This study investigates the optimization and prediction of mechanical properties in 3D-printed PLA composites reinforced with graphene nanoplatelets (GNP). The effects of GNP content (0, 2, and 5 wt.%), nozzle temperature (190–210 °C), print speed (20–60 mm/s), and layer thickness (0.15–0.35 mm) on tensile strength, Young’s modulus, and hardness were analyzed using a central composite design, at three print orientations (0°, 45°, and 90°). Compared to pure PLA, the incorporation of 5 wt.% GNP led to a 67% improvement in tensile strength, a 205% increase in Young’s modulus, and a 44% enhancement in hardness. Advanced machine learning models, such as XGBoost and Gaussian Process Regression, were employed for prediction, with R² values exceeding 0.99 and MAPE below 4%. The models were validated using K-Fold Cross-Validation (K = 5), ensuring reliable and robust predictions while preventing overfitting. SHAP (Shapley Additive exPlanations) analysis indicated that GNP composition and layer thickness were the most influential factors, with SHAP values ranging between ±0.75. The Gaussian Process model outperformed both Linear Regression and XGBoost, achieving the highest R² of 0.9900 ± 0.0021, the lowest MSE (0.6593 ± 0.1054), RMSE (0.812 ± 0.323), MAE (0.6755 ± 0.1123), MAPE (3.157% ± 0.320), and RRMSE (3.409% ± 0.513), highlighting its superior predictive accuracy and stability. This integrated methodology, combining experimental optimization, ANOVA, and interpretable machine learning, presents a promising and potentially robust strategy for optimizing the mechanical performance of GNP-reinforced PLA composites, emphasizing their potential for high-performance engineering applications.

This study focuses on the valorization of coconut shell biochar (CCB) as a sustainable reinforcement in polylactic acid (PLA) biocomposites, targeting enhanced mechanical performance. PLA/CCB composites were fabricated by varying injection molding parameters at three levels: composition (Pure, 5 wt%, 10 wt%), injection temperature (135 °C, 145 °C, 155 °C), injection speed (50 mm/s, 60 mm/s, 70 mm/s), and injection pressure (30 bar, 40 bar, 50 bar). A Taguchi L27 orthogonal array was employed to systematically assess the effects of these parameters on tensile strength, Young’s modulus, and hardness. ANOVA results indicated that composition and injection temperature were the most influential factors, contributing 50.42% and 42.67% to tensile strength, and 38.58% and 20.14% to Young’s modulus, respectively. For hardness, composition dominated with a 78.3% contribution. To predict the mechanical responses, five machine learning models, including Linear Regression, Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosting, and Extreme Gradient Boosting (XGBoost), were implemented. Gradient Boosting and XGBoost exhibited superior predictive accuracy, with R² values of 98.77% for tensile strength, 96.28% for Young’s modulus, and 96.45% for hardness. The integration of Taguchi design, ANOVA-based analysis, and advanced machine learning techniques offers a robust framework for optimizing process parameters and valorizing CCB as a high-performance, eco-friendly reinforcement in biodegradable biocomposites.

Our research described in this paper focused on fabricating and characterising polylactic acid (PLA) composites reinforced with randomly dispersed boron nitride nanoplatelets (BNNP). The integrated experimental analysis with the Finite Element Method (FEM) computational simulation employed a multiscale modelling approach used to evaluate the effects of BNNP fillers at varying weight fractions (i.e. 0.005% to 0.04%). The simulations utilised Representative Volume Elements (RVEs) with 1 x 1 x 1 μm dimensions, incorporating randomly dispersed BNNP to mimic the realistic composite behaviour. The modulus predicted through the RVE approach was validated against empirical studies and pre-existing micromechanical models to ensure accuracy and reliability. The empirical findings revealed significant enhancements in the modulus of elasticity, tensile strength and flexural strength of the PLA reinforced with BNNP composites exhibiting improvements of 17.43%, 40% and 61% respectively at elevated filler concentrations of 0.005, 0.01,0.02,0.03 and 0.04. The Scanning Electron Microscopy (SEM) analysis of the fracture surfaces indicated a transition from ductile to brittle fracture patterns as the BNNP content increased, underscoring the reinforcing effect of the nanoplatelets. Flexural testing further validated improvements in the material rigidity and resistance to bending. The Finite element analysis (FEA) simulations strongly correlated with the experimental data, with deviations remaining within an acceptable range. This integrated approach underscores the efficacy of BNNP fillers in enhancing the mechanical attributes of PLA, yielding significant perspectives for developing eco-friendly composite materials exhibiting superior performance features.

This research explores the optimization of mechanical properties and predictive modeling of polylactic acid (PLA) reinforced with boron nitride nanoplatelets (BNNPs) using data-driven machine learning (ML) models. PLA-BNNP composites were fabricated through injection molding, with a focus on how key processing parameters influence their mechanical performance. A Taguchi L27 orthogonal array was applied to assess the effects of BNNP composition (0.02 wt.% and 0.04 wt.%), injection temperature (135–155 °C), injection speed (50–70 mm/s), and pressure (30–50 bar) on properties such as tensile strength, Young’s modulus, and hardness. The results indicated that a 0.04 wt.% BNNP loading improved tensile strength, Young’s modulus, and hardness by 18.6%, 32.7%, and 20.5%, respectively, compared to pure PLA. Taguchi analysis highlighted that higher BNNP concentrations, along with optimal injection temperatures, improved all mechanical properties, although excessive temperatures compromised tensile strength and modulus, while enhancing hardness. Analysis of variance (ANOVA) revealed that injection temperature was the dominant factor for tensile strength (68.88%) and Young’s modulus (86.39%), while BNNP composition played a more significant role in influencing hardness (78.83%). Predictive models were built using machine learning (ML) models such as Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and Extreme Gradient Boosting (XGBoost). Among the ML models, XGBoost demonstrated the highest predictive accuracy, achieving R² values above 98% for tensile strength, 92–93% for Young’s modulus, and 96% for hardness, with low error metrics i.e., Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE). These findings underscore the potential of using BNNP reinforcement and machine learning-driven property prediction to enhance PLA nanocomposites’ mechanical performance, making them viable for applications in lightweight packaging, biomedical implants, consumer electronics, and automotive components, offering sustainable alternatives to petroleum-based plastics.