Prediction Of Grain Size Research Articles

Austenite Growth Behavior and Prediction Modeling of Ti Microalloyed Steel.

Previous studies on the austenite grain growth were mostly based on a fixed temperature, and the relationship between the austenite grain and austenitizing parameters was fitted according to the results. However, there is a lack of quantitative research on the austenite grain growth during the heating process. In the present work, based on the diffusion principle of the controlled Ti microalloying element, the diffusion process of carbonitrides containing Ti during the heating process was analyzed. Combined with the precipitation model and the austenite growth model, the prediction model of austenite grain growth of Ti microalloyed steel during different heat treatment processes was established. The austenite grain size versus the temperature at four different heating rates of 0.5, 1, 10, 100 °C/s was calculated. The grain growth behavior of austenite during the heating process of Ti microalloyed steel was studied by optical microscope, scanning electron microscope and transmission electron microscope. The experimental data of the austenite grain size was in good agreement with the calculation by the proposed model, which provides a new idea for the prediction of austenite grain size in non-equilibrium state during the heating process. In addition, for Ti-containing microalloyed steels, the austenite grain size increased with the increasing heating temperature, while it changed little by further prolonging isothermal time after certain heating time, which was related to the equilibrium degree of the precipitation and the dissolution of Ti element. The austenite grain coarsening temperature of the tested Ti microalloyed steel was estimated within 1100~1200 °C.

A Grain Size Profile Prediction Method Based on Combined Model of Extreme Gradient Boosting and Artificial Neural Network and Its Application in Sand Control Design

Summary The grain size distribution along the well depth is of great significance for the prediction of the physical properties and the staged sand control design of the unconsolidated or weakly consolidated sandstone reservoir. In this paper, a new method for predicting the formation median grain size profile based on the combination model of extreme gradient boosting (XGBoost) and artificial neural network (ANN) is proposed. The machine learning algorithm and weighted combination model are applied to the prediction and analysis of reservoir grain size. The prediction model is improved from two aspects: First, the feature engineering of the XGBoost-ANN model is constructed by using the data of multiple sampling points on the logging curve. Second, the prediction accuracy is improved by increasing the dimension of the prediction model, that is, the XGBoost and ANN single-prediction models are weighted by the error reciprocal method and a combined prediction model containing multidimensional information is established. The research results show that compared with the single-point mapping model, the prediction accuracy of the multipoint mapping model considering the vertical geological continuity of the reservoir is higher than that of the single-point prediction and the coefficient of determination in the testing set can be improved up to 14.5%. The influence of different weighting methods on prediction performance is studied, and the prediction performance of original XGBoost, ANN, and XGBoost-ANN combined models is compared. The combined prediction model has a higher prediction accuracy than the single XGBoost and ANN models with the same number of sampling points and the coefficient of determination can be improved by up to 16.5%. The prediction accuracy and generalization ability of the XGBoost-ANN combined model are evaluated comprehensively. The combined model is used to design layered sand control of a well in an adjacent block, and good results have been achieved in production practice. This study provides a new method with high accuracy and efficiency for the prediction of unconsolidated sand median grain size profile.

Prediction of grain size and dislocation density in the cold spraying process using a dislocation-based model

The cold spray particle deposition process was simulated by modeling the high-velocity impacts of spherical particles onto a flat substrate at different velocities. In addition to simulating the single particle, this study also employed a unique simulation technique to predict the evolution of the material's microstructure under real process conditions. A dislocation-based model was implemented as a VUMAT subroutine to model the microstructure. Additionally, Python scripting was also utilized in the multi-particle impact simulation to create particles and assign velocity and interaction randomly. The relationships between stress-strain values and material microstructure are linked together. By using this model, calculations were done for the equivalent plastic strain, dislocation density, and grain size in the particle, substrate, and interface. At the interface, there were high levels of plastic strain and strain rates reaching up to, which formed a bond between the particles and the substrate. It was found that a minimum plastic strain of approximately 1 is needed to create this bond. At velocities exceeding a critical velocity, a jet was produced. The particle grain size in regions distant from the interface measured between 200 and 500 nm, which is in good agreement with the experimental results. The smallest grain size was observed at the interface, measuring 140 nm. The validation outcomes demonstrated that the suggested model effectively replicates the cold spraying process. As a result, the suggested model has the capability to predict and evaluate the changes in the material's microstructure during the cold spraying process using various factors.

Prediction of Grain Size in a High Cobalt Nickel-Based Superalloy.

With the advancement in computational approaches and experimental, simulation, and modeling tools in recent decades, a trial-and-validation method is attracting more attention in the materials community. The development of powder metallurgy Ni-based superalloys is a vivid example that relies on simulation and experiments to produce desired microstructure and properties in a tightly controlled manner. In this research, we show an integrated approach to predicting the grain size of industrial forgings starting from lab-scale cylindrical compression by employing modeling and experimental validation. (a) Cylindrical compression tests to obtain accurate flow stress data and the hot working processing window; (b) double-cone tests of laboratory scale validation; (c) sub-scale forgings for further validation under production conditions; and (d) application and validation on full-scale industrial forgings. The procedure uses modeling and simulation to predict metal flow, strain, strain rate, temperature, and the resulting grain size as a function of thermo-mechanical processing conditions. The models are calibrated with experimental data until the accuracy of the modeling predictions is at an acceptable level, which is defined as the accuracy at which the results can be used to design and evaluate industrial forgings.

Prediction of Surface Microstructure, Grain Size, Martensite Content, and Microhardness of 316L Austenitic Stainless Steel in Surface Mechanical Grinding Treatment Process

Prediction of Surface Microstructure, Grain Size, Martensite Content, and Microhardness of 316L Austenitic Stainless Steel in Surface Mechanical Grinding Treatment Process

Machine Learning-Based Prediction of Grain Size from Colored Microstructure

We constructed a convolutional neural network to estimate average grain size from microstructure images. In the previous study from our research group, the network was trained using GB-type images in which the grain matrix and grain boundary were represented in white and black, respectively. The model well estimated the same GB-type images, but did not properly predict CL-type images where grain boundaries were defined by color contrast between grains. In the present study, the convolutional neural network was trained using CL-type microstructure images, and evaluated the average grain size, for comparison with the previous GB-type model. The relationship between the microstructure image and the average grain size was determined using regression rather than classification. Then, the results were compared with the previous ones. Finally, the proposed approach was used for actual microstructural image analysis. Mid-layer images were extracted to examine how the network recognizes the characteristics of microstructures, such as grain color and grain boundary. Like the previous GB-type model, the present CL-type model seems to estimate the average grain size from the curvature of the grain boundary through edge detection of the grain boundaries. However, the GB- and CL-type models only properly predicted the grain size from the same kind of images as the training data, because the definitions of the grain boundaries of the two models were different.

Numerical prediction of microstructure evolution of high-speed railway axle formed using hot cross wedge rolling

The ununiform distribution of the grain size and coarse crystals is easy to be appeared in the high-speed railway axle (HSRA) formed by hot cross wedge rolling (CWR). It seriously reduces the comprehensive mechanical properties of the HSRA. It is vital to numerically predict microstructure evolution, so that the mixed and coarse crystals can be accurately controlled via process parameters optimization. In this study, the microstructure evolution model of the 40CrNiMo steel was developed and implemented into the DEFORM-3D software by programming a user subroutine to simulate hot upsetting. Microstructure of specimens after hot upsetting experiment was investigated to validate the finite element (FE) prediction model of microstructure evolution. The simulation results were close to the experimental results, which verifies that the established microstructure evolution model has a good predictability. The developed microstructure evolution model and the FE prediction system of hot CWR were used to simulate the forming of HSRA. Hot CWR simulations of HSRA was conducted at rolling temperature of 1000 °C and rolling sped of 1.5 r/min with initial grain size of 200um. Numerical prediction of dynamic recrystallization (DRX) volume fraction and average grain size in three segments of CWR, such as wedge entering segment, widening segment and finishing segment, were carried out to study the microstructure evolution of CWR. Results show that the maximum average grain size locates in the heart of hub axle section, which is smaller than grade 5 and above (d≤62.5μm). It indicates that the microstructure of HSRA after CWR meet the requirements of grain structure grade in international standard of EN13261. Effects of rolling temperatures, rolling speeds and initial grain sizes on the microstructure distribution after hot CWR were analyzed using FE simulations for 40CrNiMo steel. The rolling temperature of 1050 °C, rolling speed of 6 r/min and initial average grain size of less than 200μm are suggested to produce HSRA using CWR. Composite manufacturing processes, i.e. free forging plus hot CWR, are suggested to produce HSRA. The material saving as well as improving production productivity simultaneously become a reality. This provides a guiding reference for the actual production of HSRA.

Application of Hierarchical Clustering Approach for Prediction of Grain Size in Heat-Treated EN9 Steel

One of the simplest, most popular, and productive ways to conduct research and testing in the field of materials science is through the use of metallographic study. Technological boon in the field of metallographic study, opens new gateway for materials characterization through image processing technologies. Image segmentation, edge detection, and roughly estimating grain size are the three main goals of metallographic image processing. The objective of this paper was to determine the grain size of EN9 steel by applying different clustering techniques to the image textured data, collected from EN9 steel metallographic specimens in normalized and annealed condition. In order to determine the average grain size in EN9 steel specimens when seen with a metallurgical microscope, this article blends the ideas of image processing with various hierarchical clustering methodologies to study material characteristics.

Prediction of the Average Grain Size for AA5754 Plates and Sheets by Using Supervised Machine Learning

Prediction of the Average Grain Size for AA5754 Plates and Sheets by Using Supervised Machine Learning

Determination of Grain Growth Kinetics of S960MC Steel.

Fine-grained high-strength low-alloyed (HSLA) steels are used for their advantageous combination of mechanical properties such as high yield strength, tensile strength, ductility, and good formability. These properties are mainly based on applied grain boundary strengthening, which as the only strengthening mechanism allows for the yield strength to increase without a decrease in ductility. Therefore, any changes in grain size lead to irreversible changes in material properties. Such changes also occur during welding in the heat-affected zone (HAZ), where there is a significant change in austenitic grain. In coarse-grain HAZ, this leads to a decrease in yield strength, ductility, toughness, and fatigue strength. The paper experimentally determines the growth kinetics of austenitic grain for fine-grained HSLA steel S960MC. As a result, the values of the activation energy required for grain growth Q and the proportional constant K0 are determined. Knowing these values is important for numerical predictions of austenitic grain size in the HAZ. Based on these predictions, the changes in yield strength, ductility, toughness, and fatigue strength can be estimated.

Quantitative Prediction of Low-Permeability Sandstone Grain Size Based on Conventional Logging Data by Deep Neural Network-Based BP Algorithm

The median grain size of rock is the main basis for the identification of sedimentary facies, and the variation of the median grain size of rock can be used to obtain the stratum sedimentary rhythm and thus to classify the flow unit. Therefore, the median grain size of rock is an extremely important parameter for reservoir evaluation. However, there is no petrophysical method that can directly evaluate the median grain size of rock in the logging data. The predecessors used natural gamma logging data to calculate the median rock grain size (Md) based on linear and statistical analysis for medium-high porosity and permeability sandstone reservoirs work. However, for low-permeability sandstone reservoirs, the error in the fitted median grain size of rock using linear multiple regression methods is too large for the calculated results to be applied. Therefore, the calculation of the median grain size of low-permeability sandstone reservoirs is a difficult problem to be solved. In this paper, the sensitivity logging parameters of median rock grain size are optimized for low permeability sandstone reservoirs using principal component analysis obtained the grain size direction correlation curves (DEN, CNL,GR, and RD) in the study area, and the corresponding loss and activation functions are selected based on the learning characteristics of the nonlinear mapping of the logging data and the BP neural network to ensure that overfitting occurs. The best model was obtained by using decision tree, support vector machine, shallow and deep neural networks to model the median rock grain size and predict neighboring wells, and a comparative analysis showed that for the problem of predicting the median rock grain size in low-permeability sandstone reservoirs, the deep neural network improved significantly over the shallow one and was much stronger than other machine learning methods. The best model obtained a coefficient of determination ( R 2 ) of 0.9831. Machine learning of median grain size from conventional logging data was systematically carried out through conventional logging sensitivity curve optimization, algorithm modeling, network parameter optimization, median grain size prediction, and validation, and the relative error in its quantitative prediction met application requirements. This method takes into account the nonlinear mapping relationship between the logging data and the fitting of small sample data and provides a systematic way of thinking for the logging curve to predict the grain size of low-permeability sandstone.

Modeling and Prediction of Grain Size and Hardness of ZE41/ZrO$$_2$$ Nano-surface Composite Using Multiple Regression, Power Law and Artificial Intelligence Techniques

Modeling and Prediction of Grain Size and Hardness of ZE41/ZrO$$_2$$ Nano-surface Composite Using Multiple Regression, Power Law and Artificial Intelligence Techniques

Prediction of Grain Size in Cast Aluminum Alloys

Grain refinement of cast alloys, especially aluminum–silicon and magnesium-based alloys, is an effective approach to improve the strength of alloys. Grain size is the most representative parameter used to characterize grain refinement in the industry, thereby attracting increasing attention for developing accurate grain size prediction models. In this paper, several important grain size prediction models under different adaptation conditions are reviewed. These models are obtained either by regression of experimental data or by physical/mathematical inference under certain assumptions of specified cases, focusing on the effects of alloy composition, solidification temperature gradient, grain growth rate, and fining agent composition, among others. The trends of grain size prediction models were also discussed. The results revealed machine learning as an effective tool to establish a data-driven prediction model of grain size in cast aluminum alloys.

Continuous, High-Resolution Mapping of Coastal Seafloor Sediment Distribution

Seafloor topography and grain size distribution are pivotal features in marine and coastal environments, able to influence benthic community structure and ecological processes at many spatial scales. Accordingly, there is a strong interest in multiple research disciplines to obtain seafloor geological and/or habitat maps. The aim of this study was to provide a novel, automatic and simple model to obtain high-resolution seafloor maps, using backscatter and bathymetric multibeam system data. For this purpose, we calibrated a linear regression model relating grain size distribution values, extracted from samples collected in a 16 km2 area near Bagnoli–Coroglio (southern Italy), against backscatter and depth-derived covariates. The linear model achieved excellent goodness-of-fit and predictive accuracy, yielding detailed, spatially explicit predictions of grain size. We also showed that a ground-truth sample size as large as 40% of that considered in this study was sufficient to calibrate analogous regression models in different areas. Regardless of some limitations (i.e., inability to predict rocky outcrops and/or seagrass meadows), our modeling approach proved to be a flexible tool whose main advantage is the rendering of a continuous map for sediment size, in lieu of categorical mapping approaches which usually report sharp boundaries or rely on a few sediment classes.

Prediction of Grain Size in a High Cobalt Nickel-Base Superalloy

Prediction of Grain Size in a High Cobalt Nickel-Base Superalloy

Development of a digital twin of the process of controlled rolling of thick plate from high-strength low-alloy steels. Message 1. Simulation of slab reheating in continuouos furnace with a prediction of austenite grain size before rolling

Development of a digital twin of the process of controlled rolling of thick plate from high-strength low-alloy steels. Message 1. Simulation of slab reheating in continuouos furnace with a prediction of austenite grain size before rolling

Numerical investigation and prediction of grain size in different friction stir welding areas of AA6061 aluminum alloy

Friction stir welding is a solid-state bonding technique based on two factors, a pressure greater than the material yield stress and large plastic deformation. The final joint between parts takes place in different welding geometries by using these two factors. The grain sizes in the weld areas are influenced by recovery, recrystallization, and grain growth. In the current research, using an analytical-numerical method based on finite element simulation of friction stir welding of AA6061-T6 alloy, the grain sizes in various welded areas have been predicted. The finite element simulation has been performed based on the coupled Eulerian-Lagrangian approach using ABAQUS software and the results have been verified by experiments. The predicted results were in good agreement with the experimental results. Due to the concentration of heat and plastic flow in the central region of the weld, the stirring region had the most microstructural changes and the grain size in this region decreased more sharply than other different areas of the weld. As the rotational welding speed increases, recrystallization phenomena in the central weld area increases. With increasing the translational welding speed, grain size increased in different welding areas. This increase occurred more severely in the central area of the welded joint.

A unified mechanistic model for Hall–Petch and inverse Hall–Petch relations of nanocrystalline metals based on intragranular dislocation storage

Nanocrystalline (NC) metals often show transition from Hall–Petch (HP) strengthening to inverse HP softening as the average grain size decreases below a critical value. Compared to the HP behavior whose mechanism is well understood as the dislocation pile-up at grain boundaries (GBs), several hypotheses have been proposed to explain the veiled inverse HP behavior and no consensus has been reached yet. In this work, we propose a size-dependent model considering the influence of grain size on the intragranular dislocation storage ability and unify the HP and inverse HP relations for NC metals. The reduction of the intragranular dislocation storage ability with decreasing grain size is revealed as the underlying mechanism of the breakdown of the HP behavior in NC. Prediction of the critical grain size of 26.9nm for the HP-inverse HP transition of NC copper agrees well with experimental results. Numerical results suggest that the harmonized deformation of GB and grain interior (GI) dominates the remarkable ductility enhancement of NC metals in the inverse HP region. Moreover, our results suggest that the formation of dimple structures spanning several grains at fracture surfaces of NC metals is attributed to the coalescence of inter- and intra-granular microcracks and microvoids in clustered grains with Goss texture in local shear bands. As the GB strength increases, NC metals show enhancement in yield strength and delay in occurrence of the inverse HP behavior. Our studies give new insight into the contribution of GB sliding to the plastic behaviors of NC metals, and provide valuable guidance for the rational design of NC metals with high ductility and high strength.

A cellular automaton approach for the prediction of grain size in grain refined alloys

Grain refinement by inoculation is widely practised in metal casting, in particular for aluminium alloys. During the last decades several modelling techniques have emerged to quantitatively predict solidification microstructures. The prediction of grain size remains however a challenge due to the complex competition between nucleation and growth, and physical phenomena taking place at very different length scales. Models often address the thermal recalescence and sometimes the suppression of nucleation by the solute boundary layers. The possible clustering tendency of the nucleant particles is however usually not considered, although it can play an important influence on the final grain size. This effect is particularly difficult to quantify because it requires a small-scale approach to describe the clusters, while a large computational domain is needed for grain size statistics. A cellular automaton (CA) model tracking the grain envelopes was developed to address this challenge. The spatial distribution of the nucleant particles and the solute diffusion field in the intergranular liquid were represented directly on the CA grid. Predicted grain sizes were first compared successfully with published experimental and modelling data. An investigation of the role of clustering of nucleant particles was then carried out, which demonstrated the potential of the model to address this topic.

Prediction of Shortening and Material Grain Size after Extrusion Using the ECAP Method

Equal channel angular pressing (ECAP) is a widespread severe plastic deformation (SPD) method to fabricate ultrafine-grained bulk materials. In the field of materials engineering, this method has already experienced rapid development over the past few decades. In this research, the authors sought to create a prediction of shortening and the material particle size after extrusion using ECAP. Behaviours of essential functions are analysed here on samples of pure copper Cu 99.9. It is the measurement and analytical processing of changes in the values of selected structural and mechanical parameters depending on the reduction of the structural granularity. Parameters such as deformation speed, deformation work and ECAP mechanical performance are also included in the results. The change in structure and mechanical parameters is also newly demonstrated by measuring the change in the velocity of the longitudinal ultrasound wave during the experimental passes. Based on the results obtained, new computational algorithms for Excel and Matlab were developed. The algorithms developed here contain many new findings, conclusions and derivations addressing the integrity of the surface with the inner structure of materials. Algorithms are very well-suited for obtaining results on different materials quickly, for predicting and checking measured values, and for designing optimal measurement technology parameters for the ECAP method.