Forecast the Microhardness of Ni-TiN Nanoplatings via an Artificial Neural Network Model

Abstract

This study used a backward propagation (BP) model to estimate the microhardness of Ni-TiN nanoplatings prepared using pulse electrodeposition. The influence of electroplating parameters on the microhardness of Ni-TiN nanoplatings was discussed. These parameters included the concentration of the TiN particle, pulse frequency, duty cycle, and current density. The surface morphology, microstructure, and microhardness of Ni-TiN nanoplatings were examined using white-light interfering profilometry, scanning electron microscopy, Rockwell hardness testing, and high-resolution transmission emission microscopy. The Ni-TiN thin film prepared by pulse electrodeposition had a surface roughness of about 0.122 µm, and the average size of the Ni and TiN grains on this film was 61.8 and 31.3 nm, respectively. The optimal process parameters were determined based on the maximum microhardness of the deposited Ni-TiN nanoplatings, which included an 8 g/L TiN particle concentration, a 5 A/dm2 current density, an 80 Hz pulse frequency, and a 0.7 duty cycle. It could be concluded that the BP model would accurately forecast the microhardness of Ni-TiN nanoplatings, with a maximal error of about 1.04%.