Abstract:In this work a radial basis function (RBF) neural network was used for predicting the corrosion rate in Ni-TiN composite coatings that were deposited via pulse electrode deposition onto the surface of T8 steel. The surface morphology and phase composition of the coatings before and after corrosion were investigated by atomic force microscopy (AFM), scanning electron microscopy (SEM), and X-ray diffraction (XRD). The results show that the RBF neural network had a 3×4×1 structure. The maximum and minimum errors … Show more
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