This study investigates the effects of 3D printing parameters on the mechanical properties (predominantly tensile properties) of a commercial polylactic acid-based wood fiber composite material known as wood filament. The influence of printing parameters, including layer thickness, infill density, printing speed, and nozzle temperature on the mechanical properties, is studied, and the design of the experiment (DOE) is made through Taguchi L₉ orthogonal array. The specimens for the tensile test are fabricated by the material extrusion (MEX) 3D printer, which is also known as fused deposition modeling (FDM) or fused filament fabrication (FFF). After conducting the tensile test, this research considers four significant outcomes: tensile strength, maximum load, elastic modulus, and elongation at break. Further analysis of the obtained results from mechanical testing is performed through analysis of variance (ANOVA) to determine the significance of each parameter on the mechanical properties. Moreover, prediction and optimization are conducted to verify the obtained results from the DOE. Furthermore, scanning electronic microscopy (SEM) is used to analyze the fracture zones, cracks, voids, and fiber/matrix adhesion of the FDM fabricated parts which demonstrates that the lower layer thickness provides better adhesion and fewer voids between successive layers and thus exhibits better mechanical performance. Graphical abstract

Lattice structures are regularly employed in different industries ranging from biomedical to automobile and aircraft due to their excellent mechanical properties, outstanding load carrying and energy absorption capabilities, and better strength-to-weight ratio compared to traditional structures. On the other hand, fused deposition modeling (FDM) is a cost-effective method of additive manufacturing (AM) vastly used for plastic materials which are biocompatible, biodegradable, and environment-friendly in nature. The main aim of this study is to investigate the effect of FDM printing parameters, namely, layer height, nozzle temperatures, printing speeds, and bed temperatures, on a simple cubic lattice structure printed from PLA filament. The design of the experiment is conducted through L₁₆ orthogonal array. After conducting compression tests, four significant outcomes, namely, modulus of elasticity, compressive strength, fracture strain, and modulus of toughness, are calculated from the stress–strain curves. Furthermore, an ANOVA (analysis of variance) test is carried out to find out the influence of each parameter. The analysis revealed that layer height is the most crucial parameter for modulus of elasticity and compressive strength. Secondly, the study also demonstrates the signal-to-noise ratio (S/N) analysis of each parameter and suggests the best manufacturing parameters, such as the layer height, printing temperature, printing speed, and bed temperature as 0.1 mm, 210 °C, 30 mm/s, and 60 °C, respectively, for the highest compressive strength. An SEM (scanning electron microscopy) analysis is carried out to examine the defects of the optimized lattice structure and found that the optimized structure has fewer defects in comparison to the non-optimized lattice core. Finally, based on these optimized parameters, a bone scaffold model is proposed for future biomedical applications. Graphical abstract

The current research aimed to investigate the critical buckling load of a simply supported aerospace-grade stiffened panel made of additively manufactured cubic lattice unit cell arrays, namely simple cubic, face-centered cubic (FCC) and body-centered cubic (BCC) structures. Ansys Design Modeler was chosen to design and analyze the critical buckling load of the panel, while a popular material, Ti-6Al-4V, was used as the build material. Numerical validation on both the stiffened panel and a lattice beam structure was established from multiple resources from the literature. Finally, the panels were tested against increments of a strut diameter ranging from 0.5 mm to 2 mm, which corresponds to a relative density of 6% to 78%. It was found that considering the relative density and fixed relative density, the simple cubic lattice cell outperformed the buckling results of the FCC and BCC panels. Moreover, the relationship of the parameters was found to be non-linear. Finally, the data samples collected from numerical outcomes were utilized to train four different machine learning models, namely multi-variable linear regression, polynomial regression, the random forest regressor and the K-nearest neighbor regressor. The evaluation metrics suggest that polynomial regression provides the highest accuracy among all the tested models, with the lowest mean squared error (MSE) value of 0.0001 and a perfect ${\mathrm{R}}^2$ score. The current research opens up the discussion of using cubic lattice cells as potential structures for future stiffened panels.

Elastic moduli are important mechanical properties that describe a material’s stiffness and its deformation under elastic loading. In addition to experimental techniques, computational homogenization is commonly used for composite materials to calculate their elastic moduli. This research employs a deep learning algorithm, specifically a Feedforward Neural Network (FNN), to predict the longitudinal and transverse Young’s modulus, shear modulus, and Poisson’s ratio of various unidirectional (UD) composites. The predictions are based on several features, including the names of the composites, Young’s moduli and Poisson’s ratios of the fibers and matrices, and the fiber volume fraction. Initially, 20 different UD composites were selected from the existing literature. ANSYS-19 Material Designer was then utilized to calculate the elastic moduli of these materials while varying the fiber volume fraction from 0.2 to 0.7. This process generated a dataset of 1948 samples, with 80% of the data allocated for training the FNN model and the remaining 20% used to evaluate performance metrics of the test data. These metrics include mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and R² score. The results indicate that, with optimized hyperparameters, the FNN model can accurately predict the elastic moduli, demonstrating its effectiveness as a tool for calculating the elastic properties of UD composites.

Stiffened panels are extensively used in aerospace applications, particularly in wing and fuselage sections, due to their favorable strength-to-weight ratio under in-plane loading conditions. This research employs the commercial finite element software Ansys-19 to analysis the critical buckling and ultimate collapse load of an aluminum stiffened panel having a dimension of 1244 mm (Length) × 957 mm (width) × 3.5 mm (thickness), with three stiffener blades located 280 mm away from each other. Both the critical buckling load and post-buckling ultimate failure load of the panel are validated against the experimental data found in the available literature, where the edges towards the length are clamped and simply supported, and the other two edges are free. For nonlinear buckling analysis, a plasticity power law is adopted with a small geometric imperfection of 0.4% at the middle of the panel. After the numerical validation, the investigation is further carried out considering four different lateral pressures, specifically 0.013 MPa, 0.065 MPa, 0.085 MPa, and 0.13 MPa, along with the compressive loading boundary conditions. It was found that even though the pressure application of 0.013 MPa did not significantly impact the critical buckling load of the panel, the ultimate collapse load was reduced by 18.5%. In general, the ultimate collapse load of the panel was severely affected by the presence of lateral pressure while edge compressing. Three opening shapes—namely, square, circular, and rectangular/hemispherical—were also investigated to understand the behavior of the panel with openings. It was found that the openings significantly affected the critical buckling load and ultimate collapse load of the stiffened panel, with the lateral pressure also contributing to this effect. Finally, in critical areas with higher lateral pressure load, a titanium panel can be a good alternative to the aluminum panel since it can provide almost twice to thrice better buckling stability and ultimate collapse load to the panels with a weight nearly 1.6 times higher than aluminum. These findings highlight the significance of precision manufacturing, particularly in improving and optimizing the structural efficiency of stiffened panels in aerospace industries.

Woven composites and natural fiber-reinforced composites both have widespread applications in various industries due to their appealing load-carrying capacity and performance compared to conventionally manufactured composites, such as polymeric composites. Representative volume element (RVE) generation is one of the most effective and widely adopted methods for estimating mechanical performance in current research. This study aims to explore the effects of three significant factors in woven composite RVEs: yarn spacing (from 0.5 mm to 1.5 mm), fabric thickness (from 0.2 to 0.5 mm), and shear angle (from 3.5 to 15 degrees) through finite element methods and statistical analysis to understand their effectiveness in the elastic moduli’s. The validation of this research has been conducted using available literature. The generation of representative volume elements (RVEs) and the calculation of elastic moduli were performed using ANSYS-19, including the material designer feature. The experimental design was carried out using Design-Expert software version 13, which used response surface methodology. The materials selected for this study were jute fiber and epoxy. After obtaining the elastic moduli from the ANSYS material designer, three responses were considered: longitudinal Young’s modulus (E₁₁), in-plane shear modulus (G₁₂), and major Poisson’s ratio (V₁₂). ANOVA (Analysis of Variance) and 3D contour graphs were generated to further analyze and correlate the effects of the selected materials on these responses. These investigations revealed that in comparison to twill structure, plain structure in natural fiber-reinforced woven composites could be a good alternative. Additionally, the findings highlighted that yarn spacing and fabric thickness significantly influence the considered moduli in plain-weave NFRC material RVEs. However, in twill-woven composite RVEs, the effects of yarn spacing, fabric thickness, and shear angle were found to be considerable. Moreover, statistical analysis has found the best combinations for both plain and twill structures, while the yarn spacing was 1 mm, the shear angle was 9.25 degrees, and the fabric thickness was 0.35 mm.