Mazedur Rahman

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.

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.

This study proposes two novel stiffened panel configurations, designated X and X-30, manufactured from the conventional aerospace alloy Al 2024-T3 to enhance the critical buckling resistance under in-plane compression. Their performance was evaluated against traditional T-, I-, L-, and Omega-type stiffeners, as well as a newly introduced Y-panel found in the literature. Initial results show that both proposed designs achieve 80–200% higher buckling capacity than conventional panels, with only a 4.54% difference between the X and X-30 configurations. A weight-constrained optimization was then conducted using a Box–Behnken design of experiments combined with a multi-objective genetic algorithm in Ansys DesignXplorer. After correcting inconsistencies in the initial optimization ranges, the prediction error in the optimized buckling values was reduced to 4%. The optimized X panel attained the highest critical buckling load of 2920 kN, followed by the X-30 panel with 2744.3 kN, corresponding to a 114–258% improvement over traditional stiffener geometries. A Pearson correlation matrix further suggested that, for all the stiffened panels except Omega, the base plate showed a strong correlation with the critical buckling load, typically ranging from 0.83 to 0.99. In contrast, for the X-30 panel, the lower base part also showed a strong correlation of 0.93.

Natural fiber-reinforced unidirectional composites are increasingly adopted in modern industries due to their superior mechanical performance and desirable properties from both material and engineering perspectives. Among various approaches, representative volume element (RVE) generation and analysis is considered one of the most suitable and convenient methods for predicting the elastic moduli of composites. The main aim of this study is to investigate and compare the elastic moduli of natural fiber–reinforced unidirectional composite RVEs using theoretical, numerical, and machine learning models. The numerical predictions in this study were generated using the ANSYS Material Designer tool (version ANSYS 19). A comparison was made between experimental results reported in the literature and different theoretical models, showing high accuracy in validating these numerical outcomes. A dataset comprising 1600 samples was generated from numerical models in combination with the well-known theory of RVE, namely rule of mixture (ROM), to train and test two machine learning algorithms: Random Forest and Linear Regression, with the goal of predicting three major elastic moduli—longitudinal Young’s modulus (E₁₁), in-plane shear modulus (G₁₂), and major Poisson’s ratio (V₁₂). To evaluate model performance, mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R²) were calculated and compared against datasets with and without the theoretical values as input variables. The performance metrics revealed that with the theoretical values, both Linear Regression and Random Forest predict E₁₁, G₁₂, and V₁₂ well, with a maximum MSE of 0.033 for G₁₂ and an R² score of 0.99 for all cases, suggesting they can predict the mechanical properties with excellent accuracy. However, the Linear Regression model performs poorly when theoretical values are not included in the dataset, while Random Forest is consistent in accuracy with and without theoretical values.

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.

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.

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