Effects of Tribology and Mechanical Properties on Silicon Carbide and Glass Fiber-Reinforced Hybrid Nanocomposites

This study aims to enhance the mechanical and wear properties of hybrid nanocomposites by incorporating SiC nanoparticles and glass fibers into an epoxy resin matrix, utilizing a neural network for optimization. The mechanical properties were evaluated via flexural, impact, and wear tests. SiC nanoparticle concentrations were varied at three levels using the Taguchi technique. The results were optimized with the Taguchi signal-to-noise ratio approach. Regression analysis was used to determine the wear rate, flexural strength, and impact properties of the composites. SiC reinforcement significantly influenced the flexural and impact strength, along with wear resistance. The composition with 2% SiC showed a flexural strength of 95 MPa, while 4% and 6% SiC compositions exhibited strengths of 110.5 MPa and 125 MPa, respectively. The impact strength followed a similar trend. The wear test results demonstrated a decrease in the specific wear rate (Swr) and coefficient of friction (CoF) with an increasing SiC nanoparticle percentage. The optimal parameters were identified as 6% SiC nanoparticle loading, 15 N load, 160 RPM rotation speed, and a 40.2 mm sliding distance. The enhancement in impact strength is attributed to SiC nanoparticle reinforcement. The results were further refined using an artificial neural network for improved predictability. This research underscores the effectiveness of hybrid nanocomposites with SiC nanoparticles and glass fibers, as well as the potential of neural networks for process optimization, benefiting industries requiring high-performance materials.