Journal of Materials & Metallurgical Engineering Review Article
Application of Genetic Algorithm for Maximize Mechanical Properties of AZ61 Alloy and Minimize Casting Defect
Abstract
This paper presents a comprehensive study of applying a Multi-Objective Genetic Algorithm (MOGA) to simultaneously maximise mechanical properties (Ultimate Tensile Strength, Yield Strength, and Elongation) of AZ61 magnesium alloy and minimise casting defects (specifically porosity and hot tearing susceptibility). AZ61 is a highly promising lightweight structural material widely used in the automotive and aerospace industries for its excellent strength-to-weight ratio and damping capacity. However, its hexagonal close-packed (HCP) crystal structure limits room-temperature ductility, and conventional casting methods frequently introduce significant defects such as gas porosity, shrinkage porosity, and coarse intermetallic phase distribution (ß-Mg17Al12). In this work, we implement a non-dominated sorting genetic algorithm (NSGA-II) combined with experimental casting trials to optimise key processing parameters: pouring temperature, mould temperature, squeeze pressure, cooling rate, and degassing time. The microstructural evolution, phase distribution, and fracture mechanisms are thoroughly investigated. The results demonstrate that GA-optimised casting parameters significantly refine the alpha-Mg matrix, transforming the coarse, continuous network of the eutectic beta-phase into a fine, highly dispersed, discontinuous morphology. Consequently, the ultimate tensile strength (UTS) increased from 210 MPa (as-cast) to 312 MPa (optimised and heat-treated), while elongation improved from 6.5% to 13.8%, and the casting defect volume fraction was reduced to a negligible level (<0.4%). This study establishes a robust, intelligent optimisation framework for high-performance magnesium alloy casting.
Keywords
References (84)
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