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Metals
Special Issues
Additive Manufacturing of Metallic Materials: Experiments and Modelling
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Additive Manufacturing of Metallic Materials: Experiments and Modelling

A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Additive Manufacturing".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1932

Special Issue Editors

Dr. Ana Rosanete Lourenço Reis

E-Mail Website
Guest Editor
Faculty of Engineering, University of Porto (FEUP), Rua Dr. Roberto Frias 400, 4200-465 Porto, Portugal
Interests: advanced manufacturing processes; digital manufacturing and industry 4.0; material processing techniques; additive manufacturing; smart manufacturing and automation; process optimization and control in manufacturing; sustainable and energy-efficient manufacturing; hybrid manufacturing technologies; artificial intelligence and machine learning in manufacturing
Special Issues, Collections and Topics in MDPI journals
Dr. Roya Darabi

E-Mail Website
Guest Editor
Faculty of Engineering, University of Porto (FEUP), Rua Dr. Roberto Frias 400, 4200-465 Porto, Portugal
Interests: computational modeling and simulation of manufacturing processes; advanced manufacturing processes; multiscale modeling; multiphysics simualtions

Special Issue Information

Dear Colleagues,

The additive manufacturing (AM) of metallic materials has transformed the way we design, fabricate, and optimize metal components for various industries, including the aerospace, automotive, biomedical, and energy industries. Its ability to produce highly complex geometries with enhanced material properties, reduced waste, and improved efficiency has positioned AM as a key driver of innovation in modern manufacturing.

Despite significant advancements, several scientific and engineering challenges remain, particularly in understanding the microstructural evolution, mechanical performance, residual stresses, and process optimization of the metallic materials produced through AM. Bridging the gap between experimental studies and computational modeling is essential for advancing metal AM technologies and accelerating their industrial adoption.

We invite contributions to this Special Issue that address these challenges and enhance our fundamental understanding of the additive manufacturing of metallic materials. This Special Issue aims to gather a comprehensive collection of high-quality research covering both the experimental and modeling aspects of metal AM. Topics of interest include, but are not limited to, the following:

  • Process–structure–property relationships in metal AM;
  • Microstructural evolution and mechanical behavior;
  • The modeling and simulation of AM processes;
  • Residual stress formation and mitigation strategies;
  • Multiscale and multiphysics modeling approaches;
  • Advanced characterization techniques for AM metals;
  • The application of AI and machine learning in AM process control;
  • Defects, porosity, and their influence on mechanical performance;
  • Post-processing techniques and their impact on material properties.

We look forward to receiving your contributions and advancing the field of metal additive manufacturing together.

Best regards,

Dr. Ana Rosanete Lourenço Reis
Dr. Roya Darabi
Guest 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. 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

  • metallic additive manufacturing (MAM)
  • process–structure–property relationships
  • microstructural evolution
  • mechanical behavior of AM metals
  • residual stress in additive manufacturing
  • computational modeling of AM
  • multiscale and multiphysics simulations
  • defects and porosity in AM
  • fatigue and fracture of AM components
  • machine learning for process control in AM
  • artificial intelligence for defect detection and quality assurance in AM

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

21 pages, 2630 KB  
Article
Optimization of L-PBF Process Parameters: A Multi-Objective Framework for Variance Reduction and Mechanical Strength Enhancement Using Linear Programming and Multi-Objective Methods
by Alexander I. Khaimovich, Vitaliy G. Smelov, Viktoriya V. Kokareva and Vyacheslav P. Alekseev
Metals 2025, 15(9), 1027; https://doi.org/10.3390/met15091027 - 17 Sep 2025
Viewed by 184
Abstract
A new approach to the multi-criteria optimization of L-PBF process parameters in the face of high variability is proposed, based on the application of the reward function (RF), which is essentially the inverse of the Taguchi quality loss function (QLF [...] Read more.
A new approach to the multi-criteria optimization of L-PBF process parameters in the face of high variability is proposed, based on the application of the reward function (RF), which is essentially the inverse of the Taguchi quality loss function (QLF). The RF is defined as the logarithm of the ratio of the design parameter value to the minimum target parameter value. For values below 1, the RF becomes sharply negative. Logarithmic additivity enables the optimization of the target function to be defined as a sum of reward functions for the optimization criteria, each with a corresponding weight coefficient. This makes it possible to formulate the optimization problem in terms of linear programming. This approach was tested in the optimization of the L-PBF technological modes of AISI 321 powder, leading to a 6.7% increase in productivity. The mechanical properties of the samples obtained were not inferior to those of the alloyed samples using the previously recommended process parameters in terms of ultimate strength, and they exceeded the latter by 5% in terms of conditional yield strength. Process variability significantly decreased; the RMS of ultimate strength decreased nine-fold, conditional yield strength decreased ten-fold, and relative elongation decreased two-fold. Full article
(This article belongs to the Special Issue Additive Manufacturing of Metallic Materials: Experiments and Modelling)
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22 pages, 5391 KB  
Article
An Experimental Study on Tensile Characteristics of Ti-6Al-4V Thin Struts Made by Laser Powder-Bed Fusion: Effects of Strut Geometry and Linear Energy Density
by Rabiul Islam, Beytullah Aydogan and Kevin Chou
Metals 2025, 15(9), 1009; https://doi.org/10.3390/met15091009 - 11 Sep 2025
Viewed by 290
Abstract
Laser powder bed fusion (L-PBF) enables the fabrication of complex lattice-type structures composed of thin struts, offering lightweight, high-strength advantages in aerospace and biomedical applications, among others. While extensive research has examined full lattices and process parameter effects individually, the combined influence of [...] Read more.
Laser powder bed fusion (L-PBF) enables the fabrication of complex lattice-type structures composed of thin struts, offering lightweight, high-strength advantages in aerospace and biomedical applications, among others. While extensive research has examined full lattices and process parameter effects individually, the combined influence of strut geometry, configuration, and processing conditions on mechanical properties remains less understood. This study investigates how the strut number, strut size, cross-sectional shape, and laser energy input affect the mechanical properties of thin-strut L-PBF tensile specimens. Ti-6Al-4V struts were designed and fabricated using an EOS M270 system using five linear energy density (LED) levels. The fabricated specimens were measured in porosity using micro-scaled computed tomography and further evaluated using a tensile tester. The results showed that increasing the strut number leads to significant reductions in tensile strength, even with the same overall cross-sectional area, especially at low LED levels. Size effects on mechanical strengths were observed, though mostly minimal, except at the smallest strut size, where defects tend to be more critical. Circular and square shapes performed similarly under general LED conditions; however, square struts exhibited inferior behavior at the lowest LED level. Overall, LED is the most influential factor, with the greatest tensile strength occurring near 0.2 J/mm; further decreasing or increasing the LED both increase the porosity, degrading mechanical strengths. Full article
(This article belongs to the Special Issue Additive Manufacturing of Metallic Materials: Experiments and Modelling)
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32 pages, 8490 KB  
Article
Physics-Based Machine Learning Framework for Predicting Structure-Property Relationships in DED-Fabricated Low-Alloy Steels
by Atiqur Rahman, Md. Hazrat Ali, Asad Waqar Malik, Muhammad Arif Mahmood and Frank Liou
Metals 2025, 15(9), 965; https://doi.org/10.3390/met15090965 - 29 Aug 2025
Viewed by 506
Abstract
The Directed Energy Deposition (DED) process has demonstrated high efficiency in manufacturing steel parts with complex geometries and superior capabilities. Understanding the complex interplays of alloy compositions, cooling rates, grain sizes, thermal histories, and mechanical properties remains a significant challenge during DED processing. [...] Read more.
The Directed Energy Deposition (DED) process has demonstrated high efficiency in manufacturing steel parts with complex geometries and superior capabilities. Understanding the complex interplays of alloy compositions, cooling rates, grain sizes, thermal histories, and mechanical properties remains a significant challenge during DED processing. Interpretable and data-driven modeling has proven effective in tackling this challenge, as machine learning (ML) algorithms continue to advance in capturing complex property structural relationships. However, accurately predicting the prime mechanical properties, including ultimate tensile strength (UTS), yield strength (YS), and hardness value (HV), remains a challenging task due to the complex and non-linear relationships among process parameters, material constituents, grain size, cooling rates, and thermal history. This study introduces an ML model capable of accurately predicting the UTS, YS, and HV of a material dataset comprising 4900 simulation analyses generated using the “JMatPro” software, with input parameters including material compositions, grain size, cooling rates, and temperature, all of which are relevant to DED-processed low-alloy steels. Subsequently, an ML model is developed using the generated dataset. The proposed framework incorporates a physics-based DED-specific feature that leverages “JMatPro” simulations to extract key input parameters such as material composition, grain size, cooling rate, and thermal properties relevant to mechanical behavior. This approach integrates a suite of flexible ML algorithms along with customized evaluation metrics to form a robust foundation to predict mechanical properties. In parallel, explicit data-driven models are constructed using Multivariable Linear Regression (MVLR), Polynomial Regression (PR), Multi-Layer Perceptron Regressor (MLPR), XGBoost, and classification models to provide transparent and analytical insight into the mechanical property predictions of DED-processed low-alloy steels. Full article
(This article belongs to the Special Issue Additive Manufacturing of Metallic Materials: Experiments and Modelling)
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15 pages, 7895 KB  
Article
Microstructural Characteristics of WC-Cu Cladding on Mild Steel Substrate Prepared Through Plasma Transferred Arc Welding
by Muhammad Hussain, Bosheng Dong, Zhijun Qiu, Ulf Garbe, Zengxi Pan and Huijun Li
Metals 2025, 15(8), 902; https://doi.org/10.3390/met15080902 - 13 Aug 2025
Viewed by 524
Abstract
This study explores the development of a novel composite coating system combining the high hardness of WC and thermal conductivity of Cu, employing the plasma transfer arc welding method under ambient conditions. Utilizing an advanced welding approach, the work investigates microstructural evolution and [...] Read more.
This study explores the development of a novel composite coating system combining the high hardness of WC and thermal conductivity of Cu, employing the plasma transfer arc welding method under ambient conditions. Utilizing an advanced welding approach, the work investigates microstructural evolution and phase formation in a WC-Cu-based coating applied to a mild steel substrate. Emphasis is placed on understanding the solidification behaviour and its influence on defects, microstructural refinement, and carbide formation. The study provides insights into the interactions between coating constituents and the underlying substrate under controlled thermal conditions. These findings demonstrate the potential for producing functionally graded coatings tailored for demanding wear and heat dissipation applications. The approach offers a pathway for enhancing the durability and performance of steel components in extreme service environments. Full article
(This article belongs to the Special Issue Additive Manufacturing of Metallic Materials: Experiments and Modelling)
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