Experimental Investigation on the Mechanical Properties of Jute Fiber and Silica Nano Particles Using Artificial Neural Network

This study explores the impact of silica nanoparticles on jute fiber-reinforced composites with epoxy resin matrices. Silica nanoparticles were synthesized at three concentrations (3%, 6%, and 9%) and incorporated into composites at varying fiber–resin weight ratios. The composites were subjected to tests for tensile strength, flexural strength, impact strength, and hardness. The Taguchi signal-to-noise ratio method was employed for optimization. Results indicate that a 9% addition of
silica nanoparticles significantly enhances the mechanical properties of jute fiber-reinforced composites. Tensile and flexural strength increased with higher silica nanoparticle content, while impact strength and hardness also improved. Notably, a 9% silica addition achieved a maximum tensile
strength of 72 MPa, resulting in a 10% increase over that yielded by the 3% addition. Flexural and impact strengths improved by 23% and 20%, respectively, when compared to the 3% silica addition. Furthermore, a neural network model accurately predicted the composite’s mechanical characteristics with 100% accuracy. These findings hold promise for the automobile and aircraft industries, as they
require high-performance materials. The integration of jute fibers and silica nanoparticles into composites offers a sustainable and eco-friendly alternative to conventional materials. The enhancement strategy employed in this analysis can be applied to enhance the mechanical properties of other composite materials.