Advanced Insights into Laser-Based Metal Additive Manufacturing: From Microstructural Control to Functional Performance

1. Introduction and Scope

Laser based metal additive manufacturing has emerged as a transformative solution for producing next-generation metallic components, from architected lattices [1] and bio-functional implants [2] to wear-resistant coatings [3] and optimized industrial tools [4], bridging computational design [5], process modeling [6] and experimental validation to deliver lightweight [7], durable [8], and performance-tailored structures [9] for biomedical, aerospace and engineering applications [10]. In particular, laser-based AM methods, including Laser Powder Bed Fusion (L-PBF) [11] and Laser-Directed Energy Deposition (L-DED) [12], have enabled unprecedented design freedom (e.g., intricate lattices and triply periodic minimal surface (TPMS) structures) [13] alongside the fabrication of fully dense metallic parts [14]. This Special Issue focuses on advances in L-PBF and DED technologies that push the boundaries of microstructural control, mechanical performance, and functional integration. Key themes of the nine articles of this Special Issue include innovations in L-PBF process optimization and topology-optimized designs, functional coatings and surface modifications for enhanced bio-performance, investigations of fatigue behavior in critical alloys (Ti-6Al-4V, Ti-15Mo, Ti-6Al-2Sn-4Zr-6Mo, and SS316L), as well as microstructural modeling and simulation frameworks that bridge process parameters to material structure. The Special Issue also highlights novel applications of triply periodic minimal surface (TPMS) lattices in biomedical implants, hybrid material approaches (combining AM-built structures with advanced coatings) and post-processing techniques like drag finishing to improve surface quality of AM parts. Finally, a comprehensive review of factors affecting as-built surface roughness in metal AM provides a unifying perspective on how process parameters, such as layer thickness, energy input and build orientation influence surface finish and how this can be modeled, optimized, and complemented by post-processing methods for improved part quality.

Collectively, the contributions in this Special Issue underscore a shift toward integrated and intelligent AM approaches that couple materials science insights with computational optimization and post-processing strategies. Innovations such as machine learning-driven process optimization and cellular automated microstructure simulation reflect the growing convergence of data-driven modeling with experimental AM process development. By addressing challenges from lattice design trade-offs and fatigue life enhancement to surface quality control, this collection not only demonstrates improved functional performance of additively manufactured metal components but also emphasizes the efficiency and reliability of the manufacturing processes themselves. Looking forward, continued research is expected in standardizing process–structure–property prediction tools, developing environmentally conscious post-processing methods, and exploring hybrid manufacturing routes that ensure consistent quality and performance in critical metallic applications.

2. Contributions

This Special Issue presents nine original research articles that address critical challenges in laser-based metal additive manufacturing, including lattice design optimization, functional coatings for biomedical applications, fatigue resistance of titanium and stainless steel alloys, advanced microstructural modeling, process parameter optimization by machine learning, showcasing the latest strategies to enhance performance, reliability, and manufacturability in metallic AM components.

The first contribution, by Guo, Z. et al., titled “Enhanced Compressive Properties of Additively Manufactured Ti-6Al-4V Gradient Lattice Structures” explores how graded lattice architectures can improve the mechanical efficiency of L-PBF Ti-6Al-4V structures. Through a combination of finite element design optimization and experimental testing, the authors compare a uniform lattice to a functionally graded lattice (both with ~84% porosity) under compression. The graded lattice showed a layer-by-layer collapse mechanism under load, resulting in superior energy absorption and load-bearing capacity. Notably, the gradient design achieved approximately 20% higher cumulative energy absorption than the one of the uniform lattices. Under longitudinal compression the graded lattice outperformed the uniform design, whereas under transverse loading both lattices behaved similarly. These findings provide a foundation for designing lightweight Ti-6Al-4V lattice components with optimized stiffness and crashworthiness for aerospace and biomedical applications.

The second contribution, by Li, Z. et al., titled “Additively Manufactured Biomedical Ti-15Mo Alloy with Triply Periodical Minimal Surfaces and Functional Surface Modification” presents a novel β-Ti alloy implant combining a low-modulus TPMS lattice structure with bioactive surface coatings. A porous Ti-15Mo structure was fabricated via PBF-LB with an elastic modulus tailored between 15 and 50 GPa (closer to bone) and ~100 MPa yield strength. To address biomedical functionality, the lattice surfaces were enhanced using a microporous TiO₂ layer by micro-arc oxidation, which was subsequently coated with silver nanoparticles (Ag) for broad antibacterial effect, tannic acid (TA) for antioxidant properties, and fluorinated hydroxyapatite (FHA) to promote osteogenesis. The result was a Ti-15Mo implant with a biofunctional multi-layer coating that demonstrated good cytocompatibility, hemocompatibility, and bactericidal performance in vitro. This work offers a comprehensive approach to aligning mechanical and biological performance in AM implants, showing that TPMS-based porous designs combined with advanced surface modification can mitigate stress-shielding while imparting infection resistance and improved osseointegration.

The third contribution, by Poyraz, O. et al., titled “Optimized and Additively Manufactured Face Mills for Enhanced Cutting Performance” applies L-PBF and topology optimization to the development of metal cutting tools. A set of indexable face milling cutters was re-engineered to reduce vibrational amplitudes during machining without sacrificing stiffness. Using M300 maraging steel and L-PBF, three face mill designs with varying weight-reduction targets were produced, leveraging the geometric freedom of AM to integrate internal damping features. Experimental modal analysis and cutting trials showed that the optimized AM face mills exhibited significantly lower vibration magnitudes and reduced tool wear compared to a conventional design. In fact, the AM-optimized tools achieved more stable cutting with diminished chatter and extended insert life, highlighting the potential of PBF-manufactured tooling to outperform traditional mills. This study demonstrates how coupling topology optimization with metal AM can yield cutting tools with improved dynamic performance, pointing toward broader adoption of AM in the tooling industry.

The fourth contribution, by Cosma, C. et al., titled “The Effect of Drag Finishing on Additively Manufactured Customized Dental Crowns” investigates a post-processing technique to enhance the surface finish of CoCr dental crowns produced by L-PBF. Drag finishing (DF), a mechanical polishing method, was applied to as-printed CoCr crowns to overcome the inherently high roughness of AM surfaces. The authors report that DF treatment on the external crown surfaces achieved a 70–90% reduction in surface roughness, bringing the roughness (Ra) down to about 0.6 µm in the best case. This smoothness is comparable to that of conventionally milled surfaces and is achieved in a fraction of the time required by manual polishing. While inner (concave) surfaces saw only limited improvement due to restricted media access, 3D surface texture analysis confirmed that drag finishing effectively uniformizes the surface by leveling microscopic peaks. In addition, coordinate measurements showed that DF improved dimensional accuracy of the crowns’ exterior (deviations reduced to +0.01–0.05 mm) and the process, coupled with a proper heat treatment, increased surface hardness to ~520 HV. This contribution underlines the importance of post-processing in AM for dental applications, demonstrating that automated finishing can yield patient-specific metal restorations with clinically acceptable surface quality and fit.

The fifth contribution, by Kirk, C. et al., titled “Microstructure, Porosity, and Bending Fatigue behavior of PBF-LB/M SS316L for Biomedical Applications” examines how inherent AM defects impact the fatigue life of 316L stainless steel, a common implant material. SS316L samples fabricated by L-PBF were subjected to microstructural analysis and four-point bending fatigue tests to simulate cyclic loading conditions of biomedical implants. The study finds that process-induced defects, specifically micro-pores and unmelted powder particles within the as-built metal, act as critical stress concentrators that facilitate early crack initiation under cyclic bending. Even when loading is below the yield strength, these internal flaws markedly reduce fatigue life, as real-time microscopy of the tests captured crack nucleation at defect sites. The authors conclude that the fatigue performance of L-PBF 316L is strongly governed by its microstructural integrity, directly linking higher porosity to shorter lifespan. This insight is crucial for the design of reliable patient-specific implants, suggesting that improved process parameters or post-build hot isostatic pressing (HIP) may be necessary to decrease the level of the defects. The work supports ongoing efforts to ensure AM-built biomedical components meet stringent durability requirements by controlling porosity and microstructure.

The sixth contribution, by Qiao, Y. et al., titled “Toward an Efficient and Robust Process–Structure Prediction Framework for Filigree L-PBF 316L Stainless Steel Structures” introduces a computational modeling approach to predict microstructural outcomes in thin-walled AM parts. The authors develop a cellular automata (CA) simulation framework to model grain growth and texture formation during the solidification of 316L stainless steel in L-PBF. Filigree geometries (with a wall thickness approaching the scale of individual grains) pose a particular challenge for prediction, as even minor thermal fluctuations can influence grain structure. By calibrating the CA model with relevant process parameters (like laser power and scan speed) and validating against EBSD measurements, the study demonstrates that the simulation can effectively reproduce key microstructural features observed experimentally. The CA model captured the grain size distribution, aspect ratio, and crystallographic texture in the thin walls, proving its robustness in reflecting how process conditions translate into microstructure. This work highlights the value of physics-based modeling in AM: reliable process–structure prediction tools can help optimize scan strategies and parameters virtually, ensuring desired grain structures (and thus consistent mechanical properties) in complex L-PBF components. It marks a step toward integrating microstructural simulation into the design loop of high-performance, safety-critical AM parts.

The seventh contribution, by Pirro, G. et al., titled “Effect of Heat Treatments and Related Microstructural Modifications on High-Cycle Fatigue Behavior of PBF-LB-Fabricated Ti-6Al-2Sn-4Zr-6Mo Alloy”, investigates how tailored post-build heat treatments can enhance the fatigue life of a high-strength titanium alloy produced by L-PBF. The alloy Ti-6Al-2Sn-4Zr-6Mo (Ti6246) was studied under two conditions: a standard α + β anneal at 875 °C (AN875) and a solution treatment at 825 °C followed by aging at 500 °C (STA825). High-cycle fatigue tests revealed that the STA825 heat treatment delivers approximately 25% greater fatigue strength than the conventional anneal. Microstructural analysis explains this improvement: STA825 yields a finer bi-lamellar α/β microstructure with thinner primary α lamellae and a more homogeneous dispersion of secondary α within the β matrix. Such a refined microstructure effectively delays crack nucleation and slows crack propagation, as confirmed by defect-sensitivity models, showing a higher fatigue limit and threshold stress intensity for STA825-treated samples. Notably, the optimized STA825-treated Ti6246 outperformed even the well-established Ti-6Al-4V alloy in fatigue endurance, underscoring Ti6246’s potential as a superior material for load-bearing AM components. This contribution emphasizes that post-processing heat treatments are vital for unlocking the full fatigue performance of AM Ti alloys, enabling their use in demanding aerospace and automotive applications where fatigue failure is a primary concern.

The eighth contribution, by Liu, C. et al., titled “Multi-Model Collaborative Optimization of Inconel 690 Deposited Geometry in Laser-Directed Energy Deposition: Machine Learning Prediction and NSGA-II Decision Framework” addresses the challenge of achieving precise geometric control in L-DED processes through a data-driven approach. Focusing on Inconel 690 (a nickel alloy relevant for nuclear industry repairs), the authors built a large experimental dataset relating process parameters to single-bead geometry (width, height, depth) via full-factorial laser deposition trials. By applying statistical analysis, they identified strong correlations (e.g., laser power positively correlating with bead width r ≈ 0.82 and depth r ≈ 0.85, and powder feed rate correlating with bead height r ≈ 0.70). Using this knowledge, a hybrid machine learning model based on a backpropagation neural network (BPNN) was trained to predict bead dimensions with high accuracy (R² up to ~0.96). The model was then coupled with a multi-objective optimization using the NSGA-II genetic algorithm, which generated a Pareto set of 100 optimal parameter combinations for controlling bead geometry. Remarkably, when tested, the ML-driven framework could predict and achieve target bead widths and depths within ≤5% error, and heights within ~5.3%. This confirms the effectiveness of the approach for reliable, first-try process setup in L-DED, reducing trial-and-error and ensuring dimensional accuracy in deposited features. The work showcases how combining machine learning prediction with evolutionary optimization can guide process parameter selection for complex AM processes, opening new directions to adaptive control in critical applications like nuclear component repair.

The ninth contribution, by Paggetti, S. et al., titled “Factors Affecting the Surface Roughness of As-Built Additively Manufactured Metal Parts: A Review,” provides a comprehensive synthesis of the main factors influencing surface quality across metal AM technologies. It covers all major metal AM processes, including powder bed fusion (laser-based and electron-beam), direct energy deposition (both laser and arc-based), binder jetting, material extrusion, and sheet lamination, outlining the typical as-built roughness levels and the mechanisms behind them. The authors systematically identify how factors such as layer thickness, scan strategy, energy input, feedstock form (powder or wire), and build orientation/inclination angle affect the resulting surface texture. For example, inclined or curved surfaces built in PBF tend to exhibit higher roughness due to the stair-step effect, which can be mitigated by applying in situ re-melting scans. Likewise, in DED processes, increased laser power or powder flow can inadvertently increase roughness by enlarging melt pools and spatter, whereas optimized spot size and travel speed yield smoother tracks. The review presents these correlations in a series of tables and charts, serving as practical guidelines for engineers to predict or control surface finish. By providing a concise summary of roughness drivers across technologies, this work helps practitioners design parts and select process parameters that meet surface quality requirements or determine when post-processing (like machining or polishing) will be necessary. In doing so, it bridges the knowledge gap for achieving application-specific surface roughness in metal AM in close correlation with better process control.

3. Acknowledgments

As Guest Editors of this Special Issue, “Advances in Additive Manufacturing for Metallic Materials and Their Applications (3rd Edition),” we would like to express our sincere gratitude to all the contributing authors for sharing their innovative research and insights into laser-based metal AM. We extend our thanks to the expert reviewers for their thorough evaluations and constructive feedback, which have greatly enhanced the quality of these articles. We are also grateful to the editorial team of Metals and the MDPI staff for their professional support throughout the publication process. We trust that the diverse advancements compiled in this Special Issue will serve as a valuable resource for researchers, engineers, and practitioners in the additive manufacturing community and inspire further developments in this rapidly evolving field in the future.