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

Key Points

• This research aims to optimize cold spray parameters to minimize wear loss of composite coatings on magnesium alloy.
• Employed a three-factor, five-level central composite design for optimization.
• Developed a quadratic regression model connecting wear loss to carrier gas temperature, nozzle jet distance, and powder injection rate.
• Used a genetic algorithm to predict minimal wear loss and validated experimentally.
• Identified carrier gas temperature as the most influential parameter (p < 0.0001, R² = 0.9528).
• Achieved a predicted minimum wear loss of 2.2877 mg with experimental validation errors of 1.64% to 3.70%.
• Computed a composite desirability of 0.871 from desirability function analysis.