Electrochemical and Mechanical Properties of Ni/g-C3N4 ‎Nanocomposite Coatings with Enhanced Corrosion Protective ‎Properties: A Case Study for Modeling the Corrosion Resistance ‎by ANN and ANFIS Models

This work investigates the effect of electrolysis bath parameters on the corrosion, micro-hardness, and wear behavior of Ni coatings. The characterization of synthesized Graphitic carbon nitride (g-C3N4) was done by Fourier transform infrared, Raman spectroscopy, and transmission electron microscope. The surface morphology of coated samples with various amounts of current density was studied by scanning electron microscopy and energy-dispersive X-ray spectroscopy. The corrosion prevention effect of Ni/g-C3N4 nanocomposite coatings was investigated by EIS and polarization techniques. The experimental outcome demonstrates that an electrolysis bath of 0.3 g/L g-C3N4 and 0.1 A.cm-2 presents a Ni coating with the highest corrosion protection, wear resistance, and microhardness. The corrosion current densities of Ni/g-C3N4 coatings obtained by electrochemical tests were used for training two machine learning techniques (Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS)) based on current density, g-C3N4 concentration, and plating time as an input. Various statistical criteria showed that the ANFIS model (R2= 0.99) could forecast corrosion current density more accurately than ANN with R2= 0.91. Finally, due to the robust performance of ANFIS in modeling the corrosion behavior of Ni/g-C3N4 nanocomposite coating, the effect of each parameter was studied.