Effect of Sugarcane/Al ₂ O ₃ /TiO ₂ Reinforcement on Thermal Conductivity of Epoxy Hybrid Composites Using Parallel GA-PSO Modeling Evaluation

The present investigation studies the thermal conductivity of epoxy hybrid composites incorporating sugarcane, alumina (Al2O3), and titania (TiO2) with a parallel Genetic Algorithm-Particle Swarm Optimization (parallel GA-PSO) modeling technique. The generated MATLAB code enables statistical and systematic parametric evaluation of thermal conductivity for these epoxy laminates, employing GA and PSO for fine-tuning and extensive solution exploration. The model predicts enhanced thermal conductivity of hybrid composites by optimizing crucial factors such as particulate loading and fiber volume proportion. The optimization frameworks, with adjustable factors including mutation rate, crossover rate, and population size, facilitate comprehensive experimentation to determine thermal conductivity improvements. This investigation determines the effectiveness of the approach in optimizing the accurate configuration of reinforcement, with findings validated through residual and regression analysis. The research develops an effective optimization approach to enhance polymer composite thermal conductivity for engineering applications using lightweight structures. However, the current findings are based entirely on computational modeling and require experimental validation to confirm practical applicability.