The appeal of HEAs rests on four core effects that researchers have identified as the source of their exceptional behaviour. First, the sheer configurational entropy of mixing many elements stabilises single-phase solid solutions instead of brittle intermetallic compounds. Second, atomic diffusion proceeds sluggishly through the crowded lattice, slowing phase transformations. Third, the wildly different atomic sizes of the constituent elements create severe lattice distortion, which impedes dislocation motion and strengthens the material. Fourth, the so-called cocktail effect produces property combinations that exceed what any single element could deliver. Together, these effects give HEAs a performance envelope that has attracted intense interest from the aerospace, automotive, energy, biomedical and electronics sectors.

Yet conventional processing routes — vacuum arc melting, induction casting, mechanical alloying and physical vapour deposition — struggle to exploit that potential fully. Casting HEAs requires repeatedly remelting and flipping the melt to achieve chemical homogeneity, and post-processing steps are usually needed to tailor the microstructure. Some HEAs demand extremely high cooling rates to suppress unwanted intermetallic phases. These constraints make complex-shaped components difficult and large-scale production impractical. Additive manufacturing changes the equation: it builds near-net-shape parts with minimal waste, offers unprecedented design freedom, and — crucially for HEAs — imposes heating and cooling rates so extreme that the resulting microstructures differ fundamentally from anything a foundry can produce.

Laser powder bed fusion, or LPBF, is the most intensively studied technique. A focused laser selectively melts thin layers of powder, with cooling rates reaching 10^5 to 10^7 kelvin per second. At those speeds, atomic long-range diffusion is effectively frozen out, elemental segregation is suppressed, and many HEAs stabilise as metastable single-phase face-centred cubic or body-centred cubic solid solutions rather than decomposing into equilibrium phases. The process also generates dense dislocation networks and fine cellular substructures of roughly 0.2 to 1.0 micrometres within the primary grains. Reported results are striking: LPBF-fabricated CoCrFeNi showed nearly twice the yield strength of its cast counterpart, and the technique has demonstrated feasibility for refractory HEAs such as WMoTaNbV, candidates for aerospace and chemical-industry service. The trade-offs are real, however — thermal cycling, residual stresses and microcracking near the print–substrate interface remain persistent hazards, demanding careful optimisation of laser energy density.

Selective electron beam melting, or SEBM, takes a different approach. Operating in high vacuum with a powerful electron beam, it preheats the powder bed to between 600 and 1100 degrees Celsius using a defocused beam pass. That preheating reduces thermal gradients and residual stresses, which is invaluable for refractory HEAs that are notoriously crack-prone under laser processing. The vacuum environment eliminates oxidation and contamination, permitting the use of reactive, high-melting-point elements typical of HEA chemistries. SEBM’s cooling rates of 10^4 to 10^5 kelvin per second promote equiaxed grains rather than the fine dendritic structures of LPBF, often yielding refined microstructures and better mechanical properties than casting. But the combination of high vacuum and beam power can selectively evaporate volatile elements such as aluminium, manganese and copper, silently shifting the alloy composition — a problem that demands tight process control.

Directed energy deposition, or DED, uses a laser, electron beam or arc to melt powder or wire feedstock as it is deposited, allowing much larger components and higher deposition rates than powder-bed methods. Its intermediate cooling rates of 10^2 to 10^4 kelvin per second encourage epitaxial columnar grain growth along the build direction, but repeated thermal cycling can trigger dynamic recrystallisation that partially breaks up those columnar structures into finer grains. The sluggish diffusion characteristic of HEAs limits elemental partitioning and helps stabilise single- or dual-phase solid solutions. Notably, DED-produced CoCrFeMnNi showed greater strength and elongation than its LPBF counterpart, an effect attributed to in-situ annealing during thermal cycling that relieves tensile stresses. Defects such as lack-of-fusion, incomplete powder melting and interlayer cracking remain challenges, and the columnar grains and crystallographic texture can introduce mechanical anisotropy.

Perhaps the most radical departure is additive friction stir deposition, or AFSD, a fully solid-state process that combines friction stir welding principles with layer-by-layer building. Frictional heating and severe plastic deformation soften the feedstock above its solidus temperature but below melting — typically around 0.7 to 0.8 of the absolute melting temperature. Because no liquid phase ever forms, the solidification defects that plague fusion-based printing — hot cracking, keyhole porosity, lack-of-fusion pores — simply cannot occur. Severe shear deformation instead drives dynamic recrystallisation, producing highly refined equiaxed grains. In Al0.35CoCrFeNi, phase variations between the advancing and retreating sides of the deposit generated Al-rich BCC particles that promoted Zener pinning and restricted grain growth. AFSD also prevented brittle intermetallic formation at particle–matrix interfaces in AA5083 reinforced with CoCrFeNi particles, boosting both strength and ductility. Its current limitation is practical: the process is best suited to lower-melting-point materials, and high-temperature HEAs require wear-resistant tooling that is still under development.

None of these processes works without high-quality feedstock, and the review identifies powder and wire quality as a decisive factor in whether HEAs can ever reach commercial scale. Gas atomisation is the dominant route for powder-bed AM because it yields pre-alloyed, spherical, compositionally homogeneous powders with suitable density, flowability and particle size distribution. Mechanical alloying, by contrast, produces contaminated, irregularly shaped powders unsuited to powder-bed processing, while water atomisation is cheap but yields irregular particles with poor flowability. On the wire side, researchers have pioneered a novel combined cable wire — seven 0.6-millimetre wires (two nickel, two aluminium, one iron, one cobalt and one 304 stainless steel) bundled around a central core — as filler for wire-arc additive manufacturing of non-equiatomic AlCoCrFeNi, enabling high deposition rates and large near-net-shape components. The review notes bluntly that the commercial use of HEAs has not yet succeeded, primarily because feedstock availability and comprehensive alloy qualification lag behind the science.

The mechanical performance being reported from printed HEAs is remarkable. LPBF-fabricated HEAs have achieved yield strengths approaching 1.3 gigapascals with uniform elongations near 14 per cent, surpassing many additively manufactured metallic alloys, thanks to alternating FCC/BCC nanolamellar structures in which strong BCC lamellae provide strengthening and strain hardening. An SLM-built eutectic HEA, Al18Co30Cr10Fe10Mo1Ni30W1, retained outstanding strength from minus 196 to 760 degrees Celsius — among the highest ever reported for AM-produced HEAs — owing to ultrafine eutectic lamellae of 150 to 200 nanometres. Even more striking are the engineered heterostructures: a CoCrNi–Fe50Mn30Co10Cr10 laminated HEA with a mortise-and-tenon architecture, mimicking traditional woodworking joinery, achieved a yield strength roughly 36.5 per cent above rule-of-mixture predictions, with interface strengthening contributing about 23 per cent of the total. A mechanically interlocked network in SLM-fabricated Fe28Co29.5Ni27.5Al8.5Ti6.5 delivered a tensile strength of about 1152 megapascals with 28 per cent elongation. At cryogenic temperatures, metastable HEAs exploit transformation-induced plasticity: an AMed Co34Cr20Fe34Ni6Mn6 alloy showed an anomalous reversible gamma-to-epsilon transformation at 77 kelvin that roughly doubled tensile strength while preserving ductility.

The review’s authors argue that additive manufacturing has evolved from an alternative fabrication method into a powerful enabling technology, shifting HEA development toward a microstructure-by-design philosophy in which process parameters themselves become design tools. Machine learning is emerging as the accelerant: algorithms from random forest to artificial neural networks have been used to predict phases, screen high-hardness compositions in the AlCoCrCuFeNi system, and forecast the high-temperature yield strength of MoNbTaTiW and HfMoNbTaTiZr alloys at 800 and 1200 degrees Celsius with strong agreement against experiment. Companies are already adopting ML for alloy design in AM. Significant challenges remain — defects, residual stresses, anisotropy, elemental evaporation, phase stability, and the scarcity of data on fatigue, creep, fracture toughness, corrosion and irradiation resistance. Most published work still centres on the CoCrFeMnNi family, while lightweight and refractory HEAs remain underexplored. But the trajectory is clear: as process optimisation, real-time monitoring, multi-scale characterisation and data-driven design mature, printed high-entropy alloys are positioned to move from laboratory marvels to load-bearing components across aerospace, energy, biomedical, nuclear and defence industries — a transformation that could redefine what engineers expect from metal.

Subject of Research: Materials Science

Article Title: Process–microstructure–property relationships in additive manufacturing of high-entropy alloys: recent advances, challenges and future perspectives

Article References: Dehury, R. K., Gupta, V., Joshi, Y. G., & Victor, M. T. (2026). Process–microstructure–property relationships in additive manufacturing of high-entropy alloys: recent advances, challenges and future perspectives. Frontiers in Materials, 13, Article 1964268. https://doi.org/10.3389/fmats.2026.1964268

Image Credits: AI Generated

DOI: 10.3389/fmats.2026.1964268

Keywords: high-entropy alloys, additive manufacturing, laser powder bed fusion, microstructure, mechanical properties, machine learning, refractory alloys, cryogenic deformation, eutectic alloys, metal 3D printing, Process, property

Arc-DED is one of the most economical additive manufacturing routes for large metallic structures. Instead of a laser, it uses an electric arc to melt wire feedstock, achieving high deposition rates at low cost. Gas tungsten arc-DED (GTA-DED), the variant studied here, employs a non-consumable tungsten electrode and produces a smoother transition of molten material, making it attractive for high-quality fabrication in alloys ranging from aluminum to nickel and titanium. Yet the process is notoriously sensitive to disturbance. Heat accumulates layer upon layer, substrate conditions vary, and the surface state of each previously deposited bead changes the geometry the torch encounters next. When the deposited width drifts from the design value, defects such as porosity, poor fusion, and bead collapse appear between adjacent beads. When the actual height drifts from the programmed lifting height of the torch, the working distance between electrode and part degrades—too short and the tungsten electrode can collide with the surface, too long and shielding gas fails to protect the pool or the arc simply extinguishes, halting production.

Previous strategies to tame these deviations—offline heat-input models, path compensation, interlayer active cooling—share a common weakness: they do not respond to the dynamic geometry of the molten pool as it forms. Earlier sensing approaches each captured only half the picture. Infrared cameras can map pool isotherms but are expensive at the required frame rates and resolutions. Passive vision, using a CCD camera and image processing, reliably measures deposition width but struggles to track height without a side-mounted camera that collides with complex parts. Electrical sensing of arc voltage, by contrast, cheaply and almost instantaneously reflects the distance from torch to deposition surface—effectively the height—but says nothing about width. The innovation of the new study is to fuse the two: arc voltage for height, vision for width, each sampled at the moment it performs best.

Raw arc voltage signals, however, are noisy. Fluctuations in the conductive channel of the arc plasma corrupt the measurement, and classical threshold-based wavelet filters, derived under idealized Gaussian white-noise assumptions, either over-filter and erase faint signal features or leave too much residual noise. The team’s answer was to let an ant colony optimization algorithm search for the optimal wavelet threshold. Inspired by the pheromone trails ants lay to find shortest paths, the algorithm iteratively updates pheromone concentrations across candidate thresholds, guided by the mean squared error of the filtered signal. Because the search is global rather than fixed by a closed-form expression, the filter adapts to the actual character of each signal, preserving transient peaks and abrupt changes that carry real physical information about deposition height stability.

Calibration posed a subtler challenge. In a thick-walled, multi-layer, multi-bead part, the arc does not look the same everywhere. On the first layer, heat dissipates efficiently into the substrate and no closed-loop control is needed. On the second layer, the arc straddles the first layer’s surface and the substrate. By the third layer, the arc touches the previous layer’s surface and side wall, and from the second bead onward it spans two deposited layers simultaneously. Each morphology produces distinct electrical signatures, so a single arc-length model would be hopelessly inaccurate. The researchers therefore constructed three separate plane-fitting models linking peak arc voltage, arc length, and peak current—one for the first bead of the second layer, one for the first bead of the third layer, and one for subsequent beads—and deployed the appropriate model as deposition progressed.

Vision sensing required equally careful choreography. The system uses pulsed current, and at peak current the arc blazes so brightly that it saturates the camera’s dynamic range, drowning the molten pool in glare and reflections from neighboring beads. At base current, arc-light interference drops dramatically and image quality improves. The team therefore timed every image capture to the eightieth millisecond after the current transitioned from base to peak, using a camera fitted with a 25 mm lens, a 2 percent neutral density filter, and a narrow-band 685 nm filter. Image processing proceeded through Gaussian filtering to suppress noise, a Laplacian operator to detect pool edges, and a Hough transform to extract them, with two small 50-by-380-pixel windows positioned at the tail of the molten pool—far enough from the arc glare at the pool head, yet not so far back that solidification introduced lag. A chessboard calibration, cross-referenced against the arc-voltage-derived arc length, converted pixel coordinates into millimeters, yielding a width monitoring error below 0.04 mm.

With sensing in place, the control architecture split the problem in two. A model reference adaptive controller (MRAC) regulated wire feeding speed to stabilize deposition height: a PID controller computed wire-speed corrections from the deviation between detected and reference arc voltage, while an adaptation mechanism continuously retuned the PID gains based on sensitivity functions, allowing the controller to track a time-varying nonlinear process. In parallel, a self-tuning fuzzy controller (FSTC) adjusted peak current to hold the pool width at target. Rather than fixing the fuzzy controller’s quantization and scale factors in advance, the FSTC recalculated them on the fly from the width error and its rate of change, following rules distilled from expert experience and requiring no precise quantitative model of the process. Simulation of the width controller showed that when the target pool width jumped from 5 to 5.5 mm, the system tracked the change within five seconds without significant overshoot.

The experimental contrast was striking. With constant process parameters, a ten-layer, ten-bead thick-walled part of ER70S-6 steel wire on Q235B substrate grew increasingly erratic: peak arc voltage climbed layer by layer as height deviations accumulated, marginal beads deposited taller than interior beads, and the pool width drifted from 4.5 to about 5.3 mm as heat dissipation conditions evolved. By the eleventh layer, the tungsten-to-substrate distance exceeded the maximum at which an arc could be initiated, and the process simply stopped. Dents pocked the termination region, fusion lines between beads were irregular, and the finished part showed misalignment and bulging, with a height deviation reaching 5 mm across the part.

Under closed-loop control, the same system printed a fifty-layer, ten-bead part continuously and without manual intervention. The MRAC held arc voltage deviations to a maximum of roughly 0.27 to 0.31 volts during stable burning, while the FSTC pinned the molten pool width near 4.5 mm regardless of layer number, with a maximum absolute width error no greater than 0.45 mm and a mean squared error below 0.25 mm². Three-dimensional scanning of the finished components told the definitive story: height deviations of only 0.3 mm after fifty layers and width deviations of just 0.1 mm, compared with 5 mm and 0.7 mm respectively for the open-loop part. The top surface was smooth, bead-to-bead consistency within each layer was excellent, and the dents and defects that plagued constant-parameter printing vanished.

The authors are candid about limits. The filtering algorithm currently applies only to direct-current arc voltage signals in steel; highly reflective metals such as aluminum, typically welded with alternating current, will demand new denoising and vision algorithms to suppress noise and glare. The demonstration parts were simple cubes, and extending closed-loop control to curved geometries will strain the arc-length models, since curved trajectories continuously shift local heat accumulation, wire position, and arc shape. Still, the strategy offers a practical path forward: as a data-driven software solution, it can be integrated into existing industrial control systems through modest secondary development, without expensive hardware replacement or production downtime. For an industry seeking to print large, accurate metal parts at low cost, two humble sensors and a pair of cleverly tuned controllers may prove to be the difference between a promising laboratory process and a factory-floor reality.

Subject of Research: Collaborative closed-loop control of deposition width and height in gas tungsten arc-directed energy deposition using integrated arc voltage and vision sensing

Article Title: Collaborative control of deposition width and height for thick-walled parts in arc-directed energy deposition via integrating arc voltage and vision sensing

Article References: Cai, Y., Chen, D., Chen, H., Zhang, G., Pan, Z., & Xiong, J. (2026). Collaborative control of deposition width and height for thick-walled parts in arc-directed energy deposition via integrating arc voltage and vision sensing. Advanced Materials Joining, 1(1), Article 15. https://doi.org/10.1007/s44500-026-00017-w

Image Credits: AI Generated

DOI: 10.1007/s44500-026-00017-w

Keywords: arc-directed energy deposition, GTA-DED, additive manufacturing, arc voltage sensing, vision sensing, closed-loop control, model reference adaptive control, fuzzy control, molten pool monitoring, thick-walled parts, wire arc additive manufacturing, forming accuracy

At the heart of the work is a fundamental challenge in additive manufacturing: the properties of a 3D-printed metal part depend intimately on its grain structure, the spatial arrangement and orientation of crystalline regions separated by grain boundaries. Fine grains typically enhance strength and toughness through grain-boundary strengthening, while larger or anisotropic grains can improve ductility and creep resistance. Controlling these attributes during printing requires understanding how processing conditions such as laser scanning velocity, melt pool geometry, and heat-affected zone dimensions translate into grain evolution, a relationship complicated by vast multidimensional parameter spaces and non-linear thermal dynamics.

The research team, led by Dipayan Sanpui and Subramanian K.R.S. Sankaranarayanan, built their framework on a repository of 1,524 successfully retrieved three-dimensional polycrystalline microstructures generated with the open-source SPPARKS kinetic Monte Carlo code. These simulations modeled hundreds of laser passes over a substrate using a modified Potts Monte Carlo approach, capturing curvature-driven grain growth, melting, remelting, and nucleation in the heat-affected zone. Rather than relying on abstract statistical descriptors such as two-point statistics or chord length distributions used in earlier studies, the team extracted physically interpretable features directly from two-dimensional slices of the simulated structures.

Four descriptors formed the quantitative vocabulary of the study: average grain size, surface-to-volume ratio, sphericity, and roundness. Grain size serves as a first-order measure linked directly to mechanical performance through the Hall-Petch relationship, while surface-to-volume ratio distinguishes microstructures with similar grain dimensions but different boundary complexity. Sphericity captures overall grain compactness and elongation, whereas roundness reflects the sharpness of grain edges and corners, quantities that differ when two grains share similar elongation but diverge in boundary smoothness due to solidification dynamics. The team computed these measures directly from grain identifiers in the SPPARKS output files, streamlining integration into automated materials design pipelines.

With the features extracted, the researchers framed the problem as supervised regression, using seven processing conditions, including scanning velocity, scanning pattern, molten zone width, depth, and tail length, and heat-affected zone width and tail length, as inputs. They trained and compared multiple algorithms: Random Forest, Gradient Boosting Machine, Extreme Gradient Boosting (XGBoost), a feed-forward neural network, and a one-dimensional convolutional neural network, with hyperparameters optimized through six-fold grid search cross-validation and Keras-Tuner’s Hyperband search for the neural architectures.

Tree-based ensemble methods consistently outperformed the neural networks. XGBoost emerged as the best overall performer, achieving a coefficient of determination of 0.977 for grain size prediction, 0.946 for surface-to-volume ratio, 0.954 for sphericity, and 0.957 for roundness on test data, with a mean absolute error of just 6.62 pixels for grain size. The authors attribute grain size’s high predictability to its status as a first-order reflection of the temperature distribution across the simulation domain, while shape descriptors such as sphericity and roundness depend on secondary morphological effects, including grain boundary curvature and irregular grain interactions, that are harder to infer from processing conditions alone.

A particularly striking finding emerged from the parity plots: grain size predictions split into two distinct clusters corresponding to different microstructural regimes. The first cluster, with average grain sizes between 0 and 450 pixels, represented anisotropic structures of alternating equiaxed and elongated grains formed at higher scanning velocities and broader heat-affected zones. The second cluster, spanning 600 to 1200 pixels, arose from low-velocity, narrow-heat-affected-zone conditions that promote localized remelting and coarser, irregular grains. When trained and evaluated within the well-behaved first regime, the model reached an R² of 0.982 with an error of only 2.305 pixels; in the morphologically heterogeneous second regime, accuracy dropped to R² of 0.561, revealing where the surrogate’s reliability limits lie.

To open the black box, the team applied Shapley Additive exPlanations (SHAP), a game-theory-based framework that quantifies each input feature’s marginal contribution to predictions. The analysis showed that scanning velocity and heat-affected zone characteristics dominate grain size outcomes globally, consistent with established solidification theory in which thermal gradients, cooling rates, and grain-boundary mobility govern grain evolution. At high velocities, scanning speed and HAZ tail length controlled the outcome, with rapid cooling producing fine, anisotropic grains. At minimum velocity, scanning direction and HAZ tail length became the primary drivers: cross-hatch patterns induced multidirectional heat flux and rapid cooling that favored clusters of fine equiaxed grains, while unidirectional scanning promoted elongated, directionally aligned grains.

Beyond interpretation, the framework demonstrated practical utility for process optimization. By filtering candidate parameter combinations according to the relative error between predicted and desired grain sizes, the researchers identified viable alternative processing conditions under fixed maximum or minimum laser velocities. Under high velocity, neighboring points in parameter space with variations in melt-zone geometry yielded nearly identical grain structures, suggesting flexibility in achieving consistent microstructures. Under low velocity, HAZ tail length and scanning direction offered the most meaningful degrees of freedom. The computational payoff is substantial: simulating a single 100 × 100 × 100 site microstructure took roughly 25 minutes across four compute nodes, while the trained models deliver predictions in minutes once trained.

The authors envision their approach as a foundation for digital twins and closed-loop process control, in which sensor-derived process variables feed continuously into trained models for online quality monitoring and adaptive parameter optimization. They acknowledge limitations, notably that featurization currently operates on two-dimensional slices rather than full three-dimensional volumes, and they point toward extending the framework to experimental microscopy images, inverse design, and active-learning-based calibration as next steps. By coupling high-fidelity physics simulation with interpretable machine learning, the study illustrates how explainable AI can transform additive manufacturing from a process of empirical trial and error into a predictive, quantitatively grounded engineering discipline.

Subject of Research: Interpretable machine learning prediction of process–structure relationships in metal additive manufacturing using kinetic Monte Carlo microstructure simulations

Article Title: Data-driven discovery of process–structure relationships in additive manufacturing via featurization from kinetic Monte Carlo simulations and interpretable machine learning

Article References: Sanpui, D., Chan, H., Koneru, A., Banik, S., Manna, S., & Sankaranarayanan, S. K. (2026). Data-driven discovery of process–structure relationships in additive manufacturing via featurization from kinetic Monte Carlo simulations and interpretable machine learning. Machine Learning with Applications, 25, Article 100985. https://doi.org/10.1016/j.mlwa.2026.100985

Image Credits: AI Generated

DOI: 10.1016/j.mlwa.2026.100985

Keywords: additive manufacturing, kinetic Monte Carlo, machine learning, microstructure, XGBoost, SHAP, grain size, process-structure linkages, featurization, surrogate modeling, explainable AI, metal 3D printing