Convolutional neural network for automated quantitative analysis of non-destructively acquired three-dimensional corrosion pit morphology data

We have trained an autoencoder-decoder type convolutional neural network for automated quantitative analysis of three-dimensional corrosion-pit morphology data. We have used pit morphology data, acquired non-destructively using scanning white light interferometry from AISI 304 stainless steel specimens subjected to different surface modification treatments, followed by potentiodynamic polarization corrosion testing . The autoencoder was designed to compress the high-dimensional input pit morphology data into a latent space of two variables, from which the decoder produces reconstructions of the input data. Analysis of the compressed data in the latent space led to the identification of a unique vector which encompassed most of the data points. Application of the model to artificially constructed semi-ellipsoidal pits with independently quantifiable morphological characteristics showed that the vector was analogous to ‘effective sharpness’ of pits, helping define a ‘sharpness/bluntness’ metric for arbitrarily shaped corrosion pits, that can be linked to their propensity to transition into stress corrosion cracks.