Multi-objective Optimization of Mechanical Properties of Aluminium 7075-Based Hybrid Metal Matrix Composite Using Genetic Algorithm

The present paper involves experimental study and multi-objective optimization of mechanical properties of Aluminium 7075-based hybrid metal matrix composite fabricated by stir-casting process. The composite is reinforced with Silicon carbide (SiC) and Titanium dioxide (TiO₂) particulates. The mechanical properties that were considered in this work are impact strength, hardness and tensile strength. These properties of 7075 Al hybrid metal matrix composite are studied by performing Charpy impact test, Rockwell hardness test and tensile test, respectively. The experiments are conducted on specimens prepared by mixing the particulates in different percentage combinations such as (0, 10), (2.5, 7.5), (5, 5), (7.5, 2.5) and (10, 0) of SiC and TiO₂, respectively by maintaining the percentage of 7075 Al constant at 90 %. Based on the experimental values, second-order regression equations are fitted between each of the response parameters and the casting parameters (fraction of SiC and fraction of TiO₂) using Minitab 17 software. The equations are then optimized by defining them as the objectives of a multi-objective optimization problem (MOOP). A non-dominated sorting genetic algorithm (NSGA-II) is used to solve the MOOP. A single best compromise solution is also found from the Pareto optimal solutions obtained by NSGA II.