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Phase Transformation Theory and Microstructure Simulation of Alloys
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Phase Transformation Theory and Microstructure Simulation of Alloys

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Materials Simulation and Design".

Deadline for manuscript submissions: closed (20 October 2023) | Viewed by 4919

Special Issue Editor

Dr. Ang Zhang

E-Mail Website
Guest Editor
College of Materials Science and Engineering, Chongqing University, Chongqing, China
Interests: phase transformation; microstructure; modeling and simulation; alloys

Special Issue Information

Dear Colleagues,

Phase transformation occurs in a large variety of alloys subjected to a change in temperature or pressure. The transformation process is governed primarily by the free-energy difference between the parent phase and potential new phases. The corresponding phase transformation theory has gone a long way in the past few centuries, offering an extraordinary guide for the optimization of material properties. The new chemical composition strategies and the new technologies of casting, metal forming, heat treatment, and additive manufacturing allow us to obtain modern alloy products which satisfy the needs of the present industry. The development of phase transformation theory is directly related to a continuous progress in microstructure simulation techniques, which benefits the better establishment of microstructure–processing–properties relationships. The successful identification of multiphase microstructure and thermodynamic evolution requires the promotion and application of phase transformation theory and simulation techniques, which enriches the design, optimization, and operation of alloys and lays the foundation for material discovery.

This Special Issue aims to cover recent progress and new developments in the phase transformation theory and microstructure simulation of alloys. All aspects related to phase transformation (e.g., solidification, heat treatment, and thermomechanical processing), physical and numerical simulation, and related structural characterization are covered. Review articles which describe the current state of the art are also welcomed.

Dr. Ang Zhang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Materials is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • phase transformation
  • microstructure
  • modeling and simulation
  • alloys

Published Papers (5 papers)

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Research

13 pages, 6852 KiB  
Article
Simulation of Abnormal Grain Growth Using the Cellular Automaton Method
by Kenji Murata, Chihiro Fukui, Fei Sun, Ta-Te Chen and Yoshitaka Adachi
Materials 2024, 17(1), 138; https://doi.org/10.3390/ma17010138 - 27 Dec 2023
Viewed by 645
Abstract
The abnormal grain growth of steel, which is occurs during carburization, adversely affects properties such as heat treatment deformation and fatigue strength. This study aimed to control abnormal grain growth by controlling the materials and processes. Thus, it was necessary to investigate the [...] Read more.
The abnormal grain growth of steel, which is occurs during carburization, adversely affects properties such as heat treatment deformation and fatigue strength. This study aimed to control abnormal grain growth by controlling the materials and processes. Thus, it was necessary to investigate the effects of microstructure, precipitation, and heat treatment conditions on abnormal grain growth. We simulated abnormal grain growth using the cellular automaton (CA) method. The simulations focused on the grain boundary anisotropy and dispersion of precipitates. We considered the effect of grain boundary misorientation on boundary energy and mobility. The dispersion state of the precipitates and its pinning effect were considered, and grain growth simulations were performed. The results showed that the CA simulation reproduced abnormal grain growth by emphasizing the grain boundary mobility and the influence of the dispersion state of the precipitate on the occurrence of abnormal grain growth. The study findings show that the CA method is a potential technique for the prediction of abnormal grain growth. Full article
(This article belongs to the Special Issue Phase Transformation Theory and Microstructure Simulation of Alloys)
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14 pages, 7451 KiB  
Article
Effect of Forced Convection on Magnesium Dendrite: Comparison between Constant and Altering Flow Fields
by Lang Qin, Ang Zhang, Jinglian Du, Zhihua Dong, Feng Liu and Bin Jiang
Materials 2023, 16(24), 7695; https://doi.org/10.3390/ma16247695 - 18 Dec 2023
Viewed by 612
Abstract
Convection has a nonnegligible effect on the growth of the magnesium dendrite with six-primary-branch pattern. Most work, however, investigates the effect of the convection by simplifying the melt flow as a constant horizontal flow. In this work, four convection behaviors, including equally distributed [...] Read more.
Convection has a nonnegligible effect on the growth of the magnesium dendrite with six-primary-branch pattern. Most work, however, investigates the effect of the convection by simplifying the melt flow as a constant horizontal flow. In this work, four convection behaviors, including equally distributed convection, linearly distributed convection, sinusoidal-wave convection, and square-wave convection, are imposed and simulated through the phase-field lattice-Boltzmann schemes. The effects of constant (the former two) and altering (the latter two) flow fields are quantified by the length ratio of the upstream primary arm to the downstream one. The results show that the dendrite asymmetry increases under the constant forced convections but presents nonmonotonic change under the altering convections. A simple mathematical relation is fitted to summarize the dependence of the dendrite asymmetry on the input velocity, the undercooling, and the flow frequency. Deep understanding of the convection effects can guide the prediction and control of the magnesium dendrite under more complex situations. Full article
(This article belongs to the Special Issue Phase Transformation Theory and Microstructure Simulation of Alloys)
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16 pages, 5823 KiB  
Article
Two- and Three-Dimensional Modeling and Simulations of Grain Growth Behavior in Dual-Phase Steel Using Monte Carlo and Machine Learning
by Fei Sun, Ayano Kita, Toshio Ogawa, Ta-Te Chen and Yoshitaka Adachi
Materials 2023, 16(24), 7536; https://doi.org/10.3390/ma16247536 - 06 Dec 2023
Viewed by 688
Abstract
Dual-phase (DP) steel has been widely used in automotive steel plates with a balance of excellent strength and ductility. Grain refinement in DP steel is important to improve the properties further; however, the factors affecting grain growth need to be well understood. The [...] Read more.
Dual-phase (DP) steel has been widely used in automotive steel plates with a balance of excellent strength and ductility. Grain refinement in DP steel is important to improve the properties further; however, the factors affecting grain growth need to be well understood. The remaining problem is that acquiring data through experiments is still time-consuming and difficult to evaluate quantitatively. With the development of materials informatics in recent years, material development time and costs are expected to be significantly reduced through experimentation, simulation, and machine learning. In this study, grain growth behavior in DP steel was studied using two-dimensional (2D) and three-dimensional (3D) Monte Carlo modeling and simulation to estimate the effect of some key parameters. Grain growth can be suppressed when the grain boundary energy is greater than the phase boundary energy. When the volume fractions of the matrix and the second phase were equal, the suppression of grain growth became obvious. The long-distance diffuse frequency can promote grain growth significantly. The simulation results allow us to better understand the factors affecting grain growth behavior in DP steel. Machine learning was performed to conduct a sensitivity analysis of the affecting parameters and estimate the magnitude of each parameter’s effects on grain growth in the model. Combining MC simulation and machine learning will provide one promising research strategy to gain deeper insights into grain growth behaviors in metallic materials and accelerate the research process. Full article
(This article belongs to the Special Issue Phase Transformation Theory and Microstructure Simulation of Alloys)
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23 pages, 14792 KiB  
Article
Evaluation of Austenite–Ferrite Phase Transformation in Carbon Steel Using Bayesian Optimized Cellular Automaton Simulation
by Fei Sun, Yoshihisa Mino, Toshio Ogawa, Ta-Te Chen, Yukinobu Natsume and Yoshitaka Adachi
Materials 2023, 16(21), 6922; https://doi.org/10.3390/ma16216922 - 28 Oct 2023
Cited by 1 | Viewed by 1153
Abstract
Austenite–ferrite phase transformation is a crucial metallurgical tool to tailor the properties of steels required for particular applications. Extensive simulation and modeling studies have been conducted to evaluate the phase transformation behaviors; however, some fundamental physical parameters still need to be optimized for [...] Read more.
Austenite–ferrite phase transformation is a crucial metallurgical tool to tailor the properties of steels required for particular applications. Extensive simulation and modeling studies have been conducted to evaluate the phase transformation behaviors; however, some fundamental physical parameters still need to be optimized for better understanding. In this study, the austenite–ferrite phase transformation was evaluated in carbon steels with three carbon concentrations during isothermal annealing at various temperatures using a developed cellular automaton simulation model combined with Bayesian optimization. The simulation results show that the incubation period for nucleation is an essential factor that needs to be considered during austenite–ferrite phase transformation simulation. The incubation period constant is mainly affected by carbon concentration and the optimized values have been obtained as 10−24, 10−19, and 10−21 corresponding to carbon concentrations of 0.2 wt%, 0.35 wt%, and 0.5 wt%, respectively. The average ferrite grain size after phase transformation completion could decrease with the decreasing initial austenite grain size. Some other parameters were also analyzed in detail. The developed cellular automaton simulation model combined with Bayesian optimization in this study could conduct an in-depth exploration of critical and optimal parameters and provide deeper insights into understanding the fundamental physical characteristics during austenite–ferrite phase transformation. Full article
(This article belongs to the Special Issue Phase Transformation Theory and Microstructure Simulation of Alloys)
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14 pages, 2129 KiB  
Article
Influence of the Composition and Vacancy Concentration on Cluster Decomposition Behavior in Al–Si–Mg Alloy: A Kinetic Monte Carlo Study
by Sangjun Lee, Heon Kang, Jonggyu Jeon and Donghyun Bae
Materials 2022, 15(19), 6552; https://doi.org/10.3390/ma15196552 - 21 Sep 2022
Cited by 1 | Viewed by 1315
Abstract
The influence of cluster composition and the addition of vacancies on the decomposition behavior of clusters during artificial aging in Al–Si–Mg alloys were analyzed according to the kinetic Montel Carlo model. Clusters with a balanced composition (Mg/(Mg + Si) = 0.5) were the [...] Read more.
The influence of cluster composition and the addition of vacancies on the decomposition behavior of clusters during artificial aging in Al–Si–Mg alloys were analyzed according to the kinetic Montel Carlo model. Clusters with a balanced composition (Mg/(Mg + Si) = 0.5) were the most difficult to decompose. In addition, the cluster decomposition was slower when more vacancies were added to the cluster. Among Si, Mg, and vacancies, vacancies most significantly affect decomposition. The clusters with Mg/(Mg + Si) ≤ 0.4 strongly trap vacancies, which can be classified as hardly decomposable vacancy-rich clusters. The clustering behavior during natural aging and the effect of pre-aging were analyzed using the Kinetic Monte Carlo model. Pre-aging slows down cluster formation due to the lowered vacancy concentration. In addition, the overall composition of the clusters changes to easily decomposable clusters after pre-aging. Thus, not only is the number of clusters reduced but also the clusters are more easily decomposable when pre-aging is performed. Full article
(This article belongs to the Special Issue Phase Transformation Theory and Microstructure Simulation of Alloys)
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