Genetic algorithm-assisted optimization of cold-sprayed AA2024/YSZ composite coatings on AZ31B magnesium alloy for enhanced wear resistance

This study investigates the optimization of cold spray process parameters for minimizing the wear loss of AA2024/YSZ composite coatings deposited on AZ31B magnesium alloy. A three-factor, five-level central composite design was employed to develop a quadratic regression model relating wear loss to carrier gas temperature (CGT), nozzle jet distance (NJD), and powder injection rate (PIR). The model was statistically significant (p < 0.0001, R 2 = 0.9528), identifying CGT as the most influential process parameter. Desirability function analysis yielded a composite desirability of 0.871, while sensitivity analysis confirmed the parameter influence in the order CGT > NJD > PIR. A genetic algorithm predicted a minimum wear loss of 2.2877 mg, which was experimentally validated with errors ranging from 1.64% to 3.70%.

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