Search Results (85)

Inspired by the erosion-resistant dorsal armor of the desert scorpion, this study developed biomimetic ZTA ceramic composites with enhanced resistance to solid particle erosion. Three biomimetic configurations, namely convex-bump (CH-O), convex-curved-surface (CH-CS), and convex hybrid rigid–flexible (CH-HS) structures, were fabricated by direct ink writing (DIW) 3D printing. Their erosion performance was evaluated by gas–solid two-phase erosion tests at impact angles ranging from 15° to 90°, and the underlying mechanisms were elucidated through erosion morphology analysis, actual impact angle analysis, and stress-wave propagation analysis. The results showed that the erosion rate of all samples first increased and then decreased with increasing impact angle, reaching a maximum at around 60°. Compared with the smooth control sample, CH-O exhibited lower erosion resistance under low-angle erosion conditions but showed clear improvement under high-angle erosion conditions, with the erosion resistance increased by 18.39–32.54%. CH-CS further improved the erosion resistance of CH-O, with enhancements of 14.31–53.92% at low impact angles and 24.57–35.17% at high impact angles. Among all the biomimetic designs, CH-HS exhibited the best overall erosion resistance, showing an additional improvement of 9.22–32.16% over CH-CS across the tested impact angle range. The superior erosion resistance was attributed to the synergistic effects of convex-bump morphology, curved-surface-induced particle deflection, and rigid–flexible coupling. These biomimetic features modified the actual impact angle of the particles, deflected their trajectories, reduced direct particle impact, and generated a shadow effect, while the flexible layer dissipated impact energy through reflection unloading at the rigid–flexible interface. This study provides a novel strategy for the biomimetic design of erosion-resistant ceramic composites and offers new insights into mitigating erosion damage in ceramic-based mechanical components. Full article

Fused Deposition Modeling (FDM) components require accurate identification of printing parameters to ensure reliable quality assessment and support scalable reverse-engineering workflows. The objective of this study is to evaluate whether mechanical response curves obtained from tensile tests can be used to infer key manufacturing parameters, specifically part orientation, layer thickness, and infill density. Force–displacement and stress–strain data were transformed into image-based representations and classified using several individual and ensemble machine learning models. In addition, the influence of applying a moving-average filter to smooth the curve-derived images was analyzed. Ensemble methods, particularly the AdaBoost classifier, achieved the best performance across the evaluated variables, with the highest accuracy obtained from unfiltered stress–strain images. Under limited-data conditions, ensemble models consistently outperformed individual classifiers, whereas Multilayer Perceptron and Support Vector Machine models exhibited more stable but lower predictive accuracy. These results demonstrate that mechanical response curves contain relevant information about manufacturing conditions and can be used to infer FDM printing parameters. The proposed approach offers a potential non-destructive framework for parameter identification in additively manufactured components, thereby improving traceability and quality control in additive manufacturing processes. Full article

The present study investigates simple cubic lattice structures fabricated through an FDM-based three-dimensional (3D) printing method using wood–polylactic acid (wood–PLA) bio-composite filament and develops a data-driven framework to predict their mechanical response. The design of experiments (DOE) was developed using a response surface methodology (RSM) based on a central composite design (CCD) that was implemented in Design-Expert software (Version 13). During fabrication, four different manufacturing parameters—the layer height, the printing speed, the nozzle temperature, and the infill density—were considered. The compressive strength and compressive modulus were evaluated experimentally, and the corresponding stress–strain responses were examined. The results reveal that the layer height is the most influential parameter, where lower layer heights (0.06–0.1 mm) significantly improve both the compressive strength and the modulus due to enhanced interlayer bonding and reduced void formation. The printing speed and the nozzle temperature also play critical roles, where lower printing speeds (≈40 mm/s) and moderate nozzle temperatures (≈195–205 °C) promote more uniform material deposition and improved interlayer bonding, while higher speeds (≥60 mm/s) and excessive temperatures (≈225 °C) lead to reduced bonding quality and a deterioration in mechanical performance. In contrast, the infill density exhibited a non-monotonic influence, where intermediate levels (around 70%) provided an improved performance under combinations of the low layer height (≈0.1 mm), the low printing speed (≈40 mm/s), and the moderate nozzle temperature (≈195–215 °C), suggesting an interaction-driven effect rather than a purely density-dependent trend. To complement the experimental findings, a machine learning model based on eXtreme Gradient Boosting (XGBoost) was developed using 12,000 data points that were derived from stress–strain curves. The model successfully predicted continuous mechanical responses with errors in the range of 2–8% for unseen specimens, suggesting its capability to capture the relationship between printing parameters and mechanical behavior within the studied design space. Overall, the study highlights that the mechanical properties of wood–PLA lattice structures can be effectively tailored by choosing an appropriate printing parameter control and demonstrates the feasibility of using machine learning to estimate mechanical performance without additional physical testing within the defined parameter domain. Full article

This study investigated how fabrication method (milling versus 3D printing) affects the water sorption and solubility of PMMA dental materials, and how surface characteristics affect hydrolytic stability. Fifty-six PMMA samples were divided into three groups fabricated from CAD/CAM milled discs (Group A: I–III) and four groups from 3D-printed resin (Group B: IV–VII), each subjected to distinct postprocessing protocols. Water sorption (wsp) and solubility (wsl) were measured after immersion in distilled water at 37 °C for 24, 48, and 72 h, and 7 and 14 days. Surface topography and nanoroughness were assessed using atomic force microscopy (AFM). Statistical descriptive analyses were followed by correlation analyses. Milled PMMA demonstrated significantly lower water sorption and negative solubility (mass loss), indicating material dissolution. In contrast, 3D-printed PMMA showed higher water sorption and positive solubility (mass gain), reflecting water incorporation and polymer swelling. The kinetic profiles differed: milled PMMA displayed a monophasic absorption curve, while 3D-printed PMMA exhibited a biphasic pattern with accelerated water uptake after 72 h. AFM analysis revealed that 3D-printed surfaces had significantly greater nanoroughness than milled surfaces. Strong positive correlations were observed between surface roughness parameters (Sa, Sy) and water sorption capacity. The fabrication method was found to influence the hydrolytic stability of PMMA dental materials. Milled PMMA demonstrated superior stability, with lower water uptake, smoother surfaces, and lower leaching solubility. In contrast, 3D-printed PMMA exhibited increased surface roughness and water sorption, attributed to its layered microstructure and nanoporosity. Surface topography emerged as a strong predictor of wsl, related to hydrolytic degradation. For clinical applications, milled PMMA is recommended for long-term use requiring durability, whereas 3D-printed PMMA may be appropriate for short-term applications with optimised postprocessing. Full article

Rheology plays an important role in the 3D concrete printing technology, because it directly governs the flowability and shape retention of the material, impacting both the printing process and the final quality of the obtained structure. Local raw materials such as Portland cement, washed sand, and tap water were used for the preparation of 3D-printed concrete mixtures. The solid-state polycarboxylate ether with an anti-foaming agent was used as superplasticizer. The Portland cement was partially replaced (by volume) with a natural zeolite additive in amounts ranging from 0% to 9% in 3D-printed concrete mixtures. A rotational rheometer with coaxial cylinders was used in this research for the determination of rheological characteristics of prepared 3D-printed concrete mixtures. The Herschel–Buckley model was used to approximate experimental flow curves and assess rheological parameters such as yield stress, plastic viscosity, and shear-thinning/thickening index. The additional experiments and calculations, such as water bleeding test and evaluation of the carbon footprint of 3D-printed concrete mixtures, were performed in this work. The replacement of Portland cement with natural zeolite additive positively influenced rheological and stability-related properties of 3D-printed concrete mixtures. Natural zeolite additive consistently reduced water bleeding, enhanced yield stress under increasing shear rates, and lowered plastic viscosity, thereby improving flowability and mixture transportation during the 3D printing process. As the shear-thinning/thickening index remained stable (indicating non-thixotropic behavior in most cases), higher amounts of natural zeolite additive introduced slight thixotropy (especially under decreased shear rates). These changes contributed to better shape retention, layer stability, and the ability to print taller and narrower structures without collapse, making natural zeolite additive suitable for use in the optimized processes of 3D concrete printing. A significant decrease in total carbon footprint (from 3% to 19%) was observed in 3D-printed concrete mixtures with an increase in the mentioned amounts of natural zeolite additive, compared to the mixture without this additive. Full article

Conformal electronics are distinguished by their unique characteristics, such as the integration of structure and function and their conformability with complex geometries. These features unlock a broad spectrum of applications, including structural health monitoring and the creation of metasurfaces. However, the current landscape of large-scale curved electronic fabrication is characterized by a significant gap in specialized equipment and standardized strategies. In this context, we introduce a pioneering strategy that leverages robotized electrohydrodynamic (EHD) printing for the conformal fabrication of large-scale curved electronics on 3D surfaces. This comprehensive multi-robot EHD conformal printing strategy integrates several critical components, including plasma surface treatment, EHD conformal printing, and near-infrared (NIR) sintering processes. These are supported by enabling technologies such as 3D surface reconstruction and precise hybrid positioning. Notably, our strategy achieves 5 µm printing resolution via EHD lithography and 35 µm repeatable positioning accuracy. After plasma treatment, conductive patterns on FR4 substrates reach 5B-level adhesion strength. NIR sintering enables high-efficiency sintering within only 125 s. Seamless integration of these processes into multi-robot collaborative equipment enables the fabrication of large-area conformal electronics, such as 400 mm × 1000 mm unmanned aerial vehicle wings and 650 mm × 350 mm satellite shells, and supports multi-layer systems including wires, LED arrays, antennas, and sensors. This strategy possesses substantial potential to transcend the limitations inherent in traditional fabrication methods, paving the way for new frontiers in conformal electronics across a variety of applications, including smart wings and satellite surfaces. Full article

Gradient stiffness structures are increasingly recognized for their excellent energy absorption capabilities, particularly under challenging loading conditions. Most studies focus on varying the thickness of the structure in order to produce gradient stiffness. This work introduces an innovative approach to design honeycomb architectures with controlled gradient stiffness along the out-of-plane direction achieved by materials’ microstructure variations. The gradient is achieved by combining three types of thermoplastic polyurethane (TPU) materials: porous TPU, plain TPU, and carbon fiber (CF)-reinforced TPU. By varying the material distribution across the honeycomb layers, a smooth transition in stiffness is formed, improving both mechanical resilience and energy dissipation. To fabricate these structures, a dual-head 3D printer was employed with one head printed processed TPU with a chemical blowing agent to produce porous and plain sections, while the other printed a CF-reinforced TPU. By alternating between the two print heads and modifying the processing temperatures, honeycombs with up to three distinct stiffness zones were produced. Compression testing under out-of-plane loading revealed clear plateau and densification regions in the stress–strain curves. Pure CF-reinforced honeycombs absorbed the most energy at stress levels above ~4.5 MPa, while porous TPU honeycombs were more effective under stress levels below ~1 MPa. Importantly, the gradient stiffness honeycombs achieved a balanced energy absorption profile across a broader range of stress levels, offering enhanced performance and adaptability for applications like protective equipment, packaging, and automotive structures. Full article

This study presents a compact, wideband fractal antenna fabricated using silver nanoparticles (AgNPs) and screen-printing technology. The antenna consists of a decagonal monopole patch and a mesh ground plane, both printed on a transparent polyethylene terephthalate (PET) substrate. The proposed antenna has a compact size of 18 × 16 × 0.55 mm³, achieved by stacking two PET layers joined using double-sided tape. The antenna covers both C- and X-bands, with measured optical transmittance of 68.1% and radiation efficiency of 72%. The simulated −10 dB bandwidth (without bending) spans 4–10.8 GHz and 11.2–12.5 GHz, while the measured −10 dB bandwidth is 3.8–11.2 GHz without bending, 3–11.4 GHz at 30° bending, and 3–11.2 GHz at 45° bending, confirming that there was stable performance under flexure. The conductive patterns were formed using silver nanoparticle paste with a sheet resistance of 0.2 Ω/sq, followed by annealing in a vacuum oven at 140 °C for 20 min. The proposed antenna was tested under 30° and 45° bending, and the measured S₁₁ remained stable, confirming flexibility. The use of a flexible, optically transparent PET substrate enables installation on curved or see-through surfaces. Combining compact size, wideband performance, cost-effective fabrication, and optical transparency, the antenna demonstrates strong potential for application in X-band radar, C-band satellite communications, and S-band Wi-Fi. Full article

The present study investigates the tensile properties of 3D-printed PA12 composites reinforced with short carbon fibers, focusing on the impact of printing parameters on material performance. We employed both experimental testing and machine learning-based predictive modeling to evaluate the influence of layer thickness, extrusion width, and raster angles on failure stress, failure strain, and stress–strain curves. Four machine learning models, including Gaussian process regression (GPR), gradient boosting regression (GBR), random forest (RF), and artificial neural network (ANN), were developed and trained on the experimental data. The results indicated that ANN and GPR models outperformed RF and GBR in predicting mechanical properties, with ANN demonstrating the highest accuracy across all tasks. A SHAP analysis was conducted to interpret the models, revealing that raster angles significantly influence failure stress predictions, while extrusion width predominantly affects failure strain predictions. The ability of the models to predict entire stress–strain curves provides a comprehensive understanding of the material’s mechanical behavior, which is crucial for applications requiring detailed material response data. This study highlights the potential of machine learning models, particularly ANN, in predicting the tensile properties of 3D-printed composites. The findings offer valuable insights for optimizing the 3D printing process to achieve desired material characteristics and pave the way for further research in integrating these predictive tools into additive manufacturing workflows for real-time optimization and quality control. Full article

Three-dimensional bioprinting holds great promise for tissue engineering, but struggles with fabricating complex curved geometries such as vascular networks. Though precise, traditional Cartesian bioprinters are constrained by linear layer-by-layer deposition along fixed axes, resulting in limitations such as the stair-step effect. Multi-axis robotic bioprinting addresses these challenges by allowing dynamic nozzle orientation and motion along curvilinear paths, enabling conformal printing on anatomically relevant surfaces. Although robotic arms offer lower mechanical precision than CNC stages, accuracy can be enhanced through methods such as vision-based toolpath correction. This study presents a modular multi-axis robotic embedded bioprinting platform that integrates a six-degrees-of-freedom robotic arm, a pneumatic extrusion system, and a viscoplastic support bath. A streamlined workflow combines CAD modeling, CAM slicing, robotic simulation, and automated execution for efficient fabrication. Two case studies validate the system’s ability to print freeform surfaces and vascular-inspired tubular constructs with high fidelity. The results highlight the platform’s versatility and potential for complex tissue fabrication and future in situ bioprinting applications. Full article

While many studies on fused filament fabrication (FFF)-printed polymers focus on ultimate tensile strength or failure analysis, the elastic region of the stress–strain curve is frequently overlooked. However, in most engineering applications, components operate well within the elastic range. In mechanical joints, support frames, and other load-bearing structures, stiffness and elastic response are more critical than post-failure behavior, as these properties determine system performance during standard operating conditions before any damage occurs. This study examines the elastic properties of acrylonitrile butadiene styrene (ABS) components fabricated via FFF, with a focus on the impact of printing orientation and nozzle temperature. Tensile tests were performed according to ISO 527-2:1993, and the results were compared to those of milled ABS parts (referred to as FT). Two print orientations were studied: XT, where the layers are oriented perpendicular to the loading direction, and ZT, where the layers are aligned parallel to the loading direction (load-aligned). The study reveals that printing orientation has a significant impact on mechanical behavior. The specimens printed in the ZT orientation exhibited superior elastic modulus and tensile strength compared to the XT specimens and also outperformed the milled FT parts. At 245 °C, the ZT specimens achieved an average tensile strength of 41.0 MPa, substantially higher than the FT’s 31.1 MPa. Moreover, the ZT had approximately 12.6% higher elastic moduli than the FT (1.97 GPa ZT compared to 1.74 GPa FT). Although the FT parts showed higher strain at break, the ZT-printed parts demonstrated a stiffness and strength that suggest their viability as replacements for machined components in load-bearing applications. Full article

This work presents a multiscale, microstructure-aware framework for predicting fatigue strength distributions in additively manufactured (AM) alloys—specifically, laser powder bed fusion (L-PBF) AlSi10Mg and Ti-6Al-4V—by integrating density functional theory (DFT), instrumented indentation, and Bayesian inference. The methodology leverages principles common to all 3D printing (additive manufacturing) processes: layer-wise material deposition, process-induced defect formation (such as porosity and residual stress), and microstructural tailoring through parameter control, which collectively differentiate AM from conventional manufacturing. By linking DFT-derived cohesive energies with indentation-based modulus measurements and a MAP-based statistical model, we quantify the effect of additive-manufactured microstructural heterogeneity on fatigue performance. Quantitative validation demonstrates that the predicted fatigue strength distributions agree with experimental high-cycle and very-high-cycle fatigue (HCF/VHCF) data, with posterior modes and 95 % credible intervals of ${\widehat{\sigma }}_f^{\mathrm{AlSi}10\mathrm{Mg}}={86}_{-7}^{+8}\mathrm{MPa}$ and ${\widehat{\sigma }}_f^{\mathrm{Ti}–6\mathrm{Al}–4\mathrm{V}}={115}_{-9}^{+10}\mathrm{MPa}$, respectively. The resulting Woehler (S–N) curves and Paris crack-growth parameters envelop more than 92 % of the measured coupon data, confirming both accuracy and robustness. Furthermore, global sensitivity analysis reveals that volumetric porosity and residual stress account for over 70 % of the fatigue strength variance, highlighting the central role of process–structure relationships unique to AM. The presented framework thus provides a predictive, physically interpretable, and data-efficient pathway for microstructure-informed fatigue design in additively manufactured metals, and is readily extensible to other AM alloys and process variants. Full article

Optical coherence tomography (OCT) employs light to acquire high-resolution 3D images and is widely applied in fields such as ophthalmology and forensic science. A popular technique for visualizing the top view (en face) is to slice it with flat horizontal plane or apply statistical functions along the depth axis. However, when the target appears as a thin layer, strong reflections from other layers can interfere with the target, rendering the flat-plane approach ineffective. We apply Otsu-based thresholding to extract the object’s foreground, then use least squares (with Tikhonov regularization) to fit a polynomial curve that describes the sample’s structural morphology. The surface is then used to obtain the latent fingerprint image and its residues at different depths from a translucent tape, which cannot be analyzed using conventional en face OCT due to strong reflection from the diffusive surface, achieving FSIM of 0.7020 compared to traditional en face of 0.6445. The method is also compatible with other signal processing techniques, as demonstrated by a thermal-printed label ink thickness measurement confirmed by a microscopic image. Our approach empowers OCT to observe targets embedded in samples with arbitrary postures and morphology, and can be easily adapted to various optical imaging technologies. Full article

Nylon reinforced with short carbon fibres exhibits superior mechanical properties. Its use as a feedstock for fused deposition modelling (FDM) can extend its applications to consumer goods and industrial products. To investigate the flexural and impact properties of the FDM-printed short carbon fibre-reinforced nylon, a central composite face-centred (CCF) design with four factors and three levels and the response surface method (RSM) were employed. The four primary process parameters are the extrusion and bed temperatures, printing speed, and layer thickness. The three investigated responses were the flexural strength, flexural modulus, and impact strength. Perturbation curves and contour plots were used to analyse the influences of the individual and two-way interactions of the response parameters, respectively. Second-order statistical models were constructed to predict and optimise the mechanical properties. The optimal comprehensive mechanical properties were determined using a desirability function combined with the entropy weighting method. The predicted results of best comprehensive mechanical properties are 169.881 MPa for the flexural strength, 9249.11 MPa for the flexural modulus, and 29.659 kJ∙m⁻² for the impact strength, achieved under the parameter combination of extrusion temperature of 318 °C, bed temperature of 90 °C, printing speed of 30 mm∙s⁻¹, and layer thickness of 0.1 mm. A small deviation between the predicted and experimental results indicated the high reliability of the proposed method. The optimal outcomes under the studied parameters showed higher robustness and integrity than previously reported results. Full article

Hybrid machining has considerable potential for industrial applications. The process allows the limitations of additive manufacturing to be reduced and high-precision components to be produced. This article discusses tests determining the impact of 3D printing parameters, machining parameters, and selected milling tools on achieving defined surface roughness values in parts made of PETG (polyethylene terephthalate glycol). Perpendicular-shaped samples were printed by fused deposition modelling (FDM) using variable layer heights of 0.1 mm and 0.2 mm and variable feed rates of 90, 100, 110, and 120 mm/s. Surface roughness values, topography, and Abbott–Firestone curves were determined using a Keyence VR-6000 profilometer. Straight grooves were machined in the test samples using a DMG MORI CMX 600V milling machine with a rotary burr, single-edge spiral burr cutter and spiral endmill. The microstructure was examined using a Motic inverted microscope. The surface roughness parameters of the grooves were investigated. The results confirmed that the use of hybrid machining (with a printed layer height L_h = 0.1 mm, V_feed = 120 mm/s, and a cutter–rotary burr) allows for lower surface roughness parameters, i.e., Ra = 1.54 μm. The relationships developed between printing, cutting, and milling tool parameters can be employed to predict the roughness parameters of filaments with similar characteristics. Full article