Tungsten is crucial for extreme-environment applications; however, its fabrication by additive manufacturing is challenging due to its high melting temperature, ductile-to-brittle transition temperature, oxygen sensitivity, steep thermal gradients, and strong susceptibility to cracking. In this work, a spot-melting strategy is introduced using electron beam powder bed fusion (E-PBF) to enable repeatable fabrication of highly dense tungsten with crack-free microstructures in the sectioned specimens. The approach integrates a Defocused-Focused-Defocused (DFD) spot sequence with island-based segmentation and stochastic spot jumping to minimize steep thermal gradients and mitigate solidification cracking, under high pre-heating temperatures (~1200 °C) and vacuum conditions. Using this strategy, two builds of nine cuboids each, 18 in total, were fabricated with consistently ultra-high density (~99.9304 ± 0.0152% on average), with representative specimens exhibiting crack-free microstructures, demonstrating exceptional process stability and reproducibility. Microstructural characterization reveals coarse columnar grains aligned with the build direction, a strong θ-fiber texture 001 // Z, and a dense network of low-angle subgrain boundaries, indicative of early-stage dynamic recrystallization. Direct observation of the microstructure on the as-solidified top surfaces without conventional metallographic preparation also revealed a strong 001 // Z texture, while cross-sectional characterization confirmed its persistence throughout the build thickness, indicating that surface measurements can be representative of the bulk material. Mechanical testing demonstrates that E-PBF spot-melted tungsten achieves a room-temperature ultimate compressive strength of ~1.75GPa with substantial inelastic strain, comparable to wrought tungsten and exceeding laser powder bed fusion counterparts. At elevated temperatures (800 – 1400 °C), compressive flow stresses remain below the deformation-processed wrought-rod baseline while exhibiting continued strain hardening up to εinel ≈ 0.3. These results establish spot melting in E-PBF as a reproducible pathway for fabricating brittle refractory metals.
The geometric design of lattices or microstructures has captured the interest of many researchers in recent years, mainly due to the enabling technology of additive manufacturing that allows the physical realization of such structures with ease. The flexibility in constructing lattices is enormous, adding a whole new set of degrees of freedom to the end user to control the interior of the designed model. Yet, the set of boundary representations (B-reps) compatible parametric tiles that are offered by contemporary computer-aided design (CAD) tools is surprisingly small. For example, a fairly small set of Triply Periodic Minimal Surface (TPMS) tiles, such as the gyroid, Schwarz primitive, and diamond families, is available in CAD tools, and even they are approximated as implicits.In this work, we explore a whole new set of topologies as possible B-reps compatible parametric tiles for lattices, not necessarily cuboids, that are constructed as dual representations of space filling polyhedra. The classifications of these topologies are discussed and exemplified, including using additive manufacturing, and the resulting lattices are evaluated through finite element analysis.
Laser beam powder bed fusion of polymers (PBF-LB/P) with polyamide 11 (PA11) enables structural components while limiting negative environmental impact. However, the material remains sensitive, to not yet fully understood, thermal history effects, pronounced as visible discoloration and variability in mechanical performance. This work quantifies the synergistic effect of laser power and part placement in the build chamber on color change, mechanical properties, and dimensional response of PA11 PBF-LB/P parts, and develops predictive tools to guide pre-build decisions. A structured experimental design varied laser power, build height, and lateral position. For each specimen, we measured CIELAB color coordinates, ultimate tensile strength (UTS), elongation at break (EAB), and dimensional deviations. We then built two complementary predictors: (i) a regression-based response surface across the process window and (ii) a chamber-scale thermo-kinetic simulation that computes transient temperatures and maps discoloration using an Arrhenius-type degradation law. Laser power was the primary driver of all responses, while chamber position introduced secondary but consistent effects that reflected non-uniform thermal exposure; discoloration and shrinkage were greatest toward the chamber interior. A simple color-based degradation metric combining lightness and yellowness showed the strongest association with UTS and EAB, providing a practical indicator of thermal ageing. The simulation captured the spatial pattern of degradation both in the designed experiment and for a large industrial impeller. Together, these data-driven and physics-based approaches enable prediction and mitigation of discoloration and associated material degradation, supporting robust pre-build parameter selection and process optimization.
In-situ alloying additive manufacturing (AM) has emerged as a versatile and efficient strategy for the rapid development of new alloys. Notably, when refractory particles are introduced during the in-situ alloying AM process, the resulting partially-melted particles can significantly alter the solidification behaviour and ultimately reshape the microstructural features of the fabricated alloy. However, the mechanisms by which these partially-melted particles influence microstructural evolution remain insufficiently understood. In this study, a representative binary titanium alloy, Ti–30Ta, characterised by a pronounced mismatch in physical properties, was fabricated via in-situ alloying AM. The evolution of microstructure and mechanical properties under conduction, transition, and keyholing melt-pool modes was systematically investigated. In the transition mode, partially-melted Ta particles suppressed α″-martensite formation in Ta-rich regions (~42wt.% Ta) while promoting brittle α′-martensite, leading to limited ductility. In contrast, under the keyholing mode, partially-melted Ta particles induced dislocation pinning and facilitated the formation of a high α″ phase fraction, thereby enhancing strength–ductility synergy. Thermal fluid flow simulations reconstructed melt-pool evolution under varying processing conditions, capturing the coupled effects of elemental diffusion, melt-pool morphology, and particle–matrix thermal interactions. This integrated experimental–computational study unveils the mechanistic role of partially-melted particles as active modulators of local thermal and mechanical fields, providing a new understanding of their beneficial effects under optimised conditions. The findings challenge the conventional goal of achieving full compositional homogeneity in in-situ alloying AM and offer a new design paradigm for heterogeneity-engineered, high-performance Ti-based alloys.
Proper thermal management in large-format additive manufacturing (LFAM) to maintain geometric control and mechanical integrity requires, in part, maintaining the substrate, or the previously deposited layer temperature, within a defined process window. Temperatures that are too low result in weak interlayer bonds; conversely, elevated temperatures cause geometric instability. This work presents an autonomous closed-loop control system that modulates feedrate to maintain a setpoint substrate temperature using on-gantry thermal sensing. Geometry-independent monitoring of material deposition was enabled by six radiometric cameras mounted around the deposition nozzle. An edge-deployed computer vision pipeline enabled automatic identification of layer boundaries and extraction of substrate temperature without manual region-of-interest assignment or offline workstation process, both of which preclude closed-loop control. A proportional-integral (PI) controller with Internal Model Control (IMC) tuning adjusted the feedrate based on the measured temperature error. The layer-synchronous architecture triggered control actions at layer completion events rather than fixed time intervals. The autonomous control system was validated on a Big Area Additive Manufacturing platform during printing of wood-filled polylactic acid. The experiment demonstrated that the controller successfully recovered from substrate temperatures ~20°C below the setpoint to achieve steady-state regulation of 124.3°C ± 1.8°C against a 125°C setpoint. Notably, the controller, without recalibration, was able to adapt to increasing part thermal mass by automatically reducing steady-state feedrate from 57% to 52% between two controller activation periods, early and late in the build, to maintain the same 125°C setpoint.
Although laser powder bed fusion (LPBF) offers immense design freedom, overcoming the strength-ductility trade-off in 316L stainless steel while maintaining yield stability through microstructural tailoring remains a significant challenge. In this study, a novel composite texture (CT) is successfully designed and fabricated for the first time by changing the hatch distance to control the melt pool overlap ratio. This structural architecture consists of alternating crystallographic lamellar microstructures (CLM) and polycrystalline microstructures (PCM). In-situ tensile testing revealed grain orientation-dependent deformation behavior. The CLM, containing a large fraction of <110>-oriented grains, exhibited grain rotation along specific trajectories while sustaining a pronounced twinning-induced plasticity (TWIP) effect. The CT specimen exhibits a continuous and stable macroscopic yielding behavior. Furthermore, an increased volume fraction of the CLM regions led to a higher deformation twin area fraction, thereby achieving a programmable strength-ductility synergy. This study provides a novel physical metallurgy paradigm for designing high-performance, microstructurally tailored materials via additive manufacturing.
Materials processed by fusion-based additive manufacturing undergo rapid melting and solidification process, where the thermal history inside the melt pool governs microstructure development, defect formation, and thus determines properties of as-printed parts. However, existing post-mortem analysis as well as in-situ characterization methods cannot capture the transient sub-surface temperature evolution during heating beam scanning. In this study, we propose a nanoparticle-based temperature probe to reveal temperature evolution during laser powder bed fusion (LPBF) by in-situ synchrotron X-ray diffraction. The temperature inside melt pool during laser scanning, for the first time, is directly captured, instead of relying on surface measurements or indirect inference used in existing methods. Through tracking the thermal expansion of nanoparticle lattice, temporal temperature information including heating/cooling rate during melting-solidification process is acquired. Under the experimental conditions in this work, the maximum melt pool temperature is measured as 1669 ± 95°C, with the maximum heating rate approximately 4 times the maximum cooling rate (173025°C/s vs. 47288°C/s). The feasibility of sub-surface spatial temperature mapping inside bulk material is verified by embedding nano temperature probes at specific locations. This research provides a tool to study the rapid temperature evolution inside melt pool for fusion-based additive manufacturing.
In directed energy deposition processes, the thermal history determines the quality of the manufactured part, affecting material properties, dimensional tolerances, and potential failure. Accurate finite element-based thermal process simulations help determine the best scan strategy but can be computationally expensive to perform multiple times. Data-driven models can be fast approximators but need many simulations for a dataset. This work proposes a one-shot deep learning method by training a neural ordinary differential equation (ODE) model using only one process simulation. The neural ODE model uses only features identified in the governing transient spatial heat equation and ODE solvers to integrate the results in time. Leave-all-but-one-out cross-validation confirms one simulation is sufficient to generalize well across all cases with different scan strategies, regardless of the training simulation. This simple model with one sample has an R2 accuracy around 0.9 for the best fold and evaluates the part history faster than real-time and 20x faster than the finite element-based simulation. The method is well suited for rapidly exploring different scan paths to mitigate local heat buildup in a given part geometry.
Here, we report a 2.3 GPa ultrahigh-strength and ductile H13 tool steel fabricated via parameter-optimized laser powder bed fusion (LPBF) followed by tempering. This superior strength–ductility synergy originates from a novel microstructural design strategy based on dual heterostructure in both grain structure and chemistry, realized by integrating heterogeneous grains with a cellular architecture. The heterogeneous martensitic grain structure consists of microscale fine grains surrounded by ultrafine/nanoscale grains averaging 260 nm in size. The cell walls are primarily defined by elemental micro-segregation and nano‑carbides, with negligible crystallographic misorientation and low dislocation density. After tempering at 550 °C for 2 h, this cellular structure remains well‑defined and becomes further decorated with additional nano‑carbides along the boundaries. Consequently, this integrated microstructure gives rise to dual heterogeneity in both structure and chemistry, which activates synergistic strengthening mechanisms including solid solution, precipitation, and hetero‑deformation induced (HDI) strengthening. These mechanisms collectively underpin the observed ultrahigh strength. The good ductility primarily stems from the relatively weak barrier effect of the cellular structure against dislocation motion, combined with the HDI hardening effect resulting from the dual heterostructure. This work provides insight into the design of ultra‑strong and ductile metallic materials via additive manufacturing, with significant potential for advanced tooling and structural applications.
The development of reliable predictive models for additive manufacturing (AM) remains challenging due to the limited availability of experimental data and the high costs associated with the process, particularly when working with novel alloy systems. In this study, we propose a hybrid modeling approach, that integrates finite element modeling (FEM) with artificial neural networks (ANNs) and transfer learning (TL), to predict melt pool size and defect types, specifically lack-of-fusion and keyhole porosity, and to generate processing maps for three novel aluminum alloys (AlFeCrSi, AlFeCrTi, and AlFeCrSiTi) designed for Laser-Powder Bed Fusion (LPBF). To achieve this, first, a melt pool database was generated through FEM simulations incorporating laser and material parameters. This database, containing 1386 melt pools, was trained on melt pool data to generate an initial ANN model. Subsequently, by transfer learning (TL-ANN), the model was fine-tuned using experimental measurements from 108 additively manufactured samples corresponding to three new Al-based alloys. By leveraging finite element simulation data, the TL-ANN model achieved high predictive accuracy with reduced experimental input, providing a practical framework for efficient process optimization and defect mitigation in LPBF of new materials. While the trained source model is transferable to other aluminum alloys with limited experimental datasets, the proposed hybrid methodology is broadly applicable to different metallic materials, enabling the rapid and cost-efficient generation of processing maps. Additionally, the processing maps predicting defect-type regions indicate the existence of an upper laser power threshold beyond which the formation of fully dense material becomes unlikely.
For superalloys fabricated by Laser-Powder Bed Fusion (L-PBF), the long-term service performance influenced by various mesoscale solidification microstructures remains scarcely explored (including grain size, grain shape, and crystallographic orientation). This work investigated the stress rupture behavior of three typical L-PBF-fabricated microstructures under various test conditions, namely Fine-grained Low-textured Microstructure (FLM), Crystallographic Lamellar Microstructure (CLM), and Directional Solidification Microstructure (DSM). The results demonstrate that under medium-temperature and high-stress conditions, the stress rupture life of the three microstructures ranks in the order of DSM > CLM > FLM. Under high-temperature and low-stress conditions, the corresponding order changes to CLM > DSM > FLM. Subsequently, the damage and deformation mechanisms of the three typical microstructures were systematically analyzed. Crystallographic orientation, cellular grains and high-angle grain boundaries behave differently under varying test conditions, resulting in distinct stress rupture lives and stress exponents. In additional, when the Larson-Miller Parameter (LMP) approaches 23.3 and stress reaches 650MPa, stress rupture property superiority reverses between CLM and DSM alloys. Although these critical values change with sample states or test conditions, the reversal phenomenon stays stable. Finally, this work highlights the trade-off between stress rupture life and stress exponent. For L-PBF-fabricated CLM and DSM alloys, superior stress rupture life is accompanied by a higher stress exponent, which restricts their service conditions and thus implies that their engineering applications need to balance performance and safety.
Powder-based directed energy deposition (PB-DED) has emerged as a promising additive manufacturing technology for large-scale metal components, yet its process fidelity is plagued by the complex interplay of particle-laden flow, laser energy distribution, and melt track hydrodynamics. To overcome limitations of existing models that oversimplify these multi-physics phenomena, this study presents a high-fidelity, multi-phase computational framework based on a semi-resolved Computational Fluid Dynamics and Discrete Element Method (CFD-DEM) coupling. The framework integrates (i) a kernel-based semi-resolved CFD-DEM coupling algorithm for accurate particle-fluid momentum and heat exchange; (ii) a temperature-dependent multiphase Volume-of-Fluid (VOF) method capturing melting, solidification, and dynamic wetting via a generalized contact angle model and a phase-transition model; (iii) a conjugate heat transfer formulation enabling bidirectional thermal coupling between particles, melt pool, and substrate; and (iv) a ray-tracing laser model that resolves multiple reflections and energy absorption across both discrete particles and the diffuse melt pool surface. The framework is rigorously validated against four benchmark cases, demonstrating exceptional accuracy in predicting powder stream focusing, laser-particle interaction, melt pool dynamics, contact-angle-dependent wetting, and final melt track morphology. Beyond quantitative validation, the simulations provide mechanistic explanations for PB-DED defects, including the balling effect and discontinuous interfaces. Leveraging full-process GPU acceleration, the framework enables large-scale simulations of multi-laser, multi-powder-stream PB-DED on standard desktop hardware, achieving unprecedented computational efficiency and scalability. This work establishes a robust, predictive computational platform for defect analysis, process optimization, and digital twin development in metal additive manufacturing.
In scattering-dominated ceramic vat photopolymerization (VPP), monomer-to-polymer conversion dynamically alters the optical state of the suspension, making the conventional assumption of a constant penetration depth insufficient. Unlike conventional steady-state models, here a framework couples refractive-index evolution during curing to the effective attenuation of scattering-dominated suspensions, yielding an explicit intensity-dependent penetration-depth relation. The framework is validated on alumina suspensions using three resin chemistries (HDDA, PEGDA, ETPTA), as well as variations in solid loading, particle size, and photoinitiator type. This relation reproduces the measured cure depth with a mean and maximum error of 1.32% and 4.78%, compared with 2.05% and 13.47% for conventional working-curve fitting. Lateral overcuring is then unified with the depth direction through a closed-form excess-width relation derived from a photon variance–covariance description of scattering, in which the excess width is expressed through the same penetration-depth relation, single critical exposure, and a lateral spreading coefficient β, and scales as the three-halves power of the penetration depth. It predicts the measured excess width with a mean and maximum error of 2.54% and 32.70%, compared with 8.48% and 87.81% for the empirical working-curve fitting, and β can be fixed from as few as one measurement. A higher-refractive-index AlN suspension serves as a qualitative check on refractive-index contrast. Overall, the framework establishes incident light intensity as a mechanistically interpretable control variable linking conversion-driven optical evolution to both depth penetration and lateral overcuring, enabling improved exposure selection and print fidelity in ceramic VPP.
Deviations of key process variables (KPVs) in laser powder bed fusion machines, such as laser power, scan speed, and hatch distance, from their nominal values can occur due to the drifts in machine conditions over time. These small KPV drifts may lead to changes in part quality and their mechanical properties. In this study, the impact of process variations within the tolerance limits of an EOS M290 system was investigated by emulating the individual and combined effects of ± 4% drifts in laser power and ± 2.4% drifts in hatch distance on the defect-structure and mechanical properties, including tensile and fatigue. The results showed that the tensile behavior was not significantly impacted by the KPV drifts at their tolerance limits. However, minor variations in fatigue lives were observed due to KPV drift. Furthermore, the fatigue behavior was more affected by the part location on the build plate than KPV drift, as specimens at the center exhibited smaller crack initiating defects, and consequently, had longer fatigue lives than those away from the center.
The collapse of the keyhole and subsequent gas entrapment remain critical bottlenecks in achieving defect-free and reliable laser powder bed fusion (LPBF). This paper studies a novel defect-suppression strategy utilizing a synchronous coaxial pulsed laser to stabilize the keyhole and evaluates the robustness of the strategy. Micro-CT porosity analysis reveals that the pulsed shock reduces porosity in 3D printed stainless steel samples by 85.03 By addressing the sensitivity of porosity to beam co-axiality, a robust fiber-based pulsed-CW laser coupling powder bed fusion system was developed to maintain stable coaxial delivery of the pulsed laser beam, achieving near-total densification with an absolute porosity of 0.0032%. A simulation-assisted discussion is provided to explore a possible pathway for melt-pool and keyhole flow modulation. The numerical results suggest that the equivalent periodic pressure field may alter local melt-pool/keyhole-flow behavior and contribute to pore suppression under the investigated conditions. This approach offers a reliable pathway for high-performance high-reliability additive manufacturing of metallic components.
Nickel-aluminum bronze (NAB) is a preferred material for marine additive manufacturing due to its excellent corrosion resistance. However, in the as-deposited state, particularly in laser-based processes, it suffers from a strength-ductility trade-off and insufficient corrosion resistance. These issues primarily stem from the formation of brittle β' martensite induced by rapid solidification. This paper proposes a novel composition design strategy to tailor microstructure and properties of an advanced NAB (A-NAB) fabricated via laser directed metal deposition (L-DMD) by optimizing the aluminum equivalent and solute partitioning. Unlike standard NAB (S-NAB), the designed A-NAB completely suppresses the martensitic transformation and promotes a unique decoupled growth mode of nano-precipitates. First-principles calculations and transmission electron microscopy reveal that the crystallographic mismatch between the DO3-structured κⅡ phase and the B2-structured κⅢ phase increases interfacial energy. This thermodynamically drives the separation of conventional core-shell structures into independent, dispersedly distributed nanoparticles. This unique microstructure overcomes the strength-ductility trade-off, endowing the alloy with exceptional mechanical synergy: a ductility of 27% and a tensile strength of 654 MPa in the as-deposited state. Furthermore, electrochemical studies indicate that the corrosion rate of A-NAB is two orders of magnitude lower than that of its cast counterparts. This improvement is attributed to the elimination of martensite-related micro-galvanic couples and the formation of a dense corrosion product film, which is further facilitated by reduced internal film stresses resulting from the decoupled nano-precipitates. This study provides a new pathway for fabricating high-performance, heat-treatment-free marine components via additive manufacturing.
The microstructure and tensile behavior of a laser powder bed fusion (LPBF) processed, in situ alloyed Ti–2.5CoCrMo, fabricated using blended CP-Ti and CoCrMo powders, in the as-built and heat-treated conditions, is examined. The microstructure of the as-built alloy consists of solute-depleted regions that contain α-lamellae and fine α′–lath, and solute-rich regions that contain ω precipitates embedded within β grains. Heat treatment, which involves annealing at 500 °C for 1 h followed by quenching, leads to moderate coarsening of α-lamellae, transformation of α′ to α, and heterogenous nucleation of fine α-grains near ω-precipitates within the prior β grain. These microstructural modifications result in near-doubling of the ductility (vis-à-vis the as-fabricated alloy) with a concomitant and marginal reduction in the tensile strength. Comparisons with other solute lean Ti alloys and pure Ti reveal that the Ti–2.5CoCrMo alloy has the best combination of strength and ductility in the heat-treated state. The relatively high strength and ductility of this alloy is attributed to the asynchronous but coordinated deformation in the hierarchical microstructure and efficient slip transfer across different phases, respectively.
