Challenges and Trends in Additive Manufacturing for Metallic Applications: Toward Optimized Processes and Performance

1. Introduction and Scope

Additive Manufacturing (AM) for metallic applications continues to redefine how complex, high-performance components are designed and fabricated across a wide range of sectors, including the aerospace, biomedical, and automotive fields, etc. [1]. Recent advancements in materials science, process optimization, and modeling have contributed to more reliable, efficient, and sustainable manufacturing practices in this area [2]. Despite this progress, several challenges persist. These include improving machinability and surface quality, optimizing the process parameters to minimize energy consumption, reusing materials without quality degradation, and reliably predicting performance outcomes through accurate modeling [3]. Furthermore, achieving consistent mechanical properties and reducing thermal residual stresses across different AM methods, such as Wire Arc Additive Manufacturing (WAAM), Laser Powder Bed Fusion (L-PBF), and Direct Energy Deposition (DED), remain areas that require significant research attention in the future [4,5,6].

This Special Issue aims to address these knowledge gaps by presenting a collection of ten original articles covering a spectrum of AM challenges and solutions for metallic applications—from powder reuse and hybrid modeling frameworks to microstructural control and other advanced monitoring techniques [7]. Collectively, the articles published in this Special Issue emphasize not only the performance of manufactured components, but also the efficiency and sustainability of the AM processes in a wide area of metallic applications. The contributions presented in this Special Issue emphasize a growing shift towards integrated, intelligent AM ecosystems for metallic applications that are combining materials science, machine learning, and process engineering. Looking forward, future research must focus on standardized benchmarking protocols, environmentally conscious process design, and advanced post-processing strategies that ensure component reliability in critical AM for metallic applications.

2. Contributions

This Special Issue presents 10 scientific articles that address key challenges in metal additive manufacturing, including process optimization, material reuse, stress modeling, surface enhancement, and real-time monitoring, highlighting current trends and future directions in the field of AM for metallic applications.

The first contribution of this Special Issue is a review article by Capasso, I. et al., which presents an extensive overview of the additive manufacturing (AM) technologies used for metallic applications, with a special focus on the field of construction engineering. The article thoroughly examines the evolution, principles, and classification of various AM processes for metallic applications, including powder bed fusion (SLM, EBM), direct energy deposition (WAAM, LMD), and binder jetting. It highlights the advantages and limitations of each method in terms of accuracy, surface finish, material compatibility, and build volume. Particular attention is paid to the process parameters influencing part quality, post-processing methods, and the challenges related to scale-up, standardization, and cost. The review also discusses the integration of topology optimization with AM for metallic applications and the potential for structural repair in civil infrastructure. By synthesizing a broad range of the current literature, the study provides valuable insights for both researchers and practitioners, reinforcing the strategic relevance of AM for metallic applications in the construction sector while outlining critical areas for future development.

The second contribution of this Special Issue is a research article by Zhang, H. et al., which presents a machine learning-based framework for optimizing process parameters in Wire Arc Additive Manufacturing (WAAM), with an emphasis on energy efficiency. The study employed three machine learning models—Support Vector Regression (SVR), Backpropagation Neural Network (BPNN), and XGBoost—to predict the wire feed speed to welding speed (WFS/WS) ratio based on geometric descriptors such as bead width (BW), height (BH), and cross-sectional area (BCSA), derived from 3D laser scanning and point cloud analysis. Among these, SVR demonstrated the highest predictive accuracy, which was further enhanced by using Particle Swarm Optimization (PSO). The optimized SVR model enabled the reverse prediction of input parameters from target bead shapes, reducing reliance on empirical trial-and-error. Experimental validations confirmed the method’s effectiveness, achieving energy consumption reductions of up to 11.47%. This data-driven approach offers a practical path to process planning in WAAM, combining geometric fidelity with sustainable operation.

The third contribution of this Special Issue is a research article by Pereira, J.C. et al., which investigates the effects of powder reuse on the microstructural and mechanical integrity of directed energy deposition (DED) components produced with two different alloys: cobalt-based Stellite^® 21 and super duplex stainless steel UNS S32750. The authors conducted a comprehensive three-cycle reuse study, evaluating powder morphology, oxygen uptake, and particle size evolution, along with the densification, chemical composition, and defect formation in the resulting bulk materials. The results revealed that Stellite^® 21 can be reused up to three times without significant degradation, while the duplex steel showed marked declines in phase balance, hardness, and porosity control after a single reuse. Particularly in the super duplex case, the ferrite content dropped from 50.1% to 37.0% due to increased oxidation, causing microstructural instability and pore formation. These findings emphasize the critical role of alloy selection and controlled reuse strategies in maintaining material integrity and process efficiency for DED applications.

The fourth contribution of this Special Issue is a research article by Failla, D.P., Jr. et al., which investigates the predictive fidelity of material models for estimating residual stresses in Laser Powder Bed Fusion (L-PBF)-manufactured components using Inconel 718. The authors compare a standard elastic–perfectly plastic (EPP) model with a more advanced internal state variable-based model, the Evolving Microstructural Model of Inelasticity (EMMI), within a sequentially coupled thermo-mechanical finite element analysis. The L-shaped geometry used in the study, featuring curved edges and holes, was replicated from a neutron diffraction experiment to validate the simulation results. While both models captured the general residual stress distributions, EMMI provided more accurate predictions at critical free surfaces due to its ability to reflect microstructural evolution under thermal cycling. However, EPP outperformed EMMI in certain regions, highlighting the influence of material calibration—EMMI was tuned for wrought IN718 rather than for AM-specific microstructures. This work emphasizes the importance of selecting and calibrating material models appropriately for accurate stress prediction in geometrically complex AM parts made from metallic materials like Inconel 718.

The fifth contribution of this Special Issue is a research article by Horr, A.M., which presents a novel hybrid modeling framework for the real-time control and digital twin integration of additive manufacturing (AM) processes for metallic applications, with a focus on Wire Arc Additive Manufacturing (WAAM). The study combines reduced-order modeling (ROM) techniques with machine learning (ML) algorithms to create predictive and corrective tools for process simulation and optimization. Using a case study based on aluminum WAAM, the author demonstrates how the integration of singular value decomposition (SVD) and radial basis function (RBF) neural networks enables fast and accurate temperature predictions with dramatically reduced computational costs—less than one second per scenario compared to nearly an hour with full-scale finite element (FE) simulations. The study further evaluates the performance of several modeling methods (e.g., SVM, kriging, regression, clustering) and concludes that the SVD-RBF hybrid outperforms others, especially under high heating rate conditions. This research highlights the significant potential of ROM-ML frameworks to support adaptive, real-time decision-making in AM environments, promoting smarter, greener, and more agile manufacturing systems for metallic applications.

The sixth contribution of this Special Issue is a research article by Yang, J. et al., which presents a comprehensive strategy to reduce thermal residual stress in topologically optimized automotive brake calipers fabricated via Powder Bed Fusion (PBF). Recognizing the challenges posed by anisotropic thermal accumulation in irregularly shaped metallic AM parts, the authors proposed an optimized scan strategy using an island pattern with a 5 mm hatching length combined with vertical build orientation. Comparative experiments on residual stress and thermal deformation, supported by X-ray measurements and cantilever testing, showed that the island scan pattern reduced residual stress and deformation by up to 8.41% and 8.33%, respectively, compared to traditional patterns. The study also included finite element analysis and topology optimization of a brake caliper made from Ti-6Al-4V, leading to a 20% weight reduction while maintaining mechanical integrity under hydraulic pressure. The manufactured caliper was evaluated via a brake dynamometer using the JASO C406 procedure, demonstrating comparable or superior performance to commercial aluminum alloy counterparts. The findings validate the effectiveness of combining strategic scan planning with optimized geometry in PBF for automotive light weighting applications.

The seventh contribution of this Special Issue is a research article by Ibrahim, M. et al., which presents a preliminary evaluation of nickel silicide (NiSi12-wt%) laser cladding for enhancing the corrosion resistance and mechanical performance of S355 structural steel. Using laser metal deposition (LMD), the authors successfully applied multilayer cladding onto S355 substrates and subjected the coated and uncoated samples to accelerated corrosion testing in ferric chloride (FeCl₃) solution following the ASTM G48 protocol. Microstructural analysis via Scanning Electron Microscopy (SEM) and Light Optical Microscopy (LOM) revealed that the cladding formed a dense, dendritic microstructure with strong metallurgical bonding. The NiSi12-wt% cladding significantly outperformed the bare steel in terms of corrosion resistance at both room temperature and 50 °C, reducing pit formation and mass loss rates. Micro-hardness tests confirmed a substantial increase in surface hardness—from 842 HV in the base steel to 1258 HV in the cladded state—being maintained even after corrosion exposure. The results emphasized the potential of nickel silicide cladding as a protective surface engineering solution for steels used in aggressive marine and industrial environments, offering a promising solution for durability and the lifespan extension of structural components.

The eighth contribution of this Special Issue is a research article by Santos, L.J.E.B. et al., which investigates the integration of electric arc signal analysis with microstructural examination for anomaly detection in walls produced by the Gas Metal Arc (GMA)-based Wire Arc Additive Manufacturing (WAAM) process. The authors conducted controlled contamination experiments using sand, chalk, and oil during the deposition of 316L-Si stainless steel walls, aiming to evaluate how these contaminants affect both arc stability and material quality. Real-time voltage and current data were collected during deposition and metallographic samples were analyzed to detect solidification defects and microstructural irregularities. The study identified strong correlations between arc signal features—such as peak counts, average values, and signal deviations—and the presence of microscopic defects in contaminated regions. The findings suggest that arc signal anomalies can serve as effective indicators of hidden process instabilities, and that these signals could support the development of real-time monitoring tools or predictive models for defect detection in WAAM. This work demonstrates the potential of integrated arc–microstructure monitoring as a non-invasive approach to improve reliability in AM production environments of metallic applications.

The ninth contribution of this Special Issue is a research article by Liu, J. et al., which investigated the microstructure and wear resistance of laser-cladded Ni60/60%WC composite coatings applied to 45 steel substrates. The study aimed to fabricate crack-free, high-hardness coatings with a thickness exceeding 1 mm using optimized laser cladding parameters. The resulting coatings exhibited a well-bonded microstructure, with a uniform distribution of WC particles and the formation of hard phases such as W₂C, Cr₂₃C₆, and Fe_3.57W_9.43C_3.54. XRD and SEM analyses revealed the layered distribution of these phases and localized elemental diffusion near WC particles. The coatings achieved an average micro-hardness of 1416 HV_0.2—over five times greater than that of the base steel—and a friction coefficient reduced by 43.5%. Furthermore, the wear rate was lowered by 79.13% compared to the uncoated substrate. The wear mechanisms were characterized as predominantly abrasive, with WC acting both as a reinforcing phase and as a wear debris source over time. The results validate the effectiveness of using high-WC composite coatings to significantly enhance surface durability in high-load applications.

The tenth contribution of this Special Issue is a research article by Kunčická, L. et al., which investigated the thermomechanical behavior of AISI 316L stainless steel fabricated via Selective Laser Melting (SLM) under various strain rates and temperatures to determine the optimal post-processing parameters. The study combines experimental uniaxial hot compression tests with finite element modeling to assess the deformation response and microstructural evolution of AM-prepared 316L steel across four temperatures (900–1250 °C) and four strain rates (0.1–100 s⁻¹). The results show that high strain rates at 900 °C significantly increase the micro-hardness (up to 270 HV) and flow stress (~380 MPa) due to pronounced sub-structural development, while higher temperatures facilitate dynamic recrystallization and grain coarsening, reducing the mechanical strength. Finite Element Method (FEM) simulations, which have been validated through experimental trends in strain distribution and force predictions, have confirmed that lower temperatures and higher strain rates are optimal for strengthening via plastic deformation. This work provides valuable insights for selecting the post-processing conditions that enhance the performance of AM 316L components for demanding applications.