Cellular automaton simulation of solid-phase grain growth under conditions involving scanning heat sources and temperature gradients

Abstract A cellular automaton simulation of grain growth in a solid phase was conducted, considering the temperature gradient, heat source movement, and conditions favoring prioritized grain growth. The results reveal that, under optimal conditions, cellular grains elongated in the direction of the heat source movement. Detailed simulations illustrate the dynamics of grain growth and effect of mobility and driving forces on the dynamics, providing valuable insights into cellular grain growth in a solid phase.

Simulation of recrystallization grain growth in AZ61 magnesium alloy based on GA and 3D CA

In this paper, the Arrhenius constitutive model (GA-Arrhenius) optimized by genetic algorithm (GA) is established, and the dynamic recrystallization (DRX) grain growth process of AZ61 magnesium alloy during hot deformation is simulated by three-dimensional cellular automata (3D CA), and the morphological evolution and grain boundary migration of recrystallized grains are reproduced. The results show that the GA-Arrhenius model accurately predicts the rheological behavior of the material, while the 3D CA model effectively describes the formation and growth mechanism of grains during DRX.

Phase-Field Simulation of Grain Growth in Uranium Silicide Nuclear Fuel

Uranium silicide (U3Si2) is regarded as a viable fuel option for improving the safety of nuclear power plants. In the present work, phase-field simulations were employed to investigate grain growth phenomena, encompassing both isotropic and anisotropic grain growth. In simulations of isotropic grain growth, it is commonly assumed that the energy and mobility of the grain boundaries (GBs) remain constant, represented by average values. The calculated grain growth kinetic rate constant, K, exhibits a close correspondence with the experimental measurements, indicating a strong agreement between the two. In simulations of anisotropic grain growth, the values of GB energy and mobility are correlated with the angular disparity between GBs. The simulation results demonstrated that the growth rate of U3Si2 can be influenced by both the energy anisotropy and mobility anisotropy of GBs. Furthermore, the anisotropy in mobility results in a greater prevalence of low-angle GB distribution in comparison to high-angle GBs. However, the energy anisotropy of GBs does not impact the frequency distribution of the angle difference between GBs.

Modeling and simulation of grain growth for FGH96 superalloy using a developed cellular automaton model

Through heat treatment experiments and numerical simulations, the effects of the heating temperature (1313–1423 K) and holding time (10–240 min) on the grain growth behavior of the extruded FGH96 alloy were investigated. A two-dimensional cellular automata (CA) model that considered the dissolution of the γ′ phase over time and the distribution characteristics with different sizes was developed to explore the grain growth behavior above the γ′ phase over-solution temperature (1423 K) and below the γ′ sub-solution temperature (1383 K), respectively. The results showed that the rate of grain growth of FGH96 alloy was obviously enhanced when the heating temperature exceeded 1363 K, which was mainly related to the dissolution of the γ′ phase, and the grain growth of FGH96 alloy mainly occurred during the initial stage of insulation. The grain growth model of the extruded FHG96 alloy could accurately predict the grain growth behavior, and the simulation results were in good agreement with the experimental results at over-solution temperature or sub-solution temperature. The effects of volume fraction and radius of γ′ phase on the grain growth behavior of FGH96 alloy were studied by simulating the grain growth behavior of FGH96 alloy under different sizes and volume fractions of γ′ phase. The results follow the Zener relation, and the coefficient n in the Zener relation was determined by fitting the simulation results.

Monte Carlo simulation of grain growth in heat-affected zone during welding process of cast steel joint and optimization of welding process

Monte Carlo simulation of grain growth in heat-affected zone during welding process of cast steel joint and optimization of welding process

Mean-field modeling and Phase-field simulation of Grain Growth under Directional driving forces

Directional grain growth is a common phenomenon in the synthetic and natural evolution of various polycrystals. It occurs in the presence of an external driving force, such as a temperature gradient, along which grains show a preferred, yet competitive, growth. Novel additive manufacturing processes, with intense, localized energy deposition, are prominent examples of when directional grain growth can occur, beneath the melting pool. In this work, we derive a phenomenological mean-field model and perform 3D phase-field simulations to investigate the directional grain growth and its underlying physical mechanisms. The effect of the intensity of driving force is simulated and systematically analyzed at the evolving growth front as well as various cross-sections perpendicular to the direction of the driving force. We found that although the directional growth significantly deviates from normal grain growth, it is still governed by a power law relation 〈R〉∝tn, with an exponent n∼ 0.6–0.7. The exponent n exhibits a nontrivial dependence on the magnitude of the directional driving force, such that the lowest growth exponent is observed for intermediate driving forces. We elaborate that this can originate from the fact that the forces at grain boundary junctions evolve out of balance under the influence of the directional driving force. With increasing the driving forces, the growth exponent asymptotically approaches a value of n≈0.63, imposed by the largest possible grain aspect ratio for given grain boundary energies. The current combined mean-field and phase-field framework pave the way for future exploration in broader contexts such as the evolution of complex additively manufactured microstructures.

Insights into abnormal grain growth in copper thin films for reduced electrical resistivity: A quantitative multi-order-parameter phase-field study under finite element framework

Copper thin films are frequently utilized as microelectronic interconnects. Inducing abnormal grain growth (AGG) may result in reduced electrical resistivity of copper thin films. However, an in-depth understanding of AGG mechanism and quantitative simulation of microstructure evolution during AGG in copper thin films are still missing, prohibiting the regulation and even design of AGG process. In this paper, a multi-order-parameter phase-field (MOP-PF) model coupled with elastic mechanics under a finite element framework was first developed and applied to study the AGG mechanism in copper thin films. It was found that both elastic and grain boundary anisotropies can induce AGG, but elastic anisotropy dominates the evolution of individual grains. Subsequently, a quantitative simulation of microstructure evolution/kinetics during AGG in copper thin films was achieved by inputting the accurate materials parameters from theoretical/experimental data. A further combination with the Mayadas-Shatzkes model led to a quantitative prediction of the evolution of electrical resistivities, from which several feasible strategies were proposed for preparing high-performance copper thin films with reduced electrical resistivities. Furthermore, it is anticipated that the presently developed framework should be generally applicable for quantitative phase-field simulation of grain growth in bulk materials/films.

The Modeling and Simulation of Austenite Grain Growth in 25Cr2Ni4MoV Nuclear-Power Rotor Steel

The modeling of austenite grain growth of 25Cr2Ni4MoV steel for super-large nuclear-power rotors was investigated during the common heating process including the continuous heating and isothermal heating process. Based on the isothermal grain growth model considering the steady-state grain size and the rule of additivity, a new grain growth model during the continuous heating process was established. The comparison between experimental and predicted results indicates the model has good predictability. To describe the anisotropic and isotropic grain growth during the different isothermal heating stages of the super-large nuclear-power rotor, a cellular automaton model considering anisotropic grain boundary energy for grain growth of 25Cr2Ni4MoV steel was developed. It is found that the anisotropic grain boundary energy mainly exists in the early isothermal heating stage at lower temperatures, and the normal grain growth occurs under anisotropic grain boundary energy conditions. When the temperature is not less than 1273 K and the cellular automaton step is not less than 15, the normal grain growth containing only isotropic grain boundary energy occurs. The analysis of the morphology, energy variance, topology and growth kinetics further indicates that normal grain growth of 25Cr2Ni4MoV steel can be simulated fairly well by the present CA model.

Vertex Simulation of Grain Growth in the Presence of Zener Pinning Effect

Vertex Simulation of Grain Growth in the Presence of Zener Pinning Effect

Monte-Carlo-Assisted Phase Field Simulations of Grain Structure Evolution during the Welding Process

A Monte-Carlo-assisted phase field model for the simulation of grain growth in metals and alloys is presented. The simulation time in this model is connected to real time through an experimental data-based kinetic model. Site selection probability is introduced to simulate grain structure evolution under non-isothermal conditions. The grain evolutions with temporal and spatial distributions of temperature during the welding process are comprehensively reproduced. The average size and topological texture of the generated grains in the fusion zone and heat-affected zone are examined. The computed results are compared to experimental data for laser-welding two alloys: Fe–6.5 wt.%Si and low-carbon steel. The applications of real-time–temperature based phase field simulation to material processing indicate significant promise for understanding grain structures during the welding process or additive manufacturing processes.

Cellular automata simulation of grain growth of powder metallurgy Ni-based superalloy

Cellular automata simulation of grain growth of powder metallurgy Ni-based superalloy

Simulation of Austenite Grain Growth in Low-Alloyed Steels upon Austenitization

Simulation of Austenite Grain Growth in Low-Alloyed Steels upon Austenitization

Simulation of Austenite Grain Growth in Low-Alloyed Steels upon Austenitization

A model has been developed that describes the grain growth upon austenitization, taking intoaccount the pinning of moving grain boundaries by carbonitride precipitates. The behavior of these precipitates is described using our previously developed approach for predicting the evolution of carbonitride particles. The software implementation of the model was carried out, and numerical calculations were performed.The calculation results have been compared with the experimental data in the literature and their good agreementis shown.

Grain Boundary Migration in Polycrystals

Grain boundaries in polycrystalline materials migrate to reduce the total excess energy. It has recently been found that the factors governing migration rates of boundaries in bicrystals are insufficient to explain boundary migration in polycrystals. We first review our current understanding of the atomistic mechanisms of grain boundary migration based on simulations and high-resolution transmission electron microscopy observations. We then review our current understanding at the continuum scale based on simulations and observations using high-energy diffraction microscopy. We conclude that detailed comparisons of experimental observations with atomistic simulations of migration in polycrystals (rather than bicrystals) are required to better understand the mechanisms of grain boundary migration, that the driving force for grain boundary migration in polycrystals must include factors other than curvature, and that current simulations of grain growth are insufficient for reproducing experimental observations, possibly because of an inadequate representation of the driving force.

Non-self-similar grain growth by zero-temperature Potts model

Self-similar coarsening, a well-researched aspect of normal grain growth, is a key component of grain growth theories and visible in mesoscopic simulations of ideal grain growth. One particular simulation algorithm is the Potts model, which can be used to perform a thorough investigation of the temporal evolution of polycrystalline microstructures. Used with carefully selected parameters, the Potts model recreates normal grain growth quite accurately. One specific feature necessary to reach this goal is the simulation temperature, i.e. simulation thermal energy. It prevents grain boundaries from aligning along the underlying simulation lattice. Nevertheless, there are still today many researchers who implement classical zero-temperature Potts model simulations—often for reasons of simplification and/or modeling speed. In the current study, we show in detail the negative effect of setting the simulation temperature to zero. As a result, a unique type of non-self-similar coarsening is observed, where, e.g. the von Neumann–Mullins-relation shows unexpected time-dependent behavior. Hence, this study can be understood as a blueprint on why not to use a zero-temperature Potts model.

Phase-field simulation of grain nucleation, growth, and Rayleigh distribution of U3Si2 nuclear fuel

U3Si2 is a potential accident-tolerant fuel (ATF) due to its high thermal conductivity and uranium density relative to UO2. The grain size and distribution play an essential role in the service performance of U3Si2. However, the grain evolution is quite complicated and remains unclear, which limits further application of U3Si2 in the ATF assembly. In the present work, a phase-field model is employed to investigate the nucleation and growth of grains in U3Si2. Our results show that the number of grains rises rapidly at the nucleation stage until they occupy the whole system. After that, the grain radius and area continue to grow, and the grain number decays. The grain area increases in time according to the linear law, while the mean grain radius increases with time in a power law form with the scaling growth exponent z = 0.42, which is quite close to the theoretically predicted value. Finally, we performed statistical analysis and found that the grain size evolution of U3Si2 obeys Rayleigh distribution. Our simulation not only elucidates the nucleation and evolution of grains in U3Si2 during the thermal treatment process unambiguously but also provides a fundamental study on the investigation of grain growth, subdivision, and even amorphization in the irradiated condition, which is very important for U3Si2 used as ATF in the light water reactor.

Simulation of Grain Growth in Aluminum Alloys under Selective Laser Melting

Simulation of Grain Growth in Aluminum Alloys under Selective Laser Melting

Three-Dimensional Numerical Simulation of Grain Growth during Selective Laser Melting of 316L Stainless Steel.

The grain structure of the selective laser melting additive manufactured parts has been shown to be heterogeneous and spatially non-uniform compared to the traditional manufacturing process. However, the complex formation mechanism of these unique grain structures is hard to reveal using the experimental method alone. In this study, we presented a high-fidelity 3D numerical model to address the grain growth mechanisms during the selective laser melting of 316 stainless steel, including two heating modes, i.e., conduction mode and keyhole mode melting. In the numerical model, the powder-scale thermo-fluid dynamics are simulated using the finite volume method with the volume of fluid method. At the same time, the grain structure evolution is sequentially predicted by the cellular automaton method with the predicted temperature field and the as-melted powder bed configuration as input. The simulation results agree well with the experimental data available in the literature. The influence of the process parameters and the keyhole and keyhole-induced void on grain structure formation are addressed in detail. The findings of this study are helpful to the optimization of process parameters for tailoring the microstructure of fabricated parts with expected mechanical properties.

Establishing reduced-order process-structure linkages from phase field simulations of dendritic grain growth during solidification

Establishing a rigorous quantitative relationship between solidification parameters and microstructure is critical for facilitating the fabrication of metallic components with desired properties. Conventional approaches are time and effort intensive and lack a rigorous framework for the systematic and accurate quantification of microstructure. In this study, a novel data science approach is applied to extract high-value, computationally low-cost process-structure linkages from phase field simulations of dendritic grain growth during solidification. Reduced-order measures, which are adequate for identifying the salient features of microstructures and evaluating the effects of solidification process parameters, are obtained through two-point statistics and principal component analysis. Building on these reduced-order measures, computationally efficient surrogate models are established using various regression methods. It is demonstrated that the surrogate model with the best predictive performance can provide good predictions of not only the reduced-order measures but also the two-point statistics. Moreover, it is found that some reduced-order measures exhibit very complex relations with process parameters, which results in poor prediction accuracy. Compared with discarding the features with inaccurately predicted values, including them may lead to worse recovery of higher-order microstructure statistics. These results highlight the importance of microstructure feature selection in the improvement of prediction accuracy.