Preparation of Ni-SiC Nanocoatings and Prediction of Their Characteristics by Artificial Neural Networks

The Ni-SiC nanocoatings have been produced on a substrate comprising 45 steels following the jet pulse electrodeposition approach. X-ray photoelectron spectroscopy, transmission electron microscopy, scanning electron microscopy, Vickers test, and electrochemical techniques were used to assess the morphology, microstructure, corrosion characteristics, and microhardness of these prepared Ni-SiC nanocoatings. A backward propagation (BP) artificial neural network was utilized for predicting the corrosion mass loss and microhardness parameters of the deposited Ni-SiC nanocoatings and subsequently compared with experimental data. When the plating parameters were set at a jet rate of 5.5 m/s, a SiC concentration of 5 g/L, and a pulse density of current of 4 A/dm(2), the Ni-SiC nanocoating processed the highest microhardness value (similar to 863.4 HV). The mean grain diameters of the nanoparticles of SiC and Ni grains were 29.6 nm and 54.3 nm, respectively. In addition, when a 5.5 m/s jet rate, 5 g/L SiC concentration, and a current density of 4 A/dm(2) were used, the Ni-SiC nanocoating exhibited the least corrosion density of current equivalent to 5.3x10(-5) A/cm(2). These results showed that the Ni-SiC nanocoating had the highest value of impedance and best anti-corrosion potential. Moreover, the highest mean square errors (MEs) of microhardness and corrosion mass loss of the nanocoatings predicted by the BP model were 3.1 and 3.3%, respectively.

Automated summary

Ni-SiC nanocoatings were produced using the jet pulse electrodeposition method, and their morphology, microstructure, corrosion characteristics, and microhardness were evaluated. A BP neural network was used to predict the corrosion mass loss and microhardness of the nanocoatings, and the results were compared with experimental data. The highest microhardness and the lowest corrosion current density were achieved under specific plating parameters.