Application of artificial neural networks to predict the hardness of Ni-TiN nanocoatings fabricated by pulse electrodeposition

A three-layer backward propagation (BP) model was used to predict the hardness of Ni-TiN nanocoatings fabricated by pulse electrodeposition. The effect of plating parameters, namely, TiN particle concentration, current density, pulse frequency, and duty ratio on the hardness of Ni-TiN nanocoatings was investigated. The morphology, structure, and hardness of Ni-TiN nanocoatings were verified using scanning electron microscopy, white-light interfering profilometry, high-resolution transmission emission microscopy, and Rockwell hardness testing. The results indicated that the surface roughness of the Ni-TiN nanocoating is approximately 0.12 μm. The average grain sizes of Ni and TiN on the Ni-TiN nanocoating are 62 and 30 nm, respectively. The optimum conditions for fabricating Ni-TiN nanocoatings based on the greatest hardness of Ni-TiN deposits are as follows: TiN particle concentration of 8 g/L, current density of 5 A/dm2, pulse frequency of 80 Hz, and duty ratio of 0.7. We conclude that the BP model, with a maximum error of approximately 1.03%, can effectively predict the hardness of Ni-TiN nanocoatings.