Optimizing the Mechanical Performance of Green Composite Materials Using Muti-Integrated Optimization Solvers

Abstract

Natural fiber composites are potential alternatives for synthetic materials due to environmental issues. The overall performance of the fiber composites depends on the reinforcement conditions. Thus, this work aimed to optimize the reinforcement conditions of the natural fiber composites to improve their mechanical performance via applying an integrated scheme of Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and differential evolution (DE) methods considering various reinforcement conditions including fiber length, fiber loading, and treatment time for optimal characteristics of the composite mechanical performance. The B-Spline approximation function was adopted to predict the experimental performance of green composites. The B-Spline approximation function demonstrated incomparable accuracy compared to linear or quadratic regressions. The function is then optimized using an integrated optimization method. Results have demonstrated that optimal reinforcement conditions for the maximized desired mechanical performance of the composite were achieved with high accuracy. The robustness of the proposed approach was approved using various surface plots of the considered input-output parameter relations. Pareto front or the non-dominated solutions of the desired output mechanical properties were also obtained to demonstrate the interaction between the desired properties to facilitate finding the optimal reinforcement conditions of the composite materials.