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Additive Manufacturing of Metallic Materials: Experiments and Modelling

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

Deadline for manuscript submissions: closed (25 March 2026) | Viewed by 10321

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Dr. Ana Rosanete Lourenço Reis

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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
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Dr. Roya Darabi

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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

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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.

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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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Published Papers (11 papers)

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Research

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22 pages, 11432 KB

Open AccessArticle

Solidification Behavior and Fine Grain Control of Ti-Al-Si Coating by Electrostatic Field Assisted Direct Laser Deposition

by Yu Li, Xueting Chen, Yinglong Liang, Shuai Zhang and Guili Yin

Metals 2026, 16(5), 560; https://doi.org/10.3390/met16050560 - 21 May 2026

Viewed by 218

Abstract

The microstructure of Ti-Al-Si coatings prepared by direct laser deposition (DLD) requires further refinement and homogenization to enhance coating performance. In this study, an electrostatic field (E_F) was introduced to assist the DLD process, and a thermal-flow-electrical multiphysics coupling model was established using COMSOL Multiphysics 6.3 software. The solidification behavior of the molten pool under the E_F was investigated, focusing on the mechanism by which the E_F influences the nucleation and growth of grains and reinforcing phases. Experimental results revealed that the external E_F disrupted the molten pool flow, thereby altering the internal heat dissipation mechanism and affecting the morphology and size of the solidified grains. Under an external E_F of 150 V/cm, the coating exhibited the most refined grains, with an average size of 0.417 μm (a 46% reduction non-E_F). Concomitantly, the application of the E_F increased the concentration of Ti⁴⁺ and Si⁴⁺ ions at the solid–liquid interface, promoting the formation of a substantial quantity of Ti₅Si₃ reinforcing phases. The average microhardness of the coating reached 1393 HV₀.₂, which is 21% higher than that of the coating without an E_F. The surface roughness decreased to 0.551 μm, with a minimum wear percentage of 1.4%. Moreover, the E_F modified the wear mechanism of the DLD Ti-Al-Si coating. The findings of this study hold scientific significance and practical value for advancing DLD-fabricated high-performance titanium alloy components, offering critical insights into microstructure optimization and process control strategies. Full article

(This article belongs to the Special Issue Additive Manufacturing of Metallic Materials: Experiments and Modelling)

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15 pages, 2347 KB

Open AccessArticle

Physics-Informed Neural Networks for Process Optimization in Laser Powder Bed Fusion of Inconel 718 Superalloy: A Data-Efficient, Physics-Constrained Machine Learning Framework

by Saurabh Tiwari, Seong Jun Heo and Nokeun Park

Metals 2026, 16(5), 465; https://doi.org/10.3390/met16050465 - 24 Apr 2026

Viewed by 468

Abstract

This study aimed to develop and validate a physics-informed neural network (PINN) framework for data-efficient and physically consistent process optimization in the laser powder bed fusion (LPBF) of Inconel 718 (IN718) superalloy. Laser powder bed fusion (LPBF) is widely adopted for fabricating Inconel 718 (IN718) components in aerospace and energy applications; however, navigating its high-dimensional, nonlinear process parameter space remains a central challenge. High-fidelity finite element simulations are computationally prohibitive for extensive parameter sweeps, whereas purely data-driven machine learning (ML) models are limited by data scarcity and unphysical extrapolation behavior. This study presents a physics-informed neural network (PINN) framework that embeds the transient heat conduction equation and Goldak double-ellipsoidal heat source model directly into the neural network training loss, enforcing thermophysical consistency simultaneously with data fidelity. The model was trained on a curated, multi-source dataset of LPBF IN718 parameter combinations drawn from peer-reviewed experimental studies and validated finite element simulation outputs, spanning the laser power (70–400 W), scan speed (200–2000 mm/s), hatch spacing (50–140 µm), and layer thickness (20–50 µm). The PINN predicted the melt pool width, depth, peak temperature, and relative density with mean absolute percentage errors (MAPE) of 3.8%, 4.7%, 3.1%, and 1.9%, respectively, outperforming a baseline artificial neural network (ANN) with an identical architecture. The framework correctly identified the optimal volumetric energy density (VED) window of 55–105 J/mm³, yielding relative densities ≥99.5%, consistent with the published experimental thresholds for IN718. A data efficiency analysis demonstrated that the PINN with 25% training data achieves a performance equivalent to that of the fully trained ANN with 100% data, confirming an approximately four-fold data efficiency improvement attributable to physics-informed regularization, consistent with theoretical predictions. Sensitivity analysis via automatic differentiation confirmed that laser power and scan speed were the dominant parameters (~85% combined variance), which is in agreement with previous studies. This study provides a computationally efficient, interpretable, and physically consistent ML pathway for the accelerated process qualification of IN718 components for aerospace and energy applications. Full article

(This article belongs to the Special Issue Additive Manufacturing of Metallic Materials: Experiments and Modelling)

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26 pages, 6075 KB

Open AccessArticle

Knowledge Transfer Between Machines in Laser Powder Bed Fusion—Transfer Learning with Small Training Datasets

by Florian Funcke, Sebastian Brummer, Marinus Kolbinger and Peter Mayr

Metals 2026, 16(4), 438; https://doi.org/10.3390/met16040438 - 17 Apr 2026

Viewed by 370

Abstract

Laser Powder Bed Fusion (PBF-LB) is currently one of the most versatile and adopted additive manufacturing technologies for printing metals. To take new PBF-LB machines into service, a thorough characterization and calibration is often necessary to get the desired output. This is commonly achieved empirically; however, data-driven methods have become more and more available over the last few years. This research explores the use of transfer learning (TL) to transfer process knowledge from an already-established source machine (Nikon SLM 500) to a target machine (Trumpf TruPrint 5000) with different hardware specifications. To predict the tensile properties of AlSi10Mg0.5 utilizing a minimal data set of merely 25 training samples, eight TL model variants, determined by their degrees of training freedom, were investigated. The results showed that TL is effective in transferring machine learning (ML)-based process models. High prediction accuracy was achieved on the target machine, with coefficient of determination (

R^{2}

) values reaching 75.5% for yield strength, 82.1% for ultimate tensile strength, and up to 92.0% for elongation at break in testing. Additionally, a weighted mean model ensemble of all eight single models was developed, including all eight TL variants, to enable higher prediction robustness. Validation trials for three different use cases confirmed the capability of the approach to optimize processing conditions, like increasing hatch scan speed by 167% to 292% while maintaining high mechanical performance. Additional microstructure analysis was given to support the findings. The results demonstrate a time- and resource-efficient approach for rapid industrialization of PBF-LB machines, combining ML-based process modeling with machine-specific data. Full article

(This article belongs to the Special Issue Additive Manufacturing of Metallic Materials: Experiments and Modelling)

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18 pages, 2374 KB

Open AccessArticle

A Systematic Selection of Shielding Gas Composition for GMA-DED of HSLA Thin Walls Focused on Geometrical Features

by Moheb Ali Ghayour, Seyed Mohammad Hossein Seyedkashi, Mahmoud Moradi, Yadollah Yaghoubinezhad and Americo Scotti

Metals 2026, 16(3), 264; https://doi.org/10.3390/met16030264 - 27 Feb 2026

Viewed by 506

Abstract

While shielding gas selection significantly impacts gas metal arc directed energy deposition (GMA-DED), current industrial practices often rely on ad hoc decisions. This study proposes a logical and reproducible selection methodology that prioritizes geometric outcomes (such as layer height, width, and surface waviness) for HSLA thin walls. The performance of three Argon-based blends was examined with the constraints of the same wire, contact tip-to-work distance, wire feed, and deposition speeds. However, to ensure a scientifically ‘fair comparison’ between gas blends, the methodology prioritized maintaining optimal metal transfer regularity for each composition by adjusting the proper voltage setting with a constant-voltage power source. Results showed that increasing CO₂ content requires higher arc voltage but lower average current to maintain a constant wire feed speed. This shift leads to shorter and wider layers, while lateral surface waviness remains largely unaffected by gas composition. The primary contribution of this work is the establishment of a multifaceted decision-making system that enables users to balance these geometric and operational outcomes against specific production goals. As a demonstration, an Ar + 8% CO₂ blend was successfully selected using a criterion that balances high productivity with low thermal stress, providing a justified alternative to conventional trial-and-error selection. Full article

(This article belongs to the Special Issue Additive Manufacturing of Metallic Materials: Experiments and Modelling)

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28 pages, 15705 KB

Open AccessArticle

Effect of Layer Thickness and Scanning Parameters on Melt Pool Geometry and Track Continuity in Powder-Bed Arc Additive Manufacturing

by Arif Balci and Fatih Alibeyoglu

Metals 2026, 16(3), 259; https://doi.org/10.3390/met16030259 - 26 Feb 2026

Viewed by 614

Abstract

Powder-bed arc additive manufacturing (PBAAM) may reduce the cost of powder-bed metal additive manufacturing and enable thicker layers than laser powder bed fusion (LPBF), but melt-track stability limits are not well established. Here, 316L stainless steel powder (15–53 µm) was melted by a TIG-based arc in a custom powder-bed system while varying current, travel speed, layer thickness and hatch distance. Single tracks on an inclined bed (≈0–0.4 mm thickness) were used to identify continuity loss and melt-pool width, quantified from top-view images via width profiles, a gap-based continuity metric and the coefficient of variation. Parallel-track tests at 0.15, 0.20 and 0.25 mm layer thickness with hatch distances set to 25%, 50% and 75% of the measured melt-pool width assessed inter-track bonding and lack of fusion, and selected parameters were validated in five-layer builds. Higher current with low-to-moderate travel speeds produced wider, more stable melt pools on the inclined bed. Hatch ratios of 25–50% were the most effective for sustaining fusion in single layers and multi-layer builds, whereas 75% promoted unbonded regions and narrow-track morphologies. Overall, PBAAM can process substantially thicker layers with relatively simple equipment, but requires a narrow, carefully tuned window to balance continuity, fusion and heat accumulation. Full article

(This article belongs to the Special Issue Additive Manufacturing of Metallic Materials: Experiments and Modelling)

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21 pages, 2630 KB

Open AccessArticle

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

Cited by 1 | Viewed by 942

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). 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

Open AccessArticle

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 1082

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 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

Open AccessArticle

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

Cited by 6 | Viewed by 2075

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. 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

Open AccessArticle

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

Cited by 2 | Viewed by 1192

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 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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Review

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22 pages, 3566 KB

Open AccessReview

Fatigue Crack Growth Models Applied to Additively Manufactured Electron Beam Melted Ti6Al4V: A Review

by Nicole Atmadja and Mamidala Ramulu

Metals 2026, 16(4), 440; https://doi.org/10.3390/met16040440 - 17 Apr 2026

Viewed by 453

Abstract

This article comprehensively reviews the fatigue crack growth (FCG) models applied to Ti6Al4V alloy manufactured by electron beam melting (EBM) powder bed fusion (PBF). The current progress in FCG analytical and numerical models and their application to EBM Ti6Al4V are reviewed. Much experimental data for the creation of historical FCG models was based on conventionally manufactured (CM) aluminum alloys and various steels. With the growth of additive manufacturing (AM), recent studies have applied traditional models and modified new models to EBM Ti6Al4V and validated their use as accurate predictive models for the da/dN-ΔK curve and ΔK_th. Due to pores and surface roughness inherent in AM and the unique anisotropic microstructure developed from the EBM process, classical models may require modifications to accurately predict FCG of EBM Ti6Al4V. Full article

(This article belongs to the Special Issue Additive Manufacturing of Metallic Materials: Experiments and Modelling)

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30 pages, 4411 KB

Open AccessReview

The Tribological Behavior of Electron Beam Powder Bed Fused Ti-6Al-4V: A Review

by Mohammad Sayem Bin Abdullah and Mamidala Ramulu

Metals 2025, 15(11), 1170; https://doi.org/10.3390/met15111170 - 23 Oct 2025

Viewed by 1159

Abstract

This article comprehensively reviews the tribological behavior of a Ti-6Al-4V alloy manufactured via electron beam powder bed fusion (EB-PBF), an additive manufacturing process for aerospace and biomedical applications. EB-PBF Ti-6Al-4V demonstrates wear resistance that is superior or comparable to conventional Ti-6Al-4V. The reported average friction coefficient ranges between ~0.22 and ~0.75 during sliding wear in dry and lubricated conditions against metallic and ceramic counterparts when loading 1–50 N under varied surface and heat treatment conditions, and between 1.29 and 2.2 during fretting wear against EB-PBF Ti-6Al-4V itself. The corresponding average specific wear rates show a broad range between ~8.20 × 10⁻⁵ mm³/Nm and ~1.30 × 10⁻³ mm³/Nm during sliding wear. Lubrication reduces the wear rates and/or the friction coefficient. Wear resistance can be improved via machining and heat treatment. Wear anisotropy is reported and primarily attributed to microhardness variations, which can be mitigated through lubrication and post-processing. The effects of applied load and frequency on EB-PBF Ti-6Al-4V are also discussed. The wear resistance at elevated temperatures shows a mixed trend that depends on the counterpart material and the testing methods. Wear mechanisms involve oxide tribo-layer formation, abrasive wear, and adhesive wear. Current limitations, future research directions, and a standardization framework are also discussed. Full article

(This article belongs to the Special Issue Additive Manufacturing of Metallic Materials: Experiments and Modelling)

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