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.

Commercial ductile steel sheet is widely used in engineering applications because of its favorable combination of strength, ductility, machinability, and mechanical versatility. However, its structural integrity is frequently compromised by geometric discontinuities, specifically notches. These stress raisers negatively affect fracture toughness and fatigue strength. The V-notch is particularly prevalent and critical in structural failure analysis. This study presents an experimental, numerical, and machine-learning investigation of the tensile properties of ductile steel sheet, focusing on various V-notch geometries. A systematic experimental study of 36 distinct specimens isolated the effects of geometric variations. Tensile tests were conducted in accordance with ASTM E8 standards to ensure reliability. Experimental results quantified tensile property degradation due to stress concentration, revealing severely compromised ductility in notched specimens. A validated numerical method subsequently captured necking and fracture behavior, reducing the need for expensive destructive experiments. Additionally, experimental data established a predictive framework for untested geometries. Random Forest, LightGBM, CatBoost, and XGBoost ensemble algorithms were employed for training and validation. Engineered features and hyperparameter tuning trained the models to effectively capture the nonlinear stress–strain region. CatBoost and LightGBM (R² = 0.9917 and 0.9902, respectively) provided the most stable predictive performance. This study establishes a validated baseline for V-notch behavior while demonstrating the effectiveness of data-driven models in minimizing extensive destructive testing.