Search Results (338)

The roles of layer height, build orientation, and infill density in determining mechanical properties are well recognized in Fused Deposition Modelling (FDM). However, the combined influence of infill topology, density, and skin layer configuration on structural performance and resource efficiency has not been thoroughly investigated. This research presents a systematic multi-objective investigation of infill architectures, aiming to simultaneously maximize tensile strength and minimize printing time, material consumption, and energy usage. Six infill patterns (concentric, line, triangle, honeycomb, grid, and gyroid) were evaluated at three density levels (50%, 75%, and 90%) across multiple skin layer configurations using an L36 orthogonal experimental design. Analysis of variance (ANOVA) quantified the relative significance of process parameters on tensile performance. The results reveal that the infill topology strongly influences tensile strength, with continuous, load-aligned filament paths (concentric, linear, and gyroid) outperforming segmented lattice geometries. Notably, the concentric infill pattern achieved the highest tensile performance while simultaneously reducing printing time, material usage, and energy consumption. This performance is attributed to enhanced load transfer along continuous filament trajectories, which mitigates stress concentrations at filament junctions and interlayer interfaces. These findings provide a novel, design-oriented framework for optimizing FDM infill architectures and demonstrate that strategic topology selection can improve both mechanical efficiency and sustainability without relying solely on high-density infill. Full article

To address the critical challenges of geological hazards, such as water and mud inrush, encountered during the construction of deep-buried tunnels in China, this study investigates the hydraulic properties of remolded mud-infill materials. A multi-scale approach, integrating indoor variable-head permeability tests with scanning electron microscopy (SEM), was employed to characterize the evolutionary patterns of the permeability coefficient (k). Specifically, the research evaluates the independent influences of moisture content, dry density, and confining pressure, alongside the synergistic coupling between dry density and hydration state. The results demonstrate the following: Under independent variable conditions, k exhibits a monotonic decline with increasing dry density and confining pressure while showing a positive correlation with moisture content, with the sensitivity varying significantly across different parameter regimes; under coupled effects, the permeability in both low- and high-moisture ranges manifests a distinct “increase–decrease–increase” fluctuation as dry density rises, reaching a local peak at 2.20 g/cm³. Notably, a relative minimum k (6.12 × 10⁻⁷ cm/s) is achieved at the optimum moisture content (5.8%); micro-mechanistic analysis reveals that low-moisture samples are characterized by randomized angular particles and well-developed interconnected macropore networks, facilitating higher k values. Conversely, high-moisture samples exhibit preferential plate-like stacking dominated by occluded micropores, resulting in a substantial reduction in hydraulic conductivity. This study elucidates the multi-factor coupling mechanism governing the seepage behavior of remolded mud, providing essential theoretical benchmarks for the prediction and mitigation of water–mud outburst disasters in deep underground engineering, thereby ensuring the structural stability and operational safety of tunnel projects. Full article

This study investigates the impact response and energy absorption performance of additively manufactured PLA–geopolymer hybrid composites for potential application in road safety barriers. Hybrid Charpy specimens were fabricated with three different infill densities (20%, 60%, and 100%), combining a 3D-printed PLA outer shell with a geopolymer core. Charpy impact tests were conducted in accordance with ISO 179-1 and ASTM D6110, and the absorbed energy, specific energy absorption, and mass efficiency were determined experimentally. A phase-based analytical model was also used to estimate elastic energy contributions, while fracture surfaces were examined to identify infill-dependent damage mechanisms. To extend the material-level findings to an engineering-scale application, the observed trends were transferred to a New Jersey-type road safety barrier model and evaluated using ANSYS Explicit Dynamics. The results showed that infill density strongly affects fracture behavior and energy dissipation performance, with 60% infill providing the most balanced response in terms of energy absorption and mass/material efficiency. The originality of the present study lies in going beyond a material-scale investigation of the impact behavior of additively manufactured PLA–geopolymer hybrid structures by integrally correlating the experimental Charpy results with a theoretical energy-based framework, fracture-surface observations, and explicit dynamic finite element analysis of a New Jersey-type road safety barrier model. Full article

Additive manufacturing (AM), particularly Fused Deposition Modeling (FDM), has revolutionized polymer-based fabrication through design freedom and material efficiency. This work presents a comprehensive statical optimization of FDM parameters affecting the flexural properties of acrylonitrile/butadiene/styrene (ABS) specimens. The effects of layer thickness (0.15–0.25 mm), infill density (30–70%), printing speed (35–95 mm/s), and build orientation (Flat, On-edge, Vertical) were investigated following ASTM D790 standards. A Taguchi L9 orthogonal array coupled with ANOVA analysis was employed to quantity parameter significance. According to the ANOVA analysis, infill density was identified as the most influential parameter, accounting for 61.3% of the variation in flexural strength (σ_f) and 60.1% in flexural modulus (E_b). The optimal configuration (0.25 mm layer thickness, 70% infill, 65 mm/s speed, horizontal orientation) yielded a flexural strength of 84.9 MPa and modulus of 2.54 GPa. Microstructural observations confirmed that higher infill and moderate speed improved interlayer fusion and reduced void formation. The developed Taguchi–ANOVA framework offers quantitative insights for tailoring process–structure–property relationships in polymer-based additive manufacturing. Full article

This study aims to investigate the effect of manufacturing parameters on the mechanical properties of PC-ABS samples produced by the Fused Deposition Modeling (FDM) additive manufacturing method and to model these relationships using machine learning methods. In the study, the parameters of printing speed, infill density, and raster angle were determined according to the Taguchi L16 experimental design, and tensile, bending, and impact tests were performed on the produced samples. Experimental results showed that the infill density parameter resulted in an increase in tensile strength of approximately 62% (from 25.10 MPa to 40.71 MPa) and an increase in flexural strength of approximately 46% (from 45.13 MPa to 66.13 MPa). Furthermore, an improvement in impact energy of approximately 45% (from 1.698 J to 2.467 J) was achieved under optimum printing speed conditions. Mechanistic properties were predicted using Decision Tree, Random Forest, K-Nearest Neighbors, and Multilayer Perceptron models with a dataset generated from experimental data. Comparing the model performances, the Random Forest algorithm was found to provide the highest prediction performance with accuracy in the R² range of 0.94–0.99 and RMSE values below 0.5, demonstrating strong generalization capabilities. The results showed that infill density is the most decisive parameter on both tensile and flexural strength, and that printing speed has a significant effect, especially on impact energy. ANOVA analyses revealed that all main parameters have statistically significant effects on mechanical properties. In the performance comparison of the machine learning models, the Random Forest algorithm provided the highest prediction accuracy, demonstrating that mechanical properties can be reliably predicted. In conclusion, it has been shown that the mechanical performance of PC-ABS parts produced by the FDM method can be optimized by using the correct selection of production parameters and machine learning-based modeling approaches. 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

Rapid urban densification has intensified the scarcity of urban green space and challenged residents’ health and well-being. Pocket parks, as micro-scale infill green spaces embedded in the urban fabric, are increasingly adopted to expand everyday access to nature. Using three representative pocket parks in Nanjing, China, this study draws on self-reported data from questionnaire surveys and semi-structured interviews to characterize spatiotemporal use patterns and examine their links to perceived psychological, physiological, and social benefits through quantitative statistical analysis and modeling. Results show that pocket park use is highly routinized. Temporal patterns were evident, with weekend and autumn visits associated with improvements in emotional well-being, pain relief, and parent–child interaction. Perceived benefits were generally positive across psychological, physiological, and social domains, with psychological benefits—especially emotional relief and reduced loneliness—reported most strongly. Benefit levels varied across parks and user groups. Mechanism analysis reveals that the park supply factor, reflecting accessibility and basic facility provision, showed the most consistent direct paths to perceived benefits, whereas facility use and length of stay had no significant direct effects. These findings suggest that pocket park planning should prioritize accessibility and adequate basic provision, while strengthening activity support in ways that align with local use rhythms to enhance health-oriented performance in high-density cities. Full article

This study investigates the fatigue behavior of 3D-printed ABS subjected to cyclic torsional loads, with a focus on the effects of infill density and raster angle on torsional fatigue performance. A total of 50 test specimens representing 25 unique combinations of infill density (20%, 40%, 60%, 80%, 100%) and raster angle (25°/−65°, 45°/−45°, 75°/−15°, 0°/90°) were fabricated and tested using the cyclic torsion system. Fatigue failure was defined as a 75% reduction in torsional strength, recorded through cycle-by-cycle torque monitoring. The twist angle was cyclically varied between ±10° at a frequency of 5 Hz until failure occurred. The results indicate that increasing infill density significantly improves fatigue life by reducing internal porosity and enhancing load transfer, with the greatest gains observed at high infill levels (≥80%). Raster angle has a minimal effect at low infill densities but becomes critical at higher densities, where optimized filament orientations substantially extend fatigue life. Intermediate raster angles, particularly 25° and 75°, outperform orthogonal layouts by enabling better stress redistribution and inter-layer load sharing, while a 90° orientation leads to premature failure due to stress concentration and inter-layer debonding. When normalized by mass, specimens with 100% infill and intermediate raster angles achieve the highest fatigue endurance, highlighting the synergistic role of infill density and raster orientation in optimizing the durability and mass efficiency of 3D-printed components under cyclic torsional loading. Full article

Fused deposition modeling/fused filament fabrication (FDM/FFF) enables architectural tailoring of mechanical response through layer configuration and multi-material manufacturing strategies. However, the combined effects of layer arrangement, infill ratio, and packing geometry in polymer–metal hybrid structures and interfacial load transfer mechanisms are still not sufficiently elucidated. In this study, the tensile behavior of single- and multi-material structures produced using PLA and 17-4 PH stainless steel filaments was systematically investigated. A total of 24 experimental parameter sets were created with four-layer configurations (PLA, 17-4 PH, PLA/17-4 PH/PLA, and 17-4 PH/PLA/17-4 PH), three infill ratios (20%, 60%, and 100%), and two packing patterns (linear and hexagonal); the samples were tested according to the ASTM D638 standard. Mechanical data were modeled using Response Surface Methodology (RSM) and ANOVA, and the developed regression models showed high accuracy (R² > 0.95). The findings showed that tensile and yield strength are primarily controlled by the layer arrangement, while infill ratio and infill pattern have a secondary effect. The highest strength was measured in 100% infill linear PLA samples (≈10.35 MPa), and the lowest value was measured in 17-4 PH “green part” samples without sintering (≈0.92 MPa). Hybrid structures exhibited intermediate performance in the range of 2.9–4.9 MPa. ANOVA results showed that the majority of the mechanical variance was explained by the layer arrangement (70–85% contribution), while infill ratio and infill pattern had a secondary effect. Fracture surface analyses showed that high performance was associated with homogeneous filament fusion and low porosity; Studies have confirmed that poor performance is associated with delamination and interfacial separation. Full article

Additive manufacturing (AM) technologies are a key tool in producing complex and functional polymer parts, with Fused Deposition Modelling (FDM) emerging as the most widely used technique. PA6 polyamide is gaining increasing importance due to its high strength, wear resistance and processability, making it suitable for polymer product manufacturing. However, the mechanical properties of PA6 FDM components are largely determined by process parameters, and their optimisation is necessary to achieve stable and reliable properties. In this study, the influence of nozzle temperature, infill density and infill geometry on the tensile strength of PA6 specimens was investigated. The Central Composite Design (CCD) method was used for process modelling and optimisation, along with statistical analysis and experimental validation. The individual effects of the analysed parameters were confirmed by a preliminary experiment, while a detailed analysis of their mutual relationships was enabled through the main experiment. Analysis of the results showed that increasing both temperature and infill density positively affects tensile strength, regardless of the infill structure. The accuracy and reliability of the model were confirmed by validation, with a coefficient of determination R² = 0.8958 and a high level of agreement between experimental and predicted data. By optimising the process parameters, maximum tensile stresses of 17.705 MPa were achieved with an infill density of 74.142%, a Triangle-Hexa infill pattern, and a nozzle temperature of 254.142 °C. The confirmation experiment validated the optimised parameters, and the results provide a statistically validated framework for optimising the tensile performance of PA6 components manufactured by FDM under controlled laboratory conditions. Full article

The construction industry requires sustainable building materials to reduce its environmental impact. While using these materials in newly constructed structures primarily focuses on environmental benefits, their application in the protection of architectural heritage presents an additional requirement. These materials must be physically and chemically compatible with historical substrates to ensure the longevity of the structure. Therefore, developing eco-friendly and compatible restoration materials is a significant concern. This study aims to produce artificial aggregates to develop lightweight concrete for structural interventions and reduce natural resource consumption (i.e., minimizing the destructive extraction of natural river sand and crushed stone aggregates). Magnesium-based binders were used to mimic the carbonation process of historical lime mortars. The binders were mixed with water, shaped into coarse pellets, and cured in a ${CO}_2$ incubator for 3 and 14 days before being used in concrete production. The results show that using artificial aggregates decreased the concrete density by approximately 16.5%. Since reducing the dead load improves the seismic safety of historical masonry structures, this reduction is critical. Although the compressive strength decreased compared to natural aggregate concrete, the 14-day cured series achieved a strength of 34.7 MPa. This demonstrates that the material can be used in restoration interventions where stiffness compatibility is essential (e.g., vault infills, ring beams, or floor screeds). At the same time, since magnesium-based artificial lightweight pellets have ${CO}_2$ sequestration capacity, they can be used as a carbon-negative solution for both historical structures and broader civil infrastructure. Full article

Fused deposition modelling/fused filament fabrication (FDM/FFF) enables rapid manufacturing of functional polymer components; however, the reliability of printed parts remains strongly governed by internal architecture, process-induced porosity, and exposure to service fluids. This study quantifies the combined influence of (i) infill pattern (linear, triangular, hexagonal) at 30% density, (ii) infill density (30%, 60%, 100%) for linear infill, and (iii) short-term lubricant exposure on the tensile and microstructural response of FDM-printed polyethylene terephthalate glycol-modified (PETG) and short-carbon-fibre-reinforced PETG (PETG+CF). Specimens were printed following ISO 527-2 and tensile-tested at 5 mm/min. Microstructural analysis coupled quantitative porosity with mechanical response, Young’s Modulus, and strain-to-break. At 30% density, PETG with hexagonal infill achieved the highest tensile strength (18.54 ± 0.67 MPa), exceeding linear (16.99 ± 0.52 MPa) and triangular (14.15 ± 0.70 MPa) patterns, while triangular and linear patterns exhibited higher Young’s Modulus, indicating topology-driven decoupling of stiffness and strength. Increasing linear infill density raised strength to 31.35 ± 0.33 MPa (PETG) and 38.90 ± 0.28 MPa (PETG+CF) at 100%, consistent with reduced porosity. Seven-day immersion in SAE 15W-40 mineral engine oil reduced PETG strength by ~17% while increasing deformation to failure, whereas PETG+CF showed only minor changes. Overall, the results demonstrate that architecture-aware design, supported by quantitative porosity descriptors, is essential for ensuring the reliable mechanical performance of FDM/FFF-printed PETG-based components exposed to service fluids. Full article

Fused Deposition Modeling (FDM) enables rapid, flexible production of polymer-based parts; however, because of additive manufacturing’s nature, it creates distinct microscale surface structures. These micro-scale surface morphologies directly affect the functional properties of the parts, such as surface roughness and wettability. In this study, the surface roughness and contact angle behavior of PLA, PETG, and ABS samples printed via FDM were investigated by varying layer thickness, print orientation, and infill density. The experimental design was created using a Taguchi L16 orthogonal array. Surface roughness was determined by optical profilometry, and wettability was measured by static contact angle tests. Surface topography was supported by scanning electron microscopy (SEM) and three-dimensional surface analyses. The findings revealed that surface roughness is predominantly dependent on layer thickness, whereas wettability is more strongly influenced by printing orientation, which determines the surface’s anisotropy. The developed artificial neural network (ANN) models successfully predicted the trends in surface roughness and contact angle outputs. This study reveals the effect of micro-scale surface structures formed in the FDM process on functional surface behavior, offering a fundamental framework for developing designable surfaces for micromechanical, microfluidic, and biomedical applications. Full article

Impellers are critical components in industrial applications, requiring smooth surfaces and precise dimensions. Traditional investment casting methods are often time-consuming and costly. Fused filament fabrication (FFF), an additive manufacturing (AM) technology, offers a faster, more cost-effective alternative. FFF produces 3D-printed sacrificial patterns directly from a CAD file, making it ideal for low-volume and complex patterns. Unlike wax patterns, which can shrink or distort, 3D-printed patterns offer precise tolerances and allow for thin-walled geometries. FFF also eliminates the need for tooling, reducing capital investment. However, achieving the desired surface finish and accuracy remains a challenge. In this study, a semi-open, single-shrouded centrifugal pump impeller was fabricated using FFF with acrylonitrile butadiene styrene (ABS). A Taguchi L9 (3³) design of experiments was employed to investigate the influence of layer thickness (0.08–0.24 mm), extrusion temperature (260–280 °C), and infill density (30–70%) on dimensional accuracy and surface roughness. Dimensional deviations were evaluated for critical features, including outer diameter (OD), inner diameter (ID), blade thickness (BT), shroud thickness (ST), and blade height (BH). Results show that small and thin features (BT, ST, BH) exhibited deviations with standard deviations below 0.08 mm, whereas OD was the most affected feature with a maximum standard deviation of 0.362 mm due to dominant shrinkage effects. The optimal parameter combination for minimum dimensional deviation was identified as 0.08 mm layer thickness, 280 °C extrusion temperature, and 70% infill density. Surface roughness analysis revealed that layer thickness was the most significant factor, with Ra values ranging from 4 to 7 µm, which falls within acceptable limits for investment casting. Surfaces parallel to the XY plane demonstrated superior surface quality compared with XZ/YZ planes, highlighting the feasibility of FFF-printed ABS patterns for investment casting of complex impellers. Full article