Fused deposition modeling (FDM) faces significant challenges in simultaneously achieving high forming efficiency and surface quality when processing models with complex geometric features, such as multibranch structures and independent holes. This article proposes an adaptive path planning method for such models. First, an adaptive partitioning and hybrid infill strategy based on structural boundary features is developed to reduce the dimensional complexity of cross-sections and optimize local paths. Second, a hybrid intelligent algorithm integrating Particle Swarm Optimization and Simulated Annealing is constructed to perform a two-level, “intra-region and inter-region” decoupled optimization, mitigating premature convergence common in single algorithms when handling large-scale discrete nodes. In addition, the synergistic influence of process parameters on print quality is investigated through transient thermodynamic simulations combined with response surface experiments, and the optimal parameter set is calibrated. Physical printing tests on an S-shaped guide plate and an H-shaped structural model demonstrate that the proposed method achieves an average reduction in surface roughness (Ra) of approximately 14% and 25% relative to traditional contour offset and zigzag paths, respectively. Microscopic defects at feature intersections are reduced, and the dimensional accuracy of the models is improved. This study provides a feasible process optimization framework for efficient and accurate FDM fabrication of models with complex geometric features.
Monolithic multi-layer V-corrugated sandwich panels with two distinct stacking configurations (overlapping and interlacing laminations) were additively fabricated via fused deposition modeling (FDM) using polylactic acid (PLA) filaments. Quasi-static flatwise compression testing demonstrates that interlacing laminates exhibit 50% greater flatwise compressive strength than their overlapping counterparts. Combined mechanical characterization and force transmission analysis demonstrate divergent failure mechanisms between the two configurations: interlacing laminates undergo core-dominated crushing failure, as their vertical load-bearing vertices are perfectly aligned; by contrast, overlapping laminates undergo face-sheet bending failure owing to staggered load-bearing points. During flatwise compression, interlacing structures exhibit progressive layer-by-layer collapse, which endows them with superior damage tolerance and energy absorption capability. In contrast, overlapping structures suffer sudden global catastrophic failure upon reaching the critical load threshold. A parametric tuning strategy targeting core wall thickness and interlayer sheet thickness is proposed to enhance the flatwise compressive strength of corrugated sandwich panels, and the underlying failure mechanisms are systematically validated. Benefiting from the core-crushing-dominated failure mode, interlacing laminates also deliver enhanced edgewise and sidewise compressive performance compared with overlapping designs. Nevertheless, the ultimate load-bearing capacity of both lamination designs is governed by the total number of stacked layers, whereas their compressive strength is primarily dictated by stacking geometry. This study clarifies how lamination stacking configurations modulate the triaxial compressive responses of microscale FDM-printed sandwich structures and delivers design guidelines for lightweight, high-performance 3D-printed sandwich panels.
Because of its exceptional mechanical strength, thermal stability, and precision, selective laser melting (SLM) is finding widespread adoption in additive manufacturing (AM). In particular, the SLM processing of Ti6Al4V alloy is well-suited for applications that require high-performance with weight savings, high structural integrity, and durability for long-term applications. However, the wide range of process parameters and complex thermo-physical phenomenon involved significantly affect its torsional and torsional-fatigue properties. This study investigates the torsional performance and fracture behavior of SLM-printed Ti6Al4V alloy components under static torsional loading and high-cycle fatigue conditions. The tests were performed through 3D-printing of samples to investigate the mechanical response properties and fracture behavior of SLM-Ti6Al4V samples under torque-controlled conditions, followed by fractography analysis. Unique mechanical response characteristics were observed in the torsional stress–strain loading curves. The torsional performance parameters at the neck point were determined to be: torsional yield shear stress (τ y ) of 588 MPa, ultimate torsional shear stress (τ u ) of 1108 MPa at a twist angle of 24.58°, a shear modulus (G) of 42.85 GPa, and torsional shear stiffness of 1.7 kNm/rad. The ultimate shear stress (τ u ) is higher than what is reported so far, while the torsional yield shear stress (τ y ) obtained here is comparable to previous SLM components and higher than the wrought alloy values. Post-peak region behavior and fractography surface morphology features showed strain softening behavior as the crucial cause of the fractured surface of the Ti6Al4V alloy samples. Similarly, the torsional-fatigue strength of the SLM-Ti6Al4V alloy sample was found to be in the range of 97 MPa and 145 MPa under 100,000 cycles. These findings confirm that the torsional properties of the SLM-Ti6Al4V samples meet the performance requirements for demanding high-performance applications, offering reliable strength and stability under torsional loading conditions.
Large-scale robotic 3D printing (LSR3DP) has emerged as a promising technology for the production of customized architectural façade components, enabling geometric freedom without the need for dedicated molds or tooling. To date, most experimental façade applications have relied on PETG due to its favorable printability, despite the fact that this material was originally developed for packaging applications rather than building envelopes. This study investigates the suitability of transparent polymer materials for LSR3DP façade systems through a comparative assessment of PETG, polycarbonate (PC), polymethyl methacrylate (PMMA), and the bio-based polymer Durabio. The materials are evaluated according to both façade-performance and manufacturing-related criteria, including heat resistance, impact resistance, resistance to photo-oxidation, reaction to fire, and production ease. The methodology combines literature review, numerical thermal simulations, accelerated weathering experiments, and reaction-to-fire testing adapted from EN ISO 11925-2. Thermal simulations were conducted for different climatic contexts and façade configurations, while artificial weathering experiments assessed optical degradation through Yellowing Index measurements. Fire behavior was evaluated using small-scale flame exposure tests on 3D-printed specimens. The results demonstrate significant differences among the investigated materials. PETG exhibits the best printability but limited thermal resistance and poor resistance to photo-oxidative aging without stabilization treatments. PC provides superior heat and impact resistance together with favorable fire behavior, although its high thermal shrinkage complicates large-scale printing. PMMA and Durabio show excellent optical stability and long-term durability, as well as moderate printability, but both display less favorable fire behavior. The study highlights that no single material optimizes all performance criteria simultaneously, emphasizing the need for application-specific material selection in robotic 3D-printed façade systems. More broadly, the research contributes a comparative framework for evaluating transparent polymers in architectural Additive Manufacturing (AM) and identifies key challenges for the industrial adoption of digitally fabricated façade technologies.
This article addresses the issues of low shear strength and poor side surface roughness in 316L powder bed fusion caused by unoptimized key parameters. The process has been analyzed with an improved deep belief network to optimize variables such as feed rate, laser speed, power, powder preheating temperature, layer thickness, and laser radius. Constrained by actual processing limits, an selective laser melting (SLM) model is constructed to fit energy density and minimize side roughness. To solve such questions, contrastive divergence method has been applied in deep belief neural-network named CD-DBN. The adaptive hierarchical learning rate algorithm autonomously adjusts the number of hidden layers by identifying solution results to achieve better fitness, also shown in CD-DBN, which can get more accurate results while reducing the difficulty of solving. CD-DBN algorithm is trained on 12,000 training datasets and integrated with environment and printing process monitoring data; the optimal parameters are achieved and tested with nearest feasible values. A power blade has been used in verifying the result. Results indicate side roughness can be optimized to 5.34 μm and energy density can reach 16.001 by CD-DBN, which got a 17.85% improvement compared with the result calculated by other method, and the error rate of prediction of CD-DBN can be below 0.2%. This research provides a reliable way for improving the industrial application of SLM in aeronautics and astronautics fields.
Fatigue performance remains a major barrier to the deployment of laser powder-bed fusion (LPBF) high-strength aluminum alloys, because cyclic loading amplifies the detrimental effects of residual porosity and microstructural heterogeneity. Here, we investigate the long-term fatigue behavior of nano-treated LPBF AA2024 (2024NT), with and without hot isostatic pressing (HIP), to clarify how nano-treating and residual porosity jointly govern cyclic durability. The 2024NT alloy exhibited fatigue resistance superior to most reported LPBF aluminum alloys, while HIP further enhance the run-out stress levels up to ∼200 MPa and a fatigue strength approaching ∼50% of yield strength. Defect-tolerance analysis further indicates an effective critical pore size of ∼80–100 μm, substantially larger than that typically reported for conventional LPBF aluminum alloys. This enhanced tolerance arises from two coupled mechanisms: a refined, homogeneous microstructure with dispersed TiC nanoparticles that promotes tortuous, energy-dissipative crack paths, and TiC pseudo-clusters around pores that act as local high-modulus barriers to crack initiation and short-crack growth. These results demonstrate that nano-treating, particularly when combined with porosity mitigation postprocessing, provides an effective pathway to achieve fatigue performance in LPBF wrought aluminum alloys approaching or exceeding wrought benchmarks.
Noise-induced hearing loss often results in a characteristic notch within the 3-6 kHz frequency range, which is crucial for speech intelligibility. However, traditionally fabricated metallic sound-absorbing structures designed for harsh environments frequently suffer from issues such as large structural dimensions, low manufacturing precision, and inadequate programmability and repeatability. In this study, we developed a microscale resonator-similar structure incorporating a Helmholtz resonator, which was fabricated utilizing laser powder bed fusion technique for applications in harsh environments. These structures not only demonstrate a pronounced sound absorption peak around 3022 Hz, achieving a coefficient of 0.92, but also maintain an impressive average sound absorption coefficient of 0.69 within the critical 3-6 kHz range. Compression tests indicate that the specific energy absorption of these resonator-similar structures reaches 45.8 J/g, demonstrating a significant advantage in energy absorption capabilities, providing dual protection against mechanical stress and noise. This research seeks to explore the application of metal AM technologies in the field of sound absorption, contributing to the development of lightweight and effective metallic sound-absorbing structural components suitable for industry, vehicles, and military fields.
Thermal accumulation is a characteristic feature of additive manufacturing (AM), which can lead to layer-dependent thermal histories, microstructural heterogeneity, and property variations along the build direction. To improve the consistency of melt pool depth in wire arc AM (WAAM), an artificial intelligence (AI)-assisted heat input design was proposed and evaluated through numerical simulation and experimental feasibility tests. A moving heat source model was first established to calculate the melt pool depth, and the simulated substrate penetration showed a mean absolute percentage error of 5.65% compared with the experimental measurements. The layer thickness and layer number in the numerical simulations were used as the inputs of the neural network, while the corresponding current was selected as the output. The correlation coefficient between the predicted and target current values was 0.99944. Compared with conventional WAAM under fixed heat input, where the melt pool depth varied from 2.72 to 6.67 mm, the proposed AI-assisted heat input design reduced the melt pool depth range to 1.92-2.72 mm under simulated WAAM conditions. In addition, WAAM deposition experiments using the AI-predicted layer-wise current schedule confirmed the practical implementability and macro-forming feasibility of the proposed strategy.
Composite material extrusion (CME) additive manufacturing technique has great development potential and application advantages in astronautics and aerospace, health care, and industrial design due to its advantages of low cost, high material utilization, and so on. In the green part shaping process, the rheological property parameters of the molten filament are key factors affecting the forming process and quality. However, the corresponding research is still in the beginning stage, and the specific influences and conditions of material formability are still unclear. In this study, three different high-filling-ratio self-made 17-4PH stainless steel powder/polymer composite filaments were studied with material extrusion equipment to analyze their formability conditions. The pressure drop of the molten composite material during the shaping process was then measured by the self-constructed experimental platform, and the results of the relevant rheological properties were analyzed. Subsequently, an analytical rheological model of the molten material was established, and the theoretical analysis on the parameters was performed. Finally, a sensitivity analysis was carried out on the analytical model to investigate the influence of nozzle diameter, building speed, and extrusion temperature on the rheological property of the molten filament material. The results show that the self-made composite filament can be used to fabricate good-quality green samples. The theoretical model was verified to be reliable through the comparison between predictions and measurements (the error range is 7.07-8.1%), and the mechanism of the rheological property was elucidated. The rheological property parameters of the molten material gradually increase with the increasing filling ratio of the metal powder. Within the discussed parameter range (0.4-0.8 mm nozzle diameter, 240-260 degrees C extrusion temperature, and 10-20 mm/s building speed), the nozzle diameter has the most significant effect on the rheological property of the molten material, followed by the building speed, while the effect of extrusion temperature is relatively weak.
In selective laser melting (SLM), the relationships between process parameters and surface quality are highly nonlinear, while different quality indicators are often mutually conflicting, making multi-objective optimization under limited experimental budgets challenging. To address this issue, this study proposes a direct Gaussian process (GP)-based multi-objective Bayesian optimization framework for the efficient optimization of SLM process parameters. Targeted improvements in surrogate modeling and acquisition function design enable effective characterization of coupled multi-objective responses with a limited number of evaluations. Surface roughness ( S a ), defect area ratio, and macroscopic warpage root mean square were selected as optimization objectives based on laser confocal microscopy measurements, while laser power, scanning speed, and hatch spacing were treated as design variables. Comparative studies with the conventional GP-expected hypervolume improvement method and the evolutionary algorithm Non-dominated Sorting Genetic Algorithm II (NSGA-II) demonstrated that the proposed approach achieved faster convergence and superior Pareto front approximation under the same evaluation budget. From an engineering perspective, the proposed framework reduces reliance on extensive experiments and enables the identification of practically feasible parameter combinations, providing an efficient and transferable solution for intelligent SLM process optimization.
Ceramic materials are widely used in machinery, electronics, energy, chemical engineering, aerospace, and biomedical fields owing to their excellent mechanical strength, hardness, electrical insulation, chemical stability, and high-temperature performance. However, the inherent hardness and brittleness of ceramics make machining complex-shaped components challenging. Thus, photocuring techniques enable the fabrication of customized ceramic shapes. In addition, doping and other modification strategies significantly improve the high-temperature performance, oxidation resistance, and structural integrity of ceramics, which meet the demands for next-generation aerospace applications. This article reviews two widely used ceramic shaping methods: stereolithography and digital light processing. It also briefly examines recent advancements in slurry optimization, doping modifications, and process parameter control, focusing on elucidating the mechanisms by which these factors enhance the mechanical properties of ceramics. By summarizing these key research progresses, this review aims to provide a comprehensive reference for the development of photocuring-based ceramic manufacturing technology, and it is anticipated to promote the broader application of high-performance, complex-shaped ceramics in critical fields such as aerospace engineering and biomedicine. Future research directions may further focus on multi-material integrated printing and artificial intelligence-driven process optimization to address remaining challenges in slurry formulation and mechanical property enhancement.
This study investigates the influence of internal infill geometry and density on the mechanical and rheological behavior of 3D-printed silicone structures fabricated using direct ink writing. Test samples with three different infill patterns (linear, triangular, and honeycomb) and four infill densities (55%, 70%, 85%, and 100%) were manufactured and evaluated through rheological creep-recovery analysis, static tensile and compression tests, and cyclic compression loading. The results demonstrate that both geometry and infill degree significantly affect the rheological and mechanical properties, such as static and cyclic compressive tests of the printed structures. Linear infill exhibited the highest compressive strength at 85% density and maintained favorable short-term stability during initial cyclic loading. Triangular patterns displayed significant sensitivity to infill density, with distinct stiffness characteristics, while honeycomb structures offered a balanced trade-off between density and mechanical response. Microscopic observations confirmed that print path quality and structural continuity correlate with the mechanical properties. The findings underscore the importance of tailored infill design in optimizing the functional performance of silicone-based components for applications such as orthotic insoles, soft robotics, and cushioning systems.
Metal additive manufacturing faces challenges in directly fabricating end-use components due to the poor surface quality. Plasma electrolytic polishing (PEP) is an efficient and environmentally friendly surface treatment technology, demonstrating significant potential to enhance the surface quality of additively manufactured metal components. In this study, the surface characteristics of additively manufactured 316L stainless steel polished by PEP, including surface morphology, chemical composition, and wettability, are systematically investigated. First, the influence of key processing parameters, such as polishing time, voltage, electrolyte temperature, and concentration, on surface roughness was comprehensively examined through a series of experiments. The surface roughness was significantly reduced from 10.479 mu m to 2.195 mu m by using an optimized parameter combination: a polishing voltage of 300 V, an electrolyte concentration of 3%, an electrolyte temperature of 75 degrees C, and a polishing time of 30 min. Then, surface characteristics of specimens treated with optimized PEP process parameters were studied and compared with those of as-fabricated specimens. Results showed that defects on the surface of as-fabricated specimens, such as adhered powders and oxides, were effectively removed by PEP. In addition, PEP can significantly improve the wettability of the specimens, with the contact angle decreasing from 80.9 degrees to 39.4 degrees. This study provides a comprehensive analysis of the characteristics of PEP of additively manufactured 316L stainless steel, indicating its potential for post-processing applications.
Advanced thermal management systems in high-Reynolds-number regimes face a fundamental trade-off: enhancing convective heat transfer invariably incurs prohibitive pressure drops. To address this scaling crisis, we introduce a proof-of-concept, bio-inspired design paradigm translating the damage-tolerance principles of the starfish skeletal microlattice into a fluid impedance-matching layer. Fabricated via laser powder bed fusion, a dual-channel heat exchanger featuring a continuous converging-diverging porosity gradient was investigated. Rigorous numerical simulations and conducted physical experiments validate its fundamental performance decoupling. Compared to a uniform baseline at Reynolds number 2000, this bio-inspired structure achieves a 74.7% pressure drop reduction while increasing the Nusselt number by 12%. Crucially, an anti-gradient control group catastrophically failed mechanically and fluidically, proving that precise impedance alignment-not arbitrary aperiodicity-drives this decoupling. The superior performance is governed by an enhanced scaling law ( N u proportional to R e (0.52) ) driven by functional spatial segregation: accelerating core flow to maximize convection while diffusing outlet flow for pressure recovery. Concurrently, the design replicates its biological archetype's progressive collapse, achieving a specific energy absorption of 22.86 J/g. By synergistically optimizing thermo-fluidic and mechanical properties, this work establishes a robust framework for designing high-flux multifunctional metamaterials.
Inconel 939 (IN939) is a crack-sensitive nickel (Ni)-based superalloy when processed by laser powder bed fusion (LPBF), which limits its broader adoption for additively manufactured turbine components. This study comparatively evaluates two distinct alloying strategies-ceramic particle reinforcement (1 wt.% titanium nitride [TiN]) and solid-solution strengthening (1 wt.% tungsten [W])-to clarify their contrasting influence on crack evolution, microstructural development, and mechanical response under identical LPBF processing conditions. All compositions were fabricated using a single set of optimized parameters to isolate compositional effects from processing variations. Optical microscopy revealed that TiN addition increased both crack density and porosity, whereas W addition reduced crack density and improved densification relative to the pristine alloy. Electron backscatter diffraction showed similar columnar grain structures across all conditions with weak global texture, although localized orientation sharpening was observed in the vicinity of crack networks. Differential scanning calorimetry indicated that TiN broadened the melting/solidification range, while W shifted transformation temperatures slightly upward and produced more gradual thermal transitions. Room-temperature tensile testing demonstrated that TiN significantly reduced tensile strength and ductility, whereas W preserved a predominantly ductile response with properties comparable to those of the pristine alloy. These findings highlight the fundamentally different microstructural and mechanical consequences of ceramic versus solid-solution additions in LPBF IN939 and provide guidance on compositional strategies for mitigating crack susceptibility in additively manufactured Ni-based superalloys.
Aiming at the finishing machining difficulties of metal parts with internal cavity walls and apertures in the equipment manufacturing industry, the study proposes a polishing method with In21Sn12Bi49Pb18 low-melting-point alloy abrasive. First, the oil-granting nozzle made of 304 stainless steel powder bed fusion technology is taken as the research object, and four key parameters, namely, pressure of inlet, kinematic viscosity, strength of magnetic, and system temperature, are introduced by combining numerical simulation of computational fluid dynamics and orthogonal experiment. The results of the study show that the optimal combination of processing parameters is the kinematic viscosity of 1.5 Pas, magnetic field strength of 40 A/m, inlet pressure of 4 MPa, and system temperature of 80 degrees C. Under the optimal processing parameters, the surface roughness in the region for the oil-granting nozzle's small hole region was reduced to the level of 0.13 mu m.
Uncontrolled bleeding and wound infections remain critical challenges in clinical trauma management, necessitating advanced dressings that integrate rapid hemostatic, antimicrobial property, and tissue regeneration. In this study, we developed a multifunctional 3D-printed hydrogel scaffold using gelatin methacryloyl (GelMA), collagen, and polyhexamethylene biguanide to address these unmet needs. The GelMA/collagen bioink was optimized for extrusion-based 3D printing through systematic parameter tuning, enabling precise fabrication of porous scaffolds with tailored architectures. The 3D-printed hydrogel dressings were designed to address various wound repair requirements. Experimental results demonstrated their biocompatibility, ability to promote cell proliferation, and antimicrobial efficacy in vitro. Hemostatic performance was evaluated in rat liver and femoral artery bleeding models, whereas a full-thickness wound model in mice assessed their in vivo healing efficacy. The integration of advanced biomaterials with 3D printing technology enables the creation of more efficient and multifunctional wound repair products, providing faster and more effective therapeutic options for patients.
This article presents a novel methodology for the simultaneous optimization of both structural topology and printing path in 3D concrete printing (3DCP), addressing a critical gap between digital design and physical manufacturability. Unlike conventional sequential approaches, our framework is grounded in discrete frame structures, which inherently reflect the filament-based nature of 3DCP, thereby enhancing geometric and mechanical fidelity. The proposed formulation strategically leverages the inherent anisotropy of printed concrete by aligning the printing direction along the longitudinal axis of each frame member to maximize strength and material efficiency. Key manufacturing constraints are integrated directly into the optimization process: member widths are restricted to integer multiples of the nozzle size, and the printing path is enforced as a globally continuous, non-intersecting, and non-overlapping Eulerian circuit through a mixed-integer linear programming model. The efficacy of this simultaneous optimization approach is demonstrated through a series of benchmark problems, which confirm that the resulting designs not only satisfy strict structural displacement and stress constraints with minimal material usage but are also readily manufacturable via direct "one-stroke" printing. This work establishes a foundational integration of structural performance and manufacturability, paving the way for more efficient and reliable 3DCP applications.
The molten pool characteristics play a crucial role in the quality of parts formed through selective laser melting (SLM). Therefore, comprehending the evolution mechanism and studying the features of the molten pool during the SLM process are vital for optimizing the forming process. This study developed a multiphysics finite element model to simulate the laser selective melting of 316L stainless steel powder, considering factors such as phase change, recoil pressure, surface tension, and the Marangoni effect. The simulation results illustrated the temperature evolution, flow field, and surface morphology changes over time during the single-melt channel SLM forming process. The agreement between simulated and experimental surface morphology results was observed. Notably, the Marangoni effect causes fluid in the molten pool to move opposite to the laser scanning direction, leading to ripple formation on the surface and the creation of protrusions and depressions at specific points. Furthermore, the thermal gradient and solidification speed of solid-liquid interface of molten pool were analyzed; it was found that the solidification speed decreased with the increase of the depth of the molten pool and approached to zero at the bottom of the molten pool. Variations in molten pool geometry and length-to-depth ratio under different powers and scanning speeds were also analyzed, revealing that the molten pool geometry is more sensitive to changes in laser parameters at lower powers or scanning speeds.
Inkjet 3D printing has broad application prospects in the field of printed electronics. Surface topography has a significant impact on the electrical performance characteristics of many electronic devices, such as microstrip patch antennas. However, during the printing process, the characteristics of the deposited surface cause changes in the spreading morphology of the droplets. After layer-by-layer stacking, this effect is magnified, causing the surfaces of printed parts to be severely uneven, and ultimately affecting the electrical performance of electronic devices. The present work addresses inkjet 3D printing control and proposes a height evolution model based on material properties. The proposed model involves deriving the spreading diameter of the droplet from the material properties and ejection parameters, based on energy conservation. The quantitative relationship between droplet-spreading contact angle and deposition surface roughness is obtained experimentally, and the shape of the droplet is uniquely determined. In the height evolution model, the shape of the deposited droplet changes dynamically with the number of printed layers. Experimental results show that the height prediction error of the model for printed parts is within 6%, with a corresponding contour prediction error of less than 10 mu m. Based on this model, a closed-loop printing compensation method for inkjet 3D printing is proposed. The method involves adding a surface topography measurement device to a traditional printing system and dynamically adjusting subsequent printing layers. The experimental results show that, compared with traditional open-loop printing, the proposed closed-loop printing compensation method reduced printed part surface roughness by 43.26-68.88%, and peak-to-peak profile by 45.40-49.92%. The effectiveness of this method for improving the performance of electronic devices was verified by printing a microstrip patch antenna and measuring the return loss of the printed sample. Compared to samples produced by open-loop printing, those produced by closed-loop printing were more consistent with the simulation design results, and the center frequency deviation was reduced by 80.95%, demonstrating the effectiveness of the proposed method.
