Search Results (902)

This exploratory study investigates preliminary trends in the optimization of mechanical properties in 3D-printed components produced via Fused Deposition Modeling (FDM) using polycarbonate (PC) and polylactic acid (PLA). Through a systematic full factorial experimental design, three critical parameters were examined: material types (PC and PLA), layer thickness (0.2 mm and 0.4 mm), and build orientation (horizontal and vertical). Preliminary trends suggest that vertically oriented specimens showed up to 64.7% higher tensile strength compared to horizontal builds, though with significantly reduced ductility. Contributing to growing evidence regarding layer thickness effects, thicker layers (0.4 mm) showed improved ultimate strength by up to 36.2% while simultaneously reducing production time by 50%. However, statistical power analysis revealed insufficient sample size (n = 1 per condition) to establish significance for orientation effects, despite large practical differences observed. PC specimens demonstrated superior strength (maximum 67.5 MPa) and fracture energy, while PLA offered better ductility (up to 22.4% strain). These exploratory findings provide promising directions for future adequately powered investigations for tailored parameter selection according to specific application requirements. Full article

This paper presents a novel mechanically beam-steered antenna system for 10 GHz applications, enabled by multi-material 3D-printing technology. The proposed design eliminates the need for complex electronic circuitry by integrating a mechanically rotatable, 3D-printed hemispherical lens with a conventional rectangular patch antenna. The system comprises three main components: a 10-GHz patch antenna, a precision-fabricated hemispherical dielectric lens produced via stereolithography (SLA), and a structurally robust rotation assembly fabricated using fused deposition modeling (FDM). The mechanical rotation of the lens enables discrete beam-steering from −45° to +45° in 5° steps. Experimental results demonstrate a gain improvement from 6.21 dBi (standalone patch) to 10.47 dBi with the integrated lens, with minimal degradation across steering angles (down to 9.59 dBi). Simulations and measurements show strong agreement, with the complete system achieving 94% accuracy in beam direction. This work confirms the feasibility of integrating additive manufacturing with passive beam-steering structures to deliver a low-cost, scalable, and high-performance alternative to electronically scanned arrays. Moreover, the design is readily adaptable for motorized actuation and closed-loop control via embedded systems, enabling future development of real-time, programmable beam-steering platforms. Full article

Background/Objectives: This study investigates the dosimetric impact of a 3D-printed applicator integrating multilayer catheter geometry and high-density shielding, designed for contact interventional radiotherapy (IRT) in non-melanoma skin cancer (NMSC) treatment. The aim is to assess its potential to enhance target coverage and reduce doses in organs at risk (OARs). Methods: A virtual prototype of a multilayer applicator was designed using 3D modeling software and realized through fused deposition modeling. Dosimetric simulations were performed using both TG-43 and TG-186 formalisms on CT scans of a water-equivalent phantom. A five-catheter array was reconstructed, and lead-cadmium-based alloy shielding of varying thicknesses (3–15 mm) was contoured. CTVs of 5 mm and 8 mm thickness were analyzed along with a neighboring OAR. Dosimetric endpoints included V95%, V100%, V150% (CTV), D_2cc (OAR), and therapeutic window (TW). Results: Compared to TG-43, the TG-186 algorithm yielded lower OAR doses while maintaining comparable CTV coverage. Progressive increase in shielding thickness led to improved V95% and V100% values and a notable reduction in OAR dose, with an optimal trade-off observed between 6 and 9 mm of shielding. The TW remained above 7 mm across all configurations, supporting its use in lesions thicker than conventional guidelines recommend. Conclusions: The integration of multilayer catheter geometry with high-density shielding in a customizable 3D-printed applicator enables enhanced dose modulation and OAR sparing in superficial IRT. This approach represents a step toward personalized brachytherapy, aligning with the broader movement in radiation oncology toward patient-specific solutions, adaptive planning, and precision medicine. Future directions should include prototyping and mechanical testing of the applicator, experimental dosimetric validation in phantoms, and pilot clinical feasibility studies to translate these promising in silico results into clinical practice. Full article

Modern manufacturing is increasingly shaped by the paradigm of Industry 4.0 (Smart Manufacturing). As one of its nine pillars, additive manufacturing plays a crucial role, enabling high-quality final products with improved profitability in minimal time. Advances in this field have facilitated the emergence of diverse technologies—such as Fused Deposition Modelling (FDM), Stereolithography (SLA), and Selective Laser Sintering (SLS)—allowing the use of metallic, polymeric, and composite materials. Within this context, Klipper v.0.12, an open-source firmware for 3D printers, addresses the performance limitations of conventional consumer-grade systems. By offloading computationally intensive tasks to an external single-board computer (e.g., Raspberry Pi), Klipper enhances speed, precision, and flexibility while reducing prototyping time. The purpose of this study is twofold: first, to identify and analyze bottlenecks in low-cost 3D printers and second, to evaluate how these shortcomings can be mitigated through the integration of supplementary hardware and software (Klipper firmware, Raspberry Pi, additional sensors, and the Mainsail interface). The scientific contribution of this study lies in demonstrating that a consumer-grade FDM 3D printer can be significantly upgraded through this integration and systematic calibration, achieving up to a 50% reduction in printing time while maintaining dimensional accuracy and improving surface quality. Full article

Additive manufacturing involves numerous independent parameters, often leading to inconsistent print quality and necessitating costly trial-and-error approaches to optimize input variables. Machine learning offers a solution to this non-linear problem by predicting optimal printing parameters from a minimal set of experiments. Using Fused Deposition Modeling (FDM) as a case study, this work develops a machine learning-powered process to predict porosity defects. Specimens in two geometrical scales were 3D-printed and CT-scanned, yielding raw datasets of grayscale images. A machine learning image classifier was trained on the small-cube dataset (~2200 images) to distinguish exploitable images from defective ones, averaging over 97% accuracy and correctly classifying more than 90% of the large-cube exploitable images. The developed preprocessing scripts extracted porosity features from the exploitable images. A repeatability study analyzed three replicate specimens printed under identical conditions, and quantified the intrinsic process variability, showing an average porosity standard deviation of 0.47% and defining an uncertainty zone for quality control. A multi-layer perceptron (MLP) was independently trained on 1709 data points derived from the small-cube dataset and 3746 data points derived from the large-cube dataset. Its accuracy was 54.4% for the small cube and increased to 77.6% with the large-cube dataset, due to the larger sample size. A rigorous grouped k-fold cross-validation protocol, relying on splitting data per cube, strengthened the ML algorithms against data leakage and overfitting. Finally, a dimensional scalability study further assessed the use of the pipeline for the large-cube dataset and established the impact of geometrical scaling on defect formation and prediction in 3D-printed parts. Full article

This study addresses critical bottlenecks in photocatalytic water treatment technologies, including difficulties in recovering traditional powdered catalysts, low mass transfer efficiency in immobilized reactors, and limited structural diversity. By integrating topology optimization with 3D printing technology, we designed and fabricated five types of triply periodic minimal surface photocatalytic reactors (TPMS-PCRs) with hierarchical porous structures—Fischer-Radin-Dunn (FRD), Neovius (N), Diamond (D), I-graph Wrapped Package (IWP) and Gyroid (G). Using fused deposition modeling (FDM), these TPMS configurations were manufactured from polylactic acid (PLA), 1.5 wt% TiO₂/PLA, and 2.5 wt% TiO₂/PLA. The catalytic degradation performance of these structurally distinct reactors for organic pollutants varied significantly. Notably, the FRD-type TPMS-PCR loaded with 2.5 wt% TiO₂ achieved a methylene blue (MB) degradation rate of 93.4% within 2.5 h under rotational flow conditions, compared to 87.5% under horizontal flow conditions. Full article

Objective: To investigate the compressive, dimensional, and antimicrobial properties of thermoplastic Acrylonitrile Butadiene Styrene (ABS) 3D printed hollow blocks following chemical disinfection. Methods: Forty-two 3D printed ABS cubes were immersed in tap water, 0.12% chlorhexidine gluconate, 3% hydrogen peroxide, 5% sodium bicarbonate, 0.5% sodium hypochlorite, and commercial Potassium Caroate (Polident) for 28 days (4 cycles). Linear Outer (OM) and Inner (IM) dimensions, Root Mean Square (RMS), and mass were recorded before and after each immersion cycle. An additional set of seven cubes was untreated and served as a control. Fourier transform infrared spectroscopy (FTIR) was executed on one randomly selected sample from each group before and after immersion. Following the completion of the immersion cycles, an evaluation of compressive strength was performed using a universal testing machine. Subsequently, from each group, a single ABS cube was randomly selected for the introduction of Streptococcus mutans and Candida albicans, followed by a 14-day incubation period with Scanning Electron Microscope (SEM) evaluation. Results: There were no significant differences (p > 0.05) between OM, IM, and compressive strength measurements (F = 1.036, p = 0.443) across all groups. RMS values increased for OM and decreased for IM. Notably, cubes that underwent immersion in a 0.12% chlorhexidine gluconate solution displayed considerable changes in mass (p < 0.05), exhibiting a low positive correlation (ρ = 0.339). The 0.12% chlorhexidine gluconate group exhibited the emergence of a new OH peak (3000–3500) in FTIR, whereas the 3% hydrogen peroxide group experienced the disappearance of the styrene peak (1300–1500). Exposure of ABS to C. albicans and S. mutans demonstrated clear surfaces under SEM with 0.12% chlorhexidine gluconate, 0.5% sodium hypochlorite, and Polident. Conclusions: Disinfection with 0.5% sodium hypochlorite and Potassium Caroate produced minimal mechanical changes and resisted growth of C. albicans and S. mutans. 0.12% chlorhexidine gluconate and 3% hydrogen peroxide altered dimensional and chemical compositions of 3D printed ABS following disinfection. Full article

Optimizing printing parameters is crucial for enhancing the efficiency, surface quality, and dimensional accuracy of Fused Deposition Modeling (FDM) processes. A review of numerous publications reveals that most scholars analyze factors such as nozzle diameter and printing speed, while few investigate the impact of layer thickness, infill density, and shell layer count on print quality. Therefore, this study employed 3D slicing software to process the three-dimensional model and design printing process parameters. It systematically investigated the effects of layer thickness, infill density, and number of shells on printing time and geometric accuracy, quantifying the evaluation through volumetric error. Using an ABS connecting rod model, optimal parameters were determined within the defined range through orthogonal experimental design and signal-to-noise ratio (S/N) analysis. Subsequently, a backpropagation (BP) neural network was constructed to establish a predictive model for process optimization. Results indicate that parameter selection significantly impacts print duration and surface quality. Validation confirmed that the combination of 0.1 mm layer thickness, 40% infill density, and 5-layer shell configuration achieves the highest dimensional accuracy (minimum volumetric error and S/N value). Under this configuration, the volumetric error rate was 3.062%, with an S/N value of −9.719. Compared to other parameter combinations, this setup significantly reduced volumetric error, enhanced surface texture, and improved overall print precision. Statistical analysis indicates that the BP neural network model achieves a Mean Absolute Percentage Error (MAPE) of no more than 5.41% for volume error rate prediction and a MAPE of 5.58% for signal-to-noise ratio prediction. This validates the model’s high-precision predictive capability, with the established prediction model providing effective data support for FDM parameter optimization. Full article

This study explores the valorization of tomato stem waste by converting it into biochar through slow pyrolysis and incorporating it into poly(lactic acid) (PLA) composites for fused filament fabrication (FFF) 3D printing. The objective was to improve the valorization and added value of tomato stem waste. Biochar derived from tomato stems was characterized for its physicochemical properties, revealing a high surface area and small particle size. PLA-based composite filaments with 5% and 7.5% biochar were manufactured via melt extrusion. The effects of biochar concentration and printing infill patterns (concentric and rectilinear) on the mechanical and thermomechanical properties of the 3D-printed composites were investigated. Results indicated that biochar slightly increased the glass transition temperature of PLA and improved the flexural properties. Dynamic mechanical analysis (DMA) showed that the storage modulus was enhanced in the glassy region for composites with 5% biochar, suggesting improved stiffness. This research demonstrates the potential of using tomato stem-derived biochar as a sustainable filler in PLA composites, contributing to the circular economy and reducing environmental impact. Full article

Considering that soft airbag sensors made from soft materials are limited to detecting only normal forces, a novel PEBA–silicone composite magneto-sensitive airbag sensor is proposed for simultaneously detecting normal contact force and horizontal motion during human–robot interaction. In terms of structural design, the PEBA–silicone composite airbag is manufactured using fused deposition modeling, 3D printing, and silicone casting, achieving a balance between high airtightness and adjustable stiffness. Beneath the airbag, a magneto-sensitive substrate with several NdFeB magnets is embedded, while a fixed Hall sensor detects spatially varying magnetic fields to determine horizontal displacements without contact. The results of contact-force and motion experiments show that the proposed sensor achieves a force resolution of 20 g, a force range of 0 to 1100 g, a fitting sensitivity of 7.54 N/Pa, an average static stiffness of 4.82 N/mm, and a horizontal motion detection range of 0.125 to 1 cm/s. In addition, the prototype of the sensor is lightweight (with the complete assembly weighing 81.25 g and the sensing part weighing 56.13 g) and low-cost, giving it potential application value in exoskeletons and industrial grippers. Full article

Additive manufacturing using fused deposition modelling (FDM) is increasingly explored for personalised drug delivery, but the lack of suitable biodegradable and printable filaments limits its pharmaceutical application. In this study, we investigated the influence of formulation and structural design on the performance of polyvinyl alcohol (PVA)-based filaments doped with theophylline anhydrous for 3D printing. To address the intrinsic brittleness and poor printability of PVA, cassava pulp-derived fibres—a sustainable and underutilised agricultural by-product—were incorporated together with polyethylene glycol (PEG 400), Eudragit^® NE 30 D, and calcium stearate. The addition of fibres modified the mechanical properties of PVA filaments through hydrogen bonding, improving flexibility but increasing surface roughness. This drawback was mitigated by Eudragit^® NE 30 D, which enhanced surface smoothness and drug distribution uniformity. The optimised composite formulation (P₁₀F₅E₅T₅) was successfully extruded and used to fabricate 3D-printed constructs. Release studies demonstrated that drug release could be modulated by pore geometry and construct thickness: wider pores enabled rapid Fickian diffusion, while narrower pores and thicker constructs shifted release kinetics toward anomalous transport governed by polymer swelling. These findings demonstrate, for the first time, the potential of cassava fibre as a functional additive in pharmaceutical FDM and provide a rational formulation–structure–performance framework for developing sustainable, geometry-tuneable drug delivery systems. Full article

The research investigated the potential of five novel additively manufactured (AM) fiber-reinforced thermoplastic composite (FRTPC) configurations as alternatives for ablative thermal protection system (TPS) applications. The thermal stability and ablative behavior of ten samples developed via fused deposition modeling (FDM) three-dimensional (3D) printing out of fire-retardant thermoplastics were investigated using an in-house oxyacetylene torch bench. All samples featured an innovative internal thermal management architecture with three air chambers. Furthermore, the enhancement of thermal benefits was achieved through several approaches: ceramic coating, mechanical hybridization, or continuous fiber reinforcement. For each configuration, two samples were exposed to flame at 1450 ± 50 °C for 30 s and 60 s, respectively, with the front surface subjected to direct exposure at a distance of 100 mm during the ablation tests. Internal temperatures recorded at two back-side contact points remained below 50 °C, well under the 180 °C maximum allowable back-face temperature for TPS during testing. Continuous reinforced configurations 4 and 5 displayed higher thermal stability the lowest values in terms of thickness, mass loss, and recession rates. Both configurations showed half of the weight losses measured for the other tested configurations, ranging from approximately 5% (30 s) to 10–12% (60 s), confirming the trend observed in the thickness loss measurements. However, continuous glass-reinforced configuration 5 exhibited the lowest weight loss values for both exposure durations, benefiting from its non-combustible nature, low thermal conductivity, and high abrasion resistance intrinsic characteristics. In particular, the Al₂O₃ surface coated configuration 1 showed a mass loss comparable to reinforced configurations, indicating that an enhanced surface coat adhesion could provide a potential benefit. A key outcome of the study was the synergistic effect of the novel air chamber architecture, which reduces thermal conductivity by forming small internal air pockets, combined with the continuous front-wall fiber reinforcement functioning as a thermal and abrasion barrier. This remains a central focus for future research and optimization. Full article

Three-dimensional (3D) printing based on polymers reinforced with lignocellulosic components is an accessible and sustainable technology. Cellulose-based byproducts from industry, as well as crops, food, and forestry wastes, represent potential resources for additive manufacturing and have been evaluated in recent years, primarily in combination with polymers such as PLA or ABS. During fused deposition modeling (FDM), several parameters must be considered during raw material conditioning, blending, extrusion, and 3D printing. It is essential to understand how these parameters influence the final properties and their impact on the final application. This review focuses on the latest studies of lignocellulosic byproducts for 3D printing filaments and how the parameters involved during filament production and 3D printing influence the properties of the final product. Recent studies concerning applications, technical issues, and environmental and regulatory aspects were also analyzed. Full article

This study examines the influence of printing parameters and filament composition on the mechanical properties of 3D printed parts, building upon prior research in fused deposition modeling. Two combinations of printing parameters, 75% infill, 0° orientation, four outer shells, with either gyroid and 3D Honeycomb infill patterns—were analyzed across eleven materials, including acrylonitrile butadiene styrene, polylactic acid, polylactic acid-based composites, polyethylene terephthalate glycol, and high-impact polystyrene. Tensile, compression, and bending tests were performed on the printed specimens to determine stiffness and elastic modulus. Each material demonstrated different levels of variability and sensitivity to printing parameters under the various loading conditions, emphasizing that no single configuration is optimal across all scenarios. For example, the gyroid pattern led to increases up to ~35% in bending modules for common thermoplastic filaments and ~30% for stone-filled polymers, while in tensile stiffness, variations between infill patterns remained below 5% for other conventional polymers. These findings underline the load-specific nature of optimal parameter combinations and the influence of material-specific characteristics, such as filler content or microstructural homogeneity. This study provides quantitative insights that can support application-driven parameter selection in additive manufacturing, offering a comparative dataset across widely used and emerging filaments. Full article

The aim of this study is to obtain poly(lactic acid) polymeric nanocomposites and carbon nanotubes for application in drone propellers produced through 3D printing. In this work, a filament based on poly(lactic acid)—PLA/functionalized carbon nanotube (CNT) composites was prepared for the fused deposition modeling (FDM) process. The effects of CNT content (0.2–1.0%), temperature variation, and extruder screw rotation variation were applied in the Design of Experiments (DOE) tool, where the main factors contributing to filament quality, focusing on mechanical strength, were identified. Through this tool, an optimum point for the material’s mechanical strength was reached, showing a value of 48.87 MPa, 43.17% above the initial value of 27.77 MPa. The response surface curve revealed a region where new filaments with similar mechanical strength values to those found in this work could be obtained. The results demonstrate that CNT content, extruder screw rotation, and extruder temperature directly influence filament quality. The data obtained from Thermogravimetry (TG) and Derivative Thermogravimetry (DTG) curves show that the addition of 0.6% CNT by weight does not significantly modify PLA degradation resistance, despite slight differences in temperatures. The main reason for these alterations is the dispersion of CNTs in the PLA matrix and CNT agglomeration. Through the demonstrated simulation, it is possible to confirm the application of the developed material in drone propeller manufacturing, facilitating access and providing new opportunities for users. Full article