Predictive modelling of surface and wetting behaviour in diamond-reinforced zinc coatings using machine learning

Cu coatings with diamond inclusion offer higher corrosion resistance, which is vital for increasing the life of parts under corrosive environments. The diamond elements serve as a barrier and slow down the corrosion process. The electrodeposition method is an economical approach to high-temperature deposition, and the optimization of parameters such as particle size and distribution remains important for obtaining the desired properties. This work aimed to study the improvement of Cu surface roughness, contact angle, and corrosion resistance by applying diamond and zinc coatings using a machine learning technique. Process variables, such as the diamond concentration, time, voltage, and the concentration of zinc, have been selected, and analysis on contact angle and surface roughness has been conducted. The key finding shows that Jaya single-objective algorithm resulted in a minimum surface roughness (SR) of 0.10868 µm and a contact angle (CA) of 44.226° at 3.5 V. However, when the Jaya (multi) algorithm was applied, SR and CA values of 0.10868 µm and 46.63°, respectively, were obtained, representing the optimal parameters as 8 wt% diamond, 3.5 V voltage, 8 min of deposition time, and 16 wt% zinc.