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Metals
Special Issues
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 December 2025 | Viewed by 416

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 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. 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 (1 paper)

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Research

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 256
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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