Mechanical optimization of an Al-Al2O3 composite nanowire via molecular dynamics simulation and metaheuristic optimization

Abstract

In the present study, mechanical properties optimization is investigated for an Al-Al2O3 composite nanostructure. The Al-Al2O3 composite nanostructure is considered an Aluminum nanowire reinforced by spherical Al2O3 particles. The structure tensile test is simulated via molecular dynamics simulation using LAMMPS. The mechanical properties of the composite are extracted from the obtained stress-strain curve of the composite. The important mechanical properties include maximum stress and toughness. An optimization process is then applied to maximize the mechanical properties of the composite via metaheuristic optimization algorithms including Genetic Algorithm (GA), Ant Colony (ACO), and Grey Wolf Optimizer (GWO). Since the studied nanostructure is mono crystal, the reinforcing mechanism differs from that of a grained macro material. Therefore, the optimization variables are not confined to size and volume fraction but they also include the location of the particles. The optimization is performed for 0.05 and 0.10 volume fractions and different particle sizes with respect to the location of particles. Applying the optimization process, the mechanical properties of the studied composite nanowire are substantially improved for tensile loading. The results reveal that the placement of the particles has a considerable effect on the improvement of mechanical characteristics. At last, a pattern is presented for the placement of particles to achieve the highest tensile characteristics of Al-Al2O3 composite nanowires.