RSM and ANN modeling for production of Al 6351/ egg shell reinforced composite: Multi objective optimization using genetic algorithm

Abstract

In this study Response Surface Methodology (RSM), artificial neural network (ANN) and non-dominated sorting genetic algorithm-II (NSGA-II) were used for modeling and multi objective optimization of Al 6351/ Egg Shell Reinforced Composite. The properties of the composite optimized were toughness and hardness whose values vary in response to changes in production process variables namely: stirring speed, stirring time and preheat temperature. An experimental design using Box-Bernken Design was used to develop an RSM model for modeling the variations in the mechanical properties of the fiberboard in response to variations in process parameters. An ANN model was equally used to predict the properties of the composite. Subsequently the ANN was used as the fitness function for multi objective optimization of the produced composite using NSGA-II. The results of the study showed that RSM effectively modeled the properties of the composite. Also the correlation coefficient of the experimental responses and ANN predictions of the properties were excellent with the minimum value being 0.9982. The optimized Pareto front of the NSGA-II algorithm would be an excellent design guide for practical applications of Al 6351/ Egg Shell Reinforced composite in engineering design.