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.

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.

The present research proposes an Artificial Neural Network (ANN) model to predict the critical buckling load of six different types of metallic aerospace grid-stiffened panels: isogrid type I, isogrid type II, bi-grid, X-grid, anisogrid, and waffle, all subjected to compressive loading. Six thousand samples (one thousand per panel type) were generated using the Latin Hypercube Sampling method to ensure a diverse and comprehensive dataset. The ANN model was systematically fine-tuned by testing various batch sizes, learning rates, optimizers, dense layer configurations, and activation functions. The optimized model featured an eight-layer architecture (200/100/50/25/12/6/3/1 neurons), used a selu–relu–linear activation sequence, and was trained using the Nadam optimizer (learning rate = 0.0025, batch size = 8). Using regression metrics, performance was benchmarked against classical machine learning models such as CatBoost, XGBoost, LightGBM, random forest, decision tree, and k-nearest neighbors. The ANN achieved superior results: MSE = 2.9584, MAE = 0.9875, RMSE = 1.72, and R² = 0.9998, significantly outperforming all other models across all metrics. Finally, a Taylor Diagram was plotted to assess the model’s reliability and check for overfitting, further confirming the consistent performance of the ANN model across both training and testing datasets. These findings highlight the model’s potential as a robust and efficient tool for predicting the buckling strength of metallic aerospace grid-stiffened panels.