Optimization of nano-filler and silane treatment on mechanical performance of nanographene hybrid composites using RSM and ANN technique

Abstract

This study utilized response surface methodology (RSM) and artificial neural networks (ANN) to optimize the formulation of polymer composites with three key variables: silane concentration, silane dipping time, and the number of nanoparticles. RSM and analysis of variance were employed to explore the effects of these variables on the composite’s mechanical properties, while ANN was used for analyzing complex interactions between parameters. The results reveal a significant correlation between the predicted and actual response values derived from the models. Notably, silane treatment of fiber was identified as the primary factor influencing the composite’s flexural strength. The study found that all fiber-related properties substantially affected both flexural strength and hardness, with silane dipping time being the most critical factor. Additionally, incorporating nanoparticles improved the fiber matrix’s strength by enhancing agglomeration. ANN achieved a 95% accuracy in predicting flexural strength and hardness. Comparisons among experimental data, the regression model, and ANN confirmed the robustness of the ANN predictions. The optimal composition identified for flexural strength was 5 wt % nanoparticles, 10 wt % silane treatment, and a 20-min dipping time. For hardness, the ideal formulation was 5 wt % nanoparticles, 15% silane treatment, and a 20-min dipping time in silane. This comprehensive approach offers a refined method for developing high-performance polymer composites with tailored properties.