Editorial: High energy additive manufacturing of advanced materials

High Energy Additive Manufacturing (HEAM) primarily utilises high-energy-density heat sources such as laser beams, electron beams, and high-energy arcs/plasmas to achieve near-net shaping of complex components through layer-by-layer deposition. Compared with traditional subtractive and formative manufacturing, HEAM exhibits significant technological and industrial advantages in the flexible and rapid fabrication of high-performance complex structures, integrated components, and difficult-to-machine materials. However, during the HEAM process, steep thermal gradients, severe non-equilibrium metallurgical behavior during solidification, molten pool dynamic instabilities, and high as-built surface roughness continue to severely limit component densification, microstructural homogeneity, and in-service performance. To overcome the application bottlenecks of advanced structural and functional materials in HEAM, comprehensive multi-dimensional investigations are urgently needed across high-throughput material design, intelligent in-situ process monitoring, process-microstructure tailoring, mechanical structure innovation, and advanced post-processing technologies. This research topic, entitled “High Energy Additive Manufacturing of Advanced Materials,” compiles eight original research articles and prospective contributions, comprehensively highlighting the latest advancements across the entire lifecycle from material design, process optimization, and melt pool simulation to structural design and post-processing.

The deep integration of data-driven methods and artificial intelligence has established new paradigms for the efficient design of advanced alloys and the intelligent monitoring of fabrication processes. In alloy design, An et al. proposed a machine learning-based computer-aided design framework for high-performance titanium alloys, constructing a 28-dimensional dataset that integrates the fundamental physical properties of 18 elements with multi-stage heat treatment regimens. By employing a Gaussian Process Regression (GPR) model, highly accurate prediction of ultimate tensile strength was achieved (R² = 0.93 on the test set), and a novel composition with promising mechanical performance, Ti6Al4V0.3Si5Mo2.3Sn0.1Nd, was screened via intelligent space search. In online process monitoring, addressing the challenge that melt pool temperature fluctuations compromise dimensional and mechanical accuracy in Laser Solid Forming (LSF), Wang et al. developed a real-time melt pool temperature prediction model based on a Particle Swarm Optimization-tuned Long Short-Term Memory (PSO-LSTM) network. Leveraging in-situ infrared thermography data, the model achieved superior predictive accuracy under sharp temperature fluctuations compared with conventional models, providing a reliable foundation for real-time closed-loop temperature control systems in additive manufacturing.

Unveiling the physical metallurgy mechanisms of HEAM, optimizing processing windows, and assessing feedstock recyclability are crucial for ensuring component quality. Regarding formation dynamics and microstructural control, Wang et al. systematically investigated the effects of laser energy density (LED) on the single molten track geometry and microstructural evolution in laser powder bed fusion (L-PBF) of 316L stainless steel, establishing an optimal processing window of 200–500 J/mm³ and elucidating the micro-mechanisms governing the transition from columnar dendrites to equiaxed cellular grains driven by Marangoni convection and thermal gradients. Wang et al. fabricated Fe-Ni-Ti low-expansion composite coatings on AISI 1045 steel via laser deposition; at an optimal energy density of 97.2 J/mm², a relative density of 99.1% and an ultra-low porosity of 1.2% were achieved, while uniformly dispersed in-situ TiC reinforcements increased the coating microhardness to 2.24 times that of the substrate and markedly enhanced wear resistance. To address feedstock degradation during recycling, Gu et al. conducted 12 reuse cycles in selective electron beam melting (SEBM) of pure copper to systematically reveal oxygen accumulation behavior. The findings indicated that rapid cooling rates and melt pool convection effectively inhibited oxide segregation at grain boundaries, yielding uniformly dispersed ∼50 nm copper oxide nanoparticles within the matrix and thereby retaining excellent ductility (>51%) and electrical conductivity even at an oxygen content of 0.4341 wt.%.

Exploiting the geometric freedom of additive manufacturing to expand novel topological architectures and overcoming the surface quality bottleneck of as-built parts are essential for achieving high-performance service. Inspired by the geometry of boomerangs, Liu et al. designed a double arrowhead triangular mechanical metamaterial structure with negative Poisson’s ratio (NPR) characteristics, identifying the optimal angle parameters for maximizing the auxetic effect and low-velocity impact energy absorption through theoretical mechanics derivations and dynamic finite element simulations. To tackle the common challenge of high surface roughness in additive manufacturing parts, Lu et al. systematically compared the effects of sandblasting, electropolishing, chemical polishing, and abrasive flow machining on L-PBF-fabricated Ti6Al4V, demonstrating the advantage of sandblasting in reducing surface roughness with minimal mass loss, as well as the unique capability of abrasive flow polishing in smoothing step defects and unmolten powder inside complex curved channels. Pan et al. introduced shear-thickening fluids into an integrated lapping and polishing process for hard and brittle SiC ceramic substrates; by modulating the dynamic response of fluid viscosity to shear rates, the surface roughness was significantly reduced from 451 nm to 9.7 nm, providing an innovative process strategy for the ultra-precision surface finishing of hard, brittle components.

In summary, the collection of articles in this Research Topic comprehensively showcases collaborative innovative achievements across the entire chain of High Energy Additive Manufacturing, spanning material and structural design, physical process regulation, and surface post-treatment strengthening. We would like to express our heartfelt gratitude to all contributing authors for their valuable research, the dedicated reviewers for their rigorous evaluations and constructive comments, and the editorial staff of Frontiers in Materials for their continuous support throughout the organization and publication of this research topic.

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Footnotes

Edited and reviewed by: Nicola Maria Pugno, University of Trento, Italy