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 0.12μm. The average grain sizes of Ni and TiN on the Ni–TiN nanocoating are 62 and 30nm, 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 8g/L, current density of 5A/dm2, pulse frequency of 80Hz, 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. •We produced Ni–TiN nanocoatings on 45 steel substrates by pulse electrodeposition.•Investigation of the surface morphology of Ni–TiN coatings was conducted.•A three-layer backward propagation model predicted the hardness of Ni–TiN coatings.•The backward propagation model exhibited a maximum error of approximately 1.03%.