Search Results (1,559)

Material extrusion (MEX), commonly known as fused deposition modeling (FDM), has become a widely adopted additive manufacturing (AM) technology owing to its low equipment cost and broad polymer compatibility. However, the geometric fidelity of the printed line often suffers from defects that compromise overall part quality. Specifically, residual edge non-uniformity degrades surface finish, while uncontrolled line width variability causes undesired gaps or overlaps that undermine mechanical performance. Therefore, ensuring an accurate line width and low edge non-uniformity is essential for advancing material extrusion toward high-precision industrial applications. In this study, a machine learning framework is proposed for the rapid prediction and analysis of printed line characteristics. Nozzle temperature, print speed, and material flow rate were considered as input process parameters. Mean line width and edge non-uniformity were taken as the target responses. Four representative machine learning algorithms (XGBoost, BPNN, GPR, and SVR) were adopted for model development. To enhance predictive accuracy, these models were optimized using Particle Swarm Optimization for automatic hyperparameter tuning. Subsequently, comparative evaluations identified GPR as the optimal predictive model. Furthermore, a SHAP-based interpretability analysis was conducted, revealing that nozzle temperature dominates line width, while the flow rate governs edge non-uniformity. Consequently, this interpretable and computationally efficient surrogate modeling approach provides a robust foundation for future closed-loop quality control and inverse process design. Full article

This study reports the filament-making stage of a sequential single-screw process-development pathway for compatibilizer- and plasticizer-free soy hull fiber (SHF)/PLA biocomposites used in fused deposition modeling. Thirty-three filament-making trials were interpreted through an event-linked process chain, and 14 trials were evaluated using phase-resolved in-line diameter records and capability-style Cp/Cpk metrics. Filament-making converged on a single-mixing-zone screw, a 3.85 mm orifice/5.75 mm land die, 10 rev/min, and a 160/170/180/195 °C barrel profile for 10–30 wt.% SHF, whereas neat PLA required 180/185/200/205 °C. The strongest sustained benchmark was a 10SHF filament produced under converged settings, with a mean diameter of 1.7411 mm, a standard deviation of 0.0236 mm, 95.45% of readings within 1.70–1.80 mm, and only 0.013% above 1.89 mm. Feed replenishment, depletion, irregular pellets, recycled material, and fines-rich feed shifted the same nominal configuration among controlled and unstable states. The highest reliably spool-fed formulation was 30 wt.% SHF. The 35SHF filament remained nozzle-depositable from loose coils but fractured repeatedly during take-up and direct spool unwinding in 3D printing. Operational validation of all four converged filament formulations comprised 720 printed mechanical-test specimens over approximately 936 h. The reported conditions define platform-specific operating windows, but the process insights hold global relevance for pellet-based extrusion systems. Full article

This study examines the influence of the dynamic pressure control parameter in the nozzle during melt deposition onto the build platform (k-Factor), also known as Linear Advance or Pressure Advance in different firmware implementations, on the quality of parts produced using the Fused Deposition Modeling (FDM) technology. The investigation was conducted based on printed test specimens made from Ingeo Biopolymer 4043D, with k-Factor values varied in the range of 0.01 to 0.20 under comparable process conditions. Dimensional measurements were performed along the X and Y axes, and visual analysis was carried out to identify defects in the specimens. The results from statistical analysis and visual inspection reveal a clear correlation between the k-Factor value and part quality. An optimal balance between dimensional and geometric stability was achieved at k-Factor = 0.04, identifying it as a suitable value for the material and process conditions used. Full article

Functionally graded multi-material architected structures fabricated by fused deposition modeling (FDM) were investigated to evaluate their multifunctional mechanical and dynamic performance. Sixteen honeycomb configurations incorporating poly(lactic acid) (PLA), thermoplastic polyurethane (TPU), and wood-filled PLA (WWF-PLA) were designed by systematically varying material distribution, cellular geometry, and structural density as integrated architected configurations. Compression, flexural, dynamic mechanical, free-vibration, density reduction, and water absorption tests were conducted, and the experimental responses were objectively evaluated using the CRITIC–WASPAS multi-criteria decision-making framework. Among the investigated configurations, A16 exhibited the highest overall performance, achieving 41.8 MPa compressive strength, 56.4 MPa flexural strength, 1425 MPa storage modulus, 0.162 loss factor (tan δ), 3.7% damping ratio, and 39% density reduction. Compared with the baseline configuration (A1), A16 demonstrated improvements of 14.5%, 17.0%, 20.8%, 44.6%, 76.2%, and 77.3% in the respective performance metrics. The proposed framework provides an objective approach for ranking integrated architected designs for lightweight multifunctional engineering applications. Full article

Background/Objectives: Distal radius and carpal fractures are common injuries in emergency radiology, where accurate lateral wrist positioning is essential for diagnostic image quality. Maintaining a true lateral position can be challenging, often requiring manual support and increasing occupational radiation exposure. This pilot phantom study aimed to design, fabricate, and evaluate a novel 3D-printed wrist immobilization device and compare its performance with adhesive tape and sandbag stabilization. Methods: A wrist immobilization device was designed using computer-aided design software and fabricated by fused deposition modeling with polylactic acid. A wrist phantom was imaged using three stabilization techniques: the 3D-printed device, adhesive tape, and sandbag support. Image quality was independently assessed by two blinded observers using an eight-point scoring system. Inter-rater reliability was evaluated using the intraclass correlation coefficient (ICC). Positioning time was recorded, and radiopacity was assessed using pixel intensity measurements within predefined regions of interest. Results: No significant differences in image quality were observed among the three stabilization techniques (p = 0.263). Inter-rater reliability was excellent (ICC = 0.877). Positioning time differed significantly among techniques (p = 0.036), with sandbag stabilization requiring the shortest time. Radiopacity analysis demonstrated minimal attenuation (8.6–20.8%) in regions adjacent to the anatomical area of interest, while higher attenuation was limited to non-diagnostic regions. Conclusions: The 3D-printed wrist immobilization device produced image quality comparable to conventional stabilization methods with minimal radiographic interference. These findings support its feasibility as a low-cost positioning aid and warrant further evaluation in larger clinical studies. Full article

This study statistically examines how fused deposition modeling (FDM) parameters influence the mechanical behavior of FDM-printed lattice structures. Diamond triply periodic minimal surface (D-TPMS) lattice structures were 3D-printed using carbon fiber-reinforced polylactic acid (PLA/CFs) composites. The effects of FDM parameters, including extruder temperature (ET), printing speed (PS), and layer thickness (LT), on the mechanical behavior of D-TPMS structures were investigated using response surface methodology (RSM). Uniaxial compression testing was performed to evaluate the mechanical properties of the 3D-printed samples, including compressive modulus (E), peak strength (σ_peak), and specific energy absorption (SEA). The optimal FDM parameter settings for maximizing E, σ_peak, and SEA were determined using multi-objective optimization via the desirability function. A deformation analysis was further conducted. The as-built D-TPMS samples generally matched the design relative density (44%), with absolute errors of 0.3–4.5%, while the largest deviation (~4.5% below the design value) occurred at low-ET and high-LT combinations. The results showed that LT was the dominant factor affecting E and σ_peak, accounting for 77.45% and 89.25% of the total variation, respectively, whereas ET had the most significant influence on SEA, accounting for 55.76% of its total variation. In addition, increasing ET improved interfacial bonding and shifted the failure mode from early wall and layer fracturing to predominantly wall yielding, thereby enhancing structural integrity during compression. Higher LT deteriorated the mechanical properties (E, σ_peak, and SEA) and promoted a progressive failure mode characterized by gradual interlayer separation. The findings revealed that the optimal settings (60 mm/s PS, 232 °C ET, and 0.2 mm LT) simultaneously maximized E (0.567 GPa), σ_peak (15.937 MPa), and SEA (15.510 J/g), with high predictive accuracy (maximum % error ~±1.41%). Correlation analysis further revealed significant relationships between as-built relative density and the compression responses E, σ_peak and SEA, with correlation coefficients exceeding 0.8. Overall, this study advances the understanding of how FDM printing parameters govern the mechanical behavior of PLA/CFs D-TPMS lattice structures and highlights the potential for predicting their mechanical performance. Full article

Fused deposition modelling (FDM) is increasingly used to produce high-performance polymer components; however, the mechanical performance of printed parts is often limited by weak interlayer adhesion, void formation, and residual thermal stresses. In this study, the effects of nozzle temperature, bed temperature, and post-treatment annealing temperature on the tensile and flexural strength of FDM-printed polyether ether ketone (PEEK) were investigated and optimized using Response Surface Methodology (RSM). A face-centred central composite design was employed to evaluate the individual, quadratic, and interaction effects of the three thermal parameters. The results showed that post-treatment annealing temperature was the most influential factor, contributing 56.48% to tensile strength and 52.73% to flexural strength, followed by nozzle temperature, which contributed 30.56% and 30.15%, respectively. Bed temperature showed a comparatively smaller individual effect; however, its interaction with nozzle temperature significantly influenced both tensile and flexural strength. The confirmation experiment performed at 200 °C post-treatment temperature, 414 °C nozzle temperature, and 142 °C bed temperature produced a tensile strength of 55.65 MPa and a flexural strength of 81.08 MPa, with prediction errors of 5.63% and 4.08%, respectively. SEM fracture analysis provided qualitative evidence that improved thermal processing reduced interlayer separation and visible void-related defects while promoting a more cohesive fracture morphology. These improvements are attributed to enhanced interlayer fusion, possible polymer-chain diffusion across layer boundaries, and thermal-stress relaxation during annealing. The findings demonstrate that thermal-parameter optimization and post-treatment annealing can improve the mechanical performance of FDM-printed PEEK within the investigated processing window. Full article

Fused deposition modeling (FDM) is a material-extrusion additive manufacturing technology that is widely used in rapid prototyping, complex product modeling, and functional part fabrication. However, process-parameter drift and environmental disturbances may induce underfilling, overfilling, warping, delamination, and other defects, thereby reducing part quality or interrupting the manufacturing process. Since FDM is characterized by point-wise extrusion and layer-by-layer deposition, layer-surface images naturally contain both spatial morphology and temporal evolution information. Existing image-based diagnostic methods often treat layer images as independent samples, and the selection of the image-stream length is still insufficiently supported by experimental evidence. Moreover, spatial compression in spatiotemporal neural networks may remove local defect information that is important for distinguishing similar process-parameter drifts. This study provides a deployment-oriented analysis of FDM image-stream diagnosis by systematically examining how layer-window length, spatial feature preservation, and strict data partitioning influence process-parameter drift recognition. To address these issues, this paper studies ConvLSTM-based FDM image-stream process-parameter drift diagnosis. Continuous region-of-interest image streams are constructed for one nominal condition and six process-parameter drift conditions. In this paper, the time step T denotes the number of consecutive layer-surface images, or, equivalently, the number of consecutive printed layers, contained in one diagnostic image stream. A ConvLSTM-Flatten baseline is first developed to preserve complete spatial feature maps and to evaluate the effect of different time-step lengths. Then, a ConvLSTM model with adaptive spatial pooling and temporal attention (ASP-TA) is constructed to analyze the influence of spatial pooling granularity and temporal feature fusion. The experiments show that the ConvLSTM-Flatten model achieves the highest average test accuracy of 0.7288 at $T=9$, whereas $T=3$ is identified as a practical optimal time step when test accuracy, image-frame computation, diagnosis latency, and convergence behavior are considered together. The paired trial-wise accuracy difference between $T=9$ and $T=3$ is small and not statistically significant over ten repeated trials. Thus, the diagnostic window corresponding to $T=3$ covers three consecutive deposited layers; after the initial window is available, stride-one stream construction allows the diagnosis to be updated with each newly acquired layer image. ASP-TA with a pooling size of eight consistently outperforms ASP-TA with a pooling size of four, but both are lower than the Flatten baseline, indicating that preserving sufficient spatial information is essential for distinguishing FDM process-parameter drift states. The results reveal the non-monotonic influence of time-step length and clarify the tradeoff between spatial feature preservation and model compactness in FDM image-stream process-parameter drift diagnosis. Full article

In this paper, a machine learning model to predict the Young’s modulus of polylactic acid specimens manufactured by Fused Deposition Modeling is proposed, based on a stacked ensemble architecture. The model uses as input parameters the fill degree, printing speed, filling pattern, yield strength, and tensile strength, along with additional features obtained through feature engineering. The proposed approach integrates nine base models with a linear meta-model, allowing it to capture both linear and nonlinear relationships between the variables. The results obtained on the test dataset show strong predictive performance, with a Mean Squared Error with a value of 7.31 together with a Coefficient of Determination R² with a value of 0.99, which is noticeably better than the performance of the individual models. To validate the model, a separate group of specimens was tested, and the difference between the measured and predicted Young’s modulus was about 1% on average. The model was also implemented in a desktop application with a graphical interface, in which the calculation can be run directly, thus allowing a rapid estimation of Young’s modulus. In this way, the need for laborious experimental testing is reduced with the help of AI-based approaches in additive manufacturing. Full article

This work investigates the potential of additive manufacturing (AM) technologies for prototyping and developing functional components in thermal systems, with particular emphasis on thermal and mechanical performance. The study focuses on two complementary prototyping strategies: (i) the use of metal-filled polymer filaments in Fused Deposition Modeling (FDM), also known as Material Extrusion (MEX) according to ISO/ASTM 52900:2022, and (ii) a hybrid approach combining polymer 3D printing with conductive coating and electrochemical copper deposition. While metal-filled filaments provide a rapid and low-cost solution for early-stage prototyping, their mechanical properties remain similar to those of the polymer matrix, limiting their applicability in load-bearing structures. In contrast, the hybrid method enables the fabrication of hollow metallic geometries with improved thermal and electrical conductivity. This approach is more time-consuming and process-intensive and is therefore considered a subsequent stage in the prototyping workflow following initial MEX-based design iterations. Compared with conventional polymer-based MEX, several AM approaches enable the development and fabrication of fully metallic or metal-functional structures, including Powder Bed Fusion (PBF), Directed Energy Deposition (DED), and hybrid polymer–metal methods based on electroplating. Furthermore, understanding mechanical properties such as tensile strength is essential for assessing the applicability of AM materials in energy system components. The results contribute to bridging the gap between rapid prototyping and the implementation of advanced AM technologies in thermal-related applications. Full article

Fused Deposition Modeling (FDM) has emerged as one of the most widely adopted additive manufacturing (AM) technologies, valued for its simplicity, cost-effectiveness, and versatility in fabricating complex geometries. The geometry of the extrusion nozzle plays a critical role in determining melt flow behavior, extrusion stability, and final print quality of thermoplastic materials. When utilizing natural fiber-reinforced thermoplastic composites (NFRCs), understanding and optimizing nozzle geometry becomes increasingly important due to the complex rheological behavior of fiber-filled melts, including challenges such as increased viscosity, shear-thinning effects, and susceptibility to nozzle clogging. The reviewed literature shows that optimized nozzle geometry, supported by computational and statistical tools, can improve the printability and mechanical performance of natural fiber composites, although further advancements are needed to address material variability and complex fiber–matrix interactions. This review paper presents a comprehensive overview of the effects of nozzle geometry on melt flow behavior in FDM, covering computational modeling approaches, experimental characterization studies, and optimization methodologies for enhancing the performance of natural fiber-reinforced composites in additive manufacturing applications. The integration of sustainable materials into FDM processes represents a significant advancement toward environmentally responsible manufacturing while maintaining mechanical performance requirements. Full article

To address the increasing demand for energy-efficient buildings, this study experimentally characterizes the effective (λ_eff) and apparent (λ_app) thermal conductivity of 3D-printed polymer plates. While 3D printing offers significant design flexibility, a lack of comprehensive comparative data between printable polymers and conventional building materials limits their integration into large-scale facade systems. This research investigates four distinct materials: standard polylactic acid (PLA Basic), foamable poly-L-lactic acid (PLA Aero), amorphous polyethylene terephthalate glycol (PETG), and carbon fiber-reinforced polyethylene terephthalate (PET-CF). Utilizing the guarded hot plate (GHP) method (ASTM C177, EN 12667, EN 12939), steady-state heat flux and temperature gradients were measured. The methodology incorporates a rigorous uncertainty analysis (k = 2) addressing the inherent inhomogeneity of additively manufactured components. Results demonstrate significant variations: PLA Aero achieved a 57.3% reduction in thermal conductivity (0.114 ± 0.005 W/(m·K)) compared to PLA Basic (0.267 ± 0.011 W/(m·K)), while PET-CF showed increased conductivity (0.533 ± 0.021 W/(m·K)) due to carbon fiber bridging. Notably, multi-layered PLA Aero assemblies outperformed conventional double-glazed units, reaching a minimum λ_app of 0.051 W/(m·K). These findings validate the GHP method for 3D-printed polymers and provide a technical foundation for material selection in next-generation, energy-efficient smart facades. Full article

The integration of additive manufacturing technology and biomimetic design provides new possibilities for functional and aesthetic innovation in automotive interiors. This study explores a roof handrail design method based on a spider web biomimetic structure from the perspectives of object character and design freedom. By transforming the spider web morphology of nature into a manufacturable parametric model, the organic unity of structural performance and visual aesthetics has been achieved. The simulation results show that the spider web biomimetic structure handrail distributed along the z-axis not only meets the mechanical performance (maximum stress of 189.11 MPa under 1500 N load) but also theoretically reduces weight by 32.03% compared to traditional designs. Material testing shows that the spider web biomimetic structure handrail made of PA6-CF material through fused deposition molding not only meets safety requirements but also has a better user experience. This study achieved organic forms that are difficult to process with traditional techniques through 3D printing technology, providing a new paradigm of “form following ecology” for automotive interior design and expanding the possibilities of functional components in user experience and spatial narrative. Full article

Background/Objectives: A major advantage of 3D printing technology is the ability to modify drug release by adjusting formulation composition and printing parameters. The aim of this study was to develop and characterize 3D-printed tablets containing prednisolone acetate and to investigate the effects of formulation composition and printing parameters, namely infill density and pattern, on the drug release profile. Methods: Filaments composed of polyvinyl alcohol, sorbitol, and prednisolone acetate, with sodium alginate incorporated in selected formulations, were prepared using hot melt extrusion. The obtained filaments were characterized and used for the fabrication of tablets via fused deposition modeling. The resulting tablets were evaluated in terms of mass variation, dimensions, hardness, content uniformity and drug release rate. Results: The extrusion of polyvinyl alcohol and prednisolone acetate in the absence of additional excipients resulted in a defective filament, highlighting the need for sorbitol incorporation. In contrast, all other filament formulations (F2–F4) exhibited a uniform structure and homogeneous drug distribution. The 3D-printed tablets complied with pharmacopeial requirements for mass variation and content uniformity and demonstrated good precision and reproducibility in terms of dimensions and hardness. Lower infill density was associated with faster drug release, while the presence of sodium alginate resulted in slower release, particularly at higher infill percentages and with a gyroid infill pattern. Furthermore, formulations with higher sorbitol content demonstrated an increased release rate of prednisolone acetate. Conclusions: Infill density was identified as the dominant factor affecting release kinetics. Among the tested formulations, A100G (gyroid structure with 100% infill density), containing prednisolone acetate, polyvinyl alcohol, sorbitol, and sodium alginate, proved most suitable for achieving sustained drug release. Full article

Traumatic Brain Injuries (TBIs) frequently require cranioplasty procedures to restore skull integrity and protect underlying brain. Conventional cranial implants are often limited by inadequate osteointegration, risk of inflammation, infection, or the need for secondary surgical interventions. In this study, a multifunctional strategy for cranial reconstruction is proposed, combining additive manufacturing, bioactive surface functionalization, and local drug delivery. Porous polylactic acid (PLA) scaffolds were fabricated by Fused Deposition Modelling (FDM) to obtain lightweight structures with controlled porosity. The scaffolds were subsequently functionalized with hydroxyapatite coatings, deposited through sol–gel, to provide osteointegrative properties. To locally modulate post-implant inflammatory responses, a drug delivery system based on polycaprolactone (PCL) microparticles loaded with dexamethasone was developed and entrapped within hydroxyapatite-coated PLA structures. The produced systems were extensively characterized in terms of morphology, mechanical and thermal behavior, structural properties, biological response, and drug release behavior. Results demonstrated that the 3D-printed scaffolds exhibited homogeneous hydroxyapatite coatings, whose continuity and retention were enhanced by NaOH surface pre-treatment. Biological assays demonstrated that HAp coating significantly improved cell viability and osteogenic differentiation, confirming the osteoconductive potential of the scaffolds for craniofacial bone regeneration applications. Dexamethasone-loaded PCL microparticles were successfully integrated into the coated scaffolds, exhibiting controlled drug release, absence of cytotoxicity, and homogeneous distribution within the porous architecture, thereby demonstrating the feasibility of a multifunctional platform combining bone-regenerative and therapeutic delivery functionalities. Overall, the proposed multifunctional scaffolds represent a promising, low-cost and customizable approach for advanced cranioplasty applications, integrating structural support, osteointegration and local anti-inflammatory therapy within a single system. Full article