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Microstructure Control and Process Optimization of Advanced Metallic Materials
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Microstructure Control and Process Optimization of Advanced Metallic Materials

A Topical Collection in Materials (ISSN 1996-1944) belonging to the section "Metals and Alloys".

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Editors

Prof. Dr. Yong-Cheng Lin

E-Mail Website
Collection Editor
School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China
Interests: alloys; intelligent manufacturing processing; heat treatment; characterization & modeling; hot deformation mechanisms; microstructure; properties
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yong-Chang Liu

E-Mail Website
Collection Editor
School of Materials Science and Engineering, Tianjin University, Tianjin 30072, China
Interests: alloys; metallurgical engineering; heat treatment; thermodynamic modeling; microstructure; properties
Dr. Guan Liu

E-Mail Website
Collection Editor
School of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China
Interests: alloys; metal forming; additive manufacturing; machine learning; heat treatment; microstructure; properties
Special Issues, Collections and Topics in MDPI journals

Topical Collection Information

Dear Colleagues,

Advanced metallic materials—including high-strength steels, titanium alloys, aluminum–lithium alloys and refractory metals—serve as the backbone of aerospace, energy, transportation and biomedical engineering. Over the past decade, the demand for lightweight, high-performance and sustainable metallic components has driven intensive research into microstructure–property relationships. However, achieving precise control over microstructure evolution (e.g., grain refinement, precipitation behavior, phase transformation and defect engineering) while balancing mechanical performance, manufacturability and cost remains a critical challenge.

This Topical Collection, “Microstructure Control and Process Optimization of Advanced Metallic Materials,”​ aims to provide a comprehensive forum for cutting-edge research on the fundamental mechanisms governing microstructural tailoring and the translation of these insights into industrial process optimization. We welcome contributions addressing both experimental and theoretical approaches, including but not limited to:

  • Fundamental Mechanisms:​ Phase transformations, recrystallization kinetics, precipitation, interface engineering, etc.
  • Advanced Processing Technologies:​ Thermomechanical processing, additive manufacturing, severe plastic deformation, welding and hybrid manufacturing routes.
  • Smart Manufacturing & Digital Twins:​ Data-driven process control, real-time monitoring of microstructure evolution, closed-loop quality control systems and the application of machine learning in the fabrication of advanced metallic components.
  • Characterization & Modeling:​ In situ characterization techniques, multiscale modeling (from atomic to component level) and machine learning–guided design.
  • Property Optimization:​ Balancing strength–ductility trade-offs, fatigue resistance, corrosion performance, high-temperature stability, etc.
  • Sustainability:​ Energy-efficient processing, recycling of strategic metals and eco-design of alloys.

We invite original research articles and comprehensive reviews that bridge laboratory-scale findings with industrial applications. Submissions focusing on emerging areas—such as advanced steels, titanium alloys, aluminum alloys, high-entropy alloys, amorphous metals and refractory metals —are especially encouraged.

This Collection supports open-access dissemination to maximize global impact. Manuscripts should be submitted via the Materials online system. Accepted papers will be published immediately upon acceptance and listed together on the Collection homepage.

We look forward to your contributions to this timely and interdisciplinary dialog.

Prof. Dr. Yong-Cheng Lin
Prof. Dr. Yong-Chang Liu
Dr. Guan Liu
Collection Editors

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. 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 collection 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-anonymized 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

  • microstructure evolution
  • process optimization
  • smart manufacturing
  • metal forming
  • additive manufacturing
  • machine learning
  • mechanical properties
  • high-entropy alloys
  • in situ characterization
  • thermomechanical processing

Published Papers (3 papers)

Download All Papers

2026

18 pages, 9929 KB  
Article
Precision Compensation and Annealing Process Exploration for Near-Net Cold Forming of Ta-2.5W Shaped Charge Liners
by Tingjun Cai, Haicheng Shi, Wentai Zhao, Bowen Pan, Hao Wu, Guiqian Xiao, Liming Gong and Guozheng Quan
Materials 2026, 19(17), 3737; https://doi.org/10.3390/ma19173737 - 2 Sep 2026
Viewed by 135
Abstract
Ta-2.5W alloy is a promising liner material for high-performance shaped-charge warheads because of its high density and excellent dynamic mechanical properties. However, conventional machining and hot-forming routes suffer from low material utilization, limited dimensional accuracy, and oxidation-related defects. In this study, near-net-shape cold [...] Read more.
Ta-2.5W alloy is a promising liner material for high-performance shaped-charge warheads because of its high density and excellent dynamic mechanical properties. However, conventional machining and hot-forming routes suffer from low material utilization, limited dimensional accuracy, and oxidation-related defects. In this study, near-net-shape cold pressing and annealing treatments were investigated for Ta-2.5W liners. The initial microstructure and mechanical properties of the starting sheet were characterized, compression tests were performed to establish a room-temperature constitutive model, and 16 combinations of deformation and annealing temperature were designed to clarify the evolution of grain morphology and crack sensitivity. To compensate for elastic die deformation and blank springback, a coupled simulation-based die correction strategy was further developed. The results show that the starting alloy exhibits an excellent strength–ductility balance with weak anisotropy. Increasing cold deformation refines the grains, whereas increasing annealing temperature initially promotes grain refinement but subsequently causes grain coarsening. Excessive deformation combined with high annealing temperature increases crack susceptibility. Based on the single-specimen screening experiments in this study, a preliminary processing range of 20–40% cold deformation and 1200 °C annealing produced the most favorable microstructural condition without obvious cracking. After iterative die compensation, trial-manufactured parts satisfied the target contour requirements and showed uniform, crack-free microstructures after annealing. Full article
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18 pages, 22036 KB  
Article
A Comparative Study on Microstructure and Mechanical Properties of Ti-6Al-4V Fabricated by Laser/Electron Beam Powder Bed Fusion
by Yaojia Ren, Jingru Wang, Jiajun Xu, Yingkang Wei, Jilei Zhu, Qingge Wang, Jianyong Wang, Shifeng Liu and Solomon-Oshioke Agbedor
Materials 2026, 19(15), 3300; https://doi.org/10.3390/ma19153300 - 4 Aug 2026
Viewed by 400
Abstract
To address the strength–ductility trade-off in titanium alloys, a comparative study was conducted on Ti-6Al-4V (TC4) alloys fabricated by laser powder bed fusion (L-PBF) and electron beam powder bed fusion (EB-PBF). The L-PBF specimen primarily consisted of acicular α′ martensite with high residual [...] Read more.
To address the strength–ductility trade-off in titanium alloys, a comparative study was conducted on Ti-6Al-4V (TC4) alloys fabricated by laser powder bed fusion (L-PBF) and electron beam powder bed fusion (EB-PBF). The L-PBF specimen primarily consisted of acicular α′ martensite with high residual stress. In contrast, the EB-PBF specimens, owing to a substrate preheating temperature of 740 °C and a reduced cooling rate (103~105 K/s), exhibited a stable and coarse α + β lamellar structure. Combined with the high oxygen content (0.24 wt.%) that provided solid-solution strengthening, this morphology enabled simultaneous attainment of a yield strength of 1120 ± 12 MPa and an elongation at fracture of 11.1 ± 1.3%. Notably, deformation-induced HCP→FCC phase transformation occurred in EB-PBF alloys, generating a dual-phase HCP/FCC structure that effectively accommodated plastic strain. These results highlight the superior potential of EB-PBF over L-PBF for fabricating titanium alloys with an exceptional strength–ductility synergy. Full article
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24 pages, 8421 KB  
Article
Enhancing Constitutive Description of 5A06 Aluminum Alloy During Warm Deformation Using Machine Learning-Assisted Johnson–Cook Model
by Zhao Liu, Lei Deng, Jinchuan Long, Chang Gao, Yi Hao, Pan Gong, Xuefeng Tang and Xinyun Wang
Materials 2026, 19(14), 2987; https://doi.org/10.3390/ma19142987 - 10 Jul 2026
Viewed by 439
Abstract
To accurately characterize the warm deformation behavior and workability of the 5A06 aluminum alloy, this study presents an innovative workflow that develops and systematically validates machine learning-assisted Johnson–Cook (ML-JC) frameworks based on artificial neural network (ANN) surrogate models. Two predictive frameworks—the parallel-decoupled PD-ANN-JC [...] Read more.
To accurately characterize the warm deformation behavior and workability of the 5A06 aluminum alloy, this study presents an innovative workflow that develops and systematically validates machine learning-assisted Johnson–Cook (ML-JC) frameworks based on artificial neural network (ANN) surrogate models. Two predictive frameworks—the parallel-decoupled PD-ANN-JC and the multi-objective integrated MOI-ANN-JC—were constructed. Quantitatively, both developed ML-JC frameworks achieve significantly higher stress prediction accuracy and superior generalization capability compared with the conventional JC model. Specifically, on the testing set, the MOI-ANN-JC framework yields an average absolute relative error (AARE) of 1.424% and an R2 of 0.997, outperforming the PD-ANN-JC framework (AARE of 3.246%, R2 of 0.988). On the validation set, the MOI-ANN-JC framework also demonstrates exceptional generalization, with an AARE of 3.302% and an R2 of 0.987. Scientifically, the superior performance of the MOI-ANN-JC framework stems from its ANN-mnδ surrogate model, which simultaneously predicts the strain hardening exponent n, thermal softening exponent m, and relative error δ directly from deformation parameters. This mutual coupling establishes an intrinsic correlation between m and n, successfully aligning with the physical reality wherein strain hardening and thermal softening are inherently linked during deformation. Qualitatively and practically, by integrating the MOI-ANN-JC framework into finite element (FE) simulation software, dynamic tracking and visualization of the thermal softening exponent m during warm deformation were achieved. Combined with FE simulations, Vickers hardness testing and EBSD observations, this study successfully establishes a direct qualitative spatial correspondence between low-m regions and macroscopic defects, which was further verified through the warm forging of a thin-walled dual-cavity component. Crucially, this approach for evaluating deformation stability bridges the gap caused by the inapplicability of conventional processing maps within this temperature regime, offering a robust and broadly applicable workflow for complex forming optimization. Full article
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