Surface study of inhibitor films formed by polyvinyl alcohol and silver nanoparticles on stainless steel in hydrochloric acid solution using convolutional neural networks

Abstract The Convolutional Neural Network (CNN) approach of deep learning was successfully applied to a field of interest such as metal surface science with modified morphology via corrosion performed in the presence and absence of inhibitors. Thus, given many microscopy images, the artificial intelligence technique learns distinctive features for each class of standard/unprotected/protected surface. Specifically, the study highlights the deep learning capacity to distinguish between the surfaces of standard stainless steel and those modified by cyclic voltammetry and potentiodynamic polarization carried out in hydrochloric acid solution in the absence and presence of some corrosion inhibitors such as polyvinyl alcohol and polyvinyl alcohol with silver nanoparticles.