Response Surface Methodology and Mayfly Optimization for Predicting the Properties of Cold-Sprayed AA2024/Al2O3 Coatings on AZ31B Magnesium Alloy

Magnesium (Mg) alloys are used more often today in the automotive and aviation industries. Owing to their many beneficial characteristics, such as higher thermal conductivity, greater strength, lower density and weight, etc., magnesium alloys tend to corrode when employed in particular moist regions, and their poor wear characteristics may reduce their lifespan. To deal with such challenges, a surface coating technique was used on the AZ31B Mg alloy with AA2024/Al 2 O 3 coating powder through a cold spray process. The porosity, corrosion, and wear rate of the deposited Mg alloy are investigated. Box–Behnken designs (BBD) are used to plan the experimental investigation. The results were verified using the Mayfly optimization technique in MATLAB 2021, which relies on a hybrid deep belief network, and also through the BBD-response surface methodology (RSM) in Design Expert 11. The metal matrix composite coating has a minimal porosity, corrosion rate, and wear loss of 0.52%, 1.35 mm/year, and 0.52 mg, experimentally. As a result, the hybrid deep belief network-mayfly optimization estimated results are more similar to the experimental results compared to the BBD-RSM.