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Metals
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Characterization and Modeling of Microstructure Evolution During Metallic Material Processing
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Characterization and Modeling of Microstructure Evolution During Metallic Material Processing

A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Metal Casting, Forming and Heat Treatment".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 6582

Special Issue Editor

Prof. Dr. Gang Fang

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
Interests: metal forming; ductile damage and fracture; elastocaloric cooling of shape memory alloys; additive manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Microstructure regulation is one of the important goals of metal material processing. At present, with the continuous improvement of new detection methods and instruments, experimental methods can more clearly and accurately characterize the evolution of microstructure in material processing. At the same time, with the development of computational materials science, many new methods have emerged for the simulation and prediction of microstructure evolution. This Special Issue welcomes original research articles and reviews on, but not limited to, the following topics:

1）Advanced experimental techniques for microstructure characterization (EBSD, TEM, in situ observation, etc.);

2）Computational modeling of microstructural evolution, including phase-field, cellular automaton, Monte Carlo, and crystal plasticity methods;

3）Multiscale approaches bridging experimental observations and numerical simulations;

4）Influence of thermomechanical processing on phase transformation, grain growth, recrystallization, and precipitation;

5）Data-driven and machine learning techniques for microstructure prediction.

Prof. Dr. Gang Fang
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 250 words) can be sent to the Editorial Office for assessment.

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. Metals is an international peer-reviewed open access monthly 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

  • microstructure evolution
  • material processing
  • characterization
  • modeling
  • phase transformation
  • grain growth
  • recrystallizion
  • crystal plasticity

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Published Papers (4 papers)

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Research

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13 pages, 5931 KB  
Article
Tailoring Thermal Conductivity and Strength of Al-Si-Fe Alloys via Cu Micro-Alloying: Mechanisms and Modeling
by Yuli Zhou, Huilin Zhang, Yuxin Chen, Fan Li, Cai Chen, Mohammed El Ganaoui, Hélène Elias-Birembaux, Mourad Khelifa, Shuai Zhang, Peijian Wang and Dunming Liao
Metals 2026, 16(5), 501; https://doi.org/10.3390/met16050501 - 3 May 2026
Viewed by 411
Abstract
The influence of Cu content on the thermal conductivity and mechanical properties of Al-9Si-0.7Fe casting alloy were investigated in this paper. The results show that as the Cu content increases from 0.1 wt.% to 2.0 wt.%, the thermal conductivity of the alloy decreases [...] Read more.
The influence of Cu content on the thermal conductivity and mechanical properties of Al-9Si-0.7Fe casting alloy were investigated in this paper. The results show that as the Cu content increases from 0.1 wt.% to 2.0 wt.%, the thermal conductivity of the alloy decreases from 173.6 W/(m·K) to 154.8 W/(m·K), while the yield strength increases from 72.2 MPa to 90.9 MPa. Metallographic, XRD, and EPMA analyses revealed that Cu has a relatively small impact on the secondary dendrite arm spacing of α-Al and the morphology of eutectic silicon. Its influence on the thermal conductivity and mechanical properties primarily stems from Cu atoms dissolving in the α-Al matrix, leading to a decreased lattice constant, increased lattice distortion, enhanced electron scattering, and improved solid solution strengthening effect. Based on the measured solubility of Cu, the Maxwell and Hashin–Shtrikman thermal conductivity models were modified. The correlation coefficients between the predicted values of the modified models and the experimental data were 92.77% and 93.11%, respectively, indicating a significant improvement in prediction accuracy. Full article
(This article belongs to the Special Issue Characterization and Modeling of Microstructure Evolution During Metallic Material Processing)
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17 pages, 5074 KB  
Article
Dynamic Recrystallization and Microstructural Evolution During Hot Deformation of Al-Cu-Mg Alloy
by Fangyan He, Xiaolan Wu, Zhizheng Rong, Xueqin Zhang, Xiangyuan Xiong, Shengping Wen, Kunyuan Gao, Wu Wei, Li Rong, Hui Huang and Zuoren Nie
Metals 2025, 15(10), 1100; https://doi.org/10.3390/met15101100 - 1 Oct 2025
Cited by 3 | Viewed by 1839
Abstract
Isothermal hot compression tests were performed on an Al-4.8Cu-0.25Mg-0.32Mn-0.17Si alloy using a Gleeble-3500 thermomechanical simulator within the temperature range of 350–510 °C and strain rate range of 0.001–10 s−1, achieving a true strain of 0.9. The constitutive equation and hot processing [...] Read more.
Isothermal hot compression tests were performed on an Al-4.8Cu-0.25Mg-0.32Mn-0.17Si alloy using a Gleeble-3500 thermomechanical simulator within the temperature range of 350–510 °C and strain rate range of 0.001–10 s−1, achieving a true strain of 0.9. The constitutive equation and hot processing maps were established to predict the flow behavior of the alloy. The hot deformation mechanisms were investigated through microstructural characterization using inverse pole figure (IPF), grain boundary (GB), and grain orientation spread (GOS) analysis. The results demonstrate that both dynamic recovery (DRV) and dynamic recrystallization (DRX) occur during hot deformation. At high lnZ values (high strain rates and low deformation temperatures), discontinuous dynamic recrystallization (DDRX) dominates. Under middle lnZ conditions (low strain rate or high deformation temperature), both continuous dynamic recrystallization (CDRX) and DDRX are the primary mechanisms. Conversely, at low lnZ values (low strain rates and high temperatures), CDRX and geometric dynamic recrystallization (GDRX) become predominant. The DRX process in the Al-Cu-Mg alloy is controlled by the deformation temperature and strain rate. Full article
(This article belongs to the Special Issue Characterization and Modeling of Microstructure Evolution During Metallic Material Processing)
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45 pages, 2796 KB  
Article
A Simulation-Based Comparative Analysis of Physics and Data-Driven Models for Temperature Prediction in Steel Coil Annealing
by Ján Kačur, Patrik Flegner, Milan Durdán and Marek Laciak
Metals 2025, 15(9), 932; https://doi.org/10.3390/met15090932 - 22 Aug 2025
Viewed by 1705
Abstract
Annealing of steel coils in bell-type furnaces is a critical process in steel production, requiring precise temperature control to ensure desired mechanical properties and microstructure. However, direct measurement of inner coil temperatures is impractical in industrial conditions, necessitating model-based estimation. This study presents [...] Read more.
Annealing of steel coils in bell-type furnaces is a critical process in steel production, requiring precise temperature control to ensure desired mechanical properties and microstructure. However, direct measurement of inner coil temperatures is impractical in industrial conditions, necessitating model-based estimation. This study presents a comparative analysis of physics-based and machine learning (ML) approaches for predicting internal temperatures during annealing. A finite difference method (FDM) was developed as a physics-based model and validated against experimental data from both laboratory and industrial annealing cycles. Furthermore, several ML models, including support vector regression (SVR), neural networks (NN), multivariate adaptive regression splines (MARS), k-nearest neighbors (k-NN), and random forests (RFs), were trained on surface temperature measurements to predict inner temperatures. The results demonstrate that the MARS, k-NN, and RF models achieved high prediction accuracy with performance index (PI) values below 1.0 on unseen data, demonstrating excellent generalization capabilities. In contrast, SVR with polynomial kernels and NN exhibited poor performance in specific configurations, highlighting their sensitivity to overfitting and data variability. The findings suggest that combining physics-based models with data-driven techniques offers a robust framework for soft-sensing in annealing operations, enabling improved process monitoring and control. Full article
(This article belongs to the Special Issue Characterization and Modeling of Microstructure Evolution During Metallic Material Processing)
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Review

Jump to: Research

44 pages, 2586 KB  
Review
Cellular Automata and Phase-Field Modeling of Microstructure Evolution in Metal Additive Manufacturing: Recent Advances, Hybrid Frameworks, and Pathways to Predictive Control
by Łukasz Łach
Metals 2026, 16(1), 124; https://doi.org/10.3390/met16010124 - 21 Jan 2026
Cited by 1 | Viewed by 2109
Abstract
Metal additive manufacturing (AM) generates complex microstructures through extreme thermal gradients and rapid solidification, critically influencing mechanical performance and industrial qualification. This review synthesizes recent advances in cellular automata (CA) and phase-field (PF) modeling to predict grain-scale microstructure evolution during AM. CA methods [...] Read more.
Metal additive manufacturing (AM) generates complex microstructures through extreme thermal gradients and rapid solidification, critically influencing mechanical performance and industrial qualification. This review synthesizes recent advances in cellular automata (CA) and phase-field (PF) modeling to predict grain-scale microstructure evolution during AM. CA methods provide computational efficiency, enabling large-domain simulations and excelling in texture prediction and multi-layer builds. PF approaches deliver superior thermodynamic fidelity for interface dynamics, solute partitioning, and nonequilibrium rapid solidification through CALPHAD coupling. Hybrid CA–PF frameworks strategically balance efficiency and accuracy by allocating PF to solidification fronts and CA to bulk grain competition. Recent algorithmic innovations—discrete event-inspired CA, GPU acceleration, and machine learning—extend scalability while maintaining predictive capability. Validated applications across Ni-based superalloys, Ti-6Al-4V, tool steels, and Al alloys demonstrate robust process–microstructure–property predictions through EBSD and mechanical testing. Persistent challenges include computational scalability for full-scale components, standardized calibration protocols, limited in situ validation, and incomplete multi-physics coupling. Emerging solutions leverage physics-informed machine learning, digital twin architectures, and open-source platforms to enable predictive microstructure control for first-time-right manufacturing in aerospace, biomedical, and energy applications. Full article
(This article belongs to the Special Issue Characterization and Modeling of Microstructure Evolution During Metallic Material Processing)
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