Application of artificial neural networks to predict corrosion behavior of Ni–SiC composite coatings deposited by ultrasonic electrodeposition

Abstract

A feed-forward, multilayer perceptron artificial neural network (ANN) model with eight hidden layers and 12 neurons was used to predict the corrosion behavior of Ni–SiC composite coatings deposited by ultrasonic electrodeposition. The effect of process parameters, namely, ultrasonic power, SiC particle concentration, and current density, on the weight losses of Ni–SiC composite coatings was investigated. The grain sizes of Ni and SiC were determined by using X-ray diffraction (XRD) and scanning probe microscopy (SPM). Results indicate that ultrasonic power, SiC particle concentration, and current density have significant effects on the weight losses of Ni–SiC composite coatings. The ANN model, which has a mean square error of approximately 3.35%, can effectively predict the corrosion behavior of Ni–SiC composite coatings. The following optimum conditions for depositing Ni–SiC composite coatings were determined on the basis of the lowest weight loss of Ni–SiC deposits: ultrasonic power of 250W, SiC particle concentration of 8g/l, and current density of 4A/dm2. XRD and SPM results demonstrate that the average grain sizes of Ni and SiC in the Ni–SiC composite coating are 90 and 70nm, respectively.