Generative adversarial networks (GANs) have revolutionized deep learning by enabling high-fidelity data synthesis. In additive manufacturing (AM) and materials characterization, they offer a powerful solution to the pervasive challenge of data scarcity by predicting microstructures and synthetically modeling stochastic defects. Drawing on a systematic PRISMA-guided retrieval from 2014 through 2026, this review critically analyzes the state-of-the-art of GANs applied to the AM lifecycle. We establish a novel taxonomy categorizing the research landscape into three distinct functional clusters: stochastic microstructure reconstruction, high-fidelity texture synthesis, and process-structure mapping. Beyond this systematic survey, our evidence appraisal identifies critical structural vulnerabilities in the current literature. We highlight a pervasive “validation deficit”, wherein contemporary models exhibit a systemic over-reliance on perceptual metrics (e.g., FID) at the expense of thermodynamic and mechanical consistency via downstream solvers. Furthermore, we expose the “binary phase bottleneck”, which limits the capacity of current architectures to model continuous multi-phase alloys, and a “topological blind spot” in 3D generation, where both voxel and slice-based paradigms fail to preserve the true connectivity of anisotropic AM defects. Finally, we warn against a critical cross-material domain adaptation vulnerability when transferring architectures from isotropic media to thermodynamically complex metals. Consolidating these insights, we argue that the future of AM digitization must transcend visual realism. We propose that integrating hard physics-informed constraints (e.g. PI-GANs), topology-aware loss functions, and recognizing the unique millisecond-inference advantage of GANs over Diffusion Models for in-situ control, are the requisite steps toward the deterministic digital certification of manufactured materials.
Copper alloys processed by laser powder bed fusion often exhibit a trade-off between processability, mechanical performance, and electrical conductivity. This study investigates the effect of laser power on the process–structure–property relationships of a Cu–2 wt
TiBw-reinforced titanium matrix composite is widely used in the aerospace industry and marine vessels. The plasma arc directed energy deposition process is one of potential technologies for fabrication of TiBw-reinforced titanium matrix composite. In this study, a physics-informed machine learning methodology is proposed to reveal the relationship between process parameters and the cross-sectional area, TiBw area ratio, coefficient of variation of TiBw particle area, and hardness of TiBw-reinforced titanium matrix composites. The Halpin-Tsai model and the energy relationships of plasma arc directed energy deposition process are incorporated as prior physical knowledge into the neural network. The genetic optimizing algorithm is employed to obtain the optimal process parameters for fabricating TiBw-reinforced titanium matrix composite. The results show that the physics-informed machine learning methodology exhibits significantly enhanced prediction performance and generalization ability. The area fraction of TiBw in the TiBw-reinforced titanium matrix composite with the optimal plasma arc directed energy deposition process reaches 0.14, the particle coefficient of variation is 0.41, and the hardness increases to 625 HV.
Wire Arc Additive Manufacturing (WAAM) has become a highly dependable manufacturing technique in the 21st century due to its cost-effectiveness, shortened production time, and reduced material wastage. Compared with other additive manufacturing techniques such as selective laser melting and electron beam electron beam melting, WAAM offers significantly higher deposition rates, lower feedstock costs, and greater capability for producing large-scale metallic components. Due to these advantages, WAAM has gained considerable attention in industries requiring large structural components and complex geometries. WAAM excels particularly in fabricating complex geometries. However, defects remain a significant challenge associated with WAAM-deposited stainless steel. The objective of this study is to investigate the influence of (Nb) addition on the microstructural characteristics, mechanical properties and mechanical behavior of WAAM deposited 316 L fabricated under different build orientations and interlayer time. WAAM deposited cubes 316L stainless steel were fabricated at the three deposition orientations (0°,67°, and 90°) with interlayer times of 2 and 5 min. The addition of 2
Three-dimensional (3D) printing, referred to as additive manufacturing (AM), has transitioned from prototyping to functional applications across multiple sectors. This paper presents an integrative review as a cross-sector comparative analysis of AM technologies focusing on five domains: healthcare, food science, agriculture, aerospace defence, and architecture construction. A corpus of 180 peer-reviewed articles comprising both primary research and review articles was manually selected sector-wise from Scopus, Web of Science and Google Scholar, including 162 articles (90
