Abstract
Under a global increase in demand for panels, there is a growing need to develop sustainable and environmentally friendly alternatives to conventional formaldehyde-based adhesives for particleboard manufacturing. This research presents a comprehensive evaluation of predicting the physical and mechanical properties of acid-based panels through novel applications of machine learning (ML) algorithms. The study collected data from peer-reviewed literature spanning various acid-based adhesives, including citric acid, tannic acid, maleic acid, lactic acid, and suberinic acid, combined with diverse raw materials. The methodology employed Pearson correlation matrix analysis to quantify linear relationships among processing parameters (temperature, duration, pressure) and performance properties (water absorption (WA), thickness swelling (TS), modulus of rupture (MOR), and modulus of elasticity (MOE)). A dendrogram hierarchical clustering analysis using Ward's linkage method and Euclidean distance metrics was implemented to identify natural groupings within the dataset based on moisture resistance and mechanical performance characteristics. Three machine learning (ML) algorithms, namely decision tree (DT), light gradient boosting machine (LightGBM), and eXtreme gradient boosting (XGBoost), were systematically compared for predictive accuracy using metrics including R-squared, mean squared error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). K-fold cross-validation was conducted to assess model generalization capabilities and detect overfitting. Results demonstrate that XGBoost achieves superior prediction accuracy with R2 values of 0.9998 and 0.9999 for TS and MOE, significantly outperforming traditional DT models. Feature importance and SHapley Additive exPlanation (SHAP) analyses reveal that WA dominates TS prediction (49.87 %), while the temperature parameter controls MOE prediction (93.50 %). The findings demonstrate that temperature exhibits strong correlations with mechanical properties, and clustering analysis identifies three distinct performance levels based on their physical and mechanical properties. However, despite the high accuracy of the models, cross-validation analysis indicates generalization limitations due to outlier sensitivity, suggesting that future research should expand datasets and incorporate additional input variables, such as board density, curing kinetics, and long-term durability indicators, to enhance predictive capabilities for next-generation sustainable acid-based wood composites.