Application of artificial neural networks to predict sliding wear resistance of Ni–TiN nanocomposite coatings deposited by pulse electrodeposition

Abstract

Ni–TiN composite coatings were prepared on 45 steel substrates by pulse electrodeposition. The effect of plating parameters on the sliding wear resistance of the Ni–TiN nanocomposite coatings was investigated using transmission electron microscopy (TEM), scanning electron microscopy (SEM), and X-ray diffraction (XRD). The sliding wear resistance of the Ni–TiN coatings was modeled using artificial neural networks (ANNs). TiN grains in Ni–TiN coatings were large when the average current density was relatively low and the pulse interval was long. At a given wear distance, with increasing TiN concentration in the bath, the wear loss of the coating initially decreased and subsequently increased. The average crystallite sizes for Ni and TiN in Ni–TiN coating were approximately 58 and 39nm, respectively. The ANN model, which showed an error of approximately 4.2%, can effectively predict sliding wear resistance of Ni–TiN nanocomposite coatings.