Data-driven analysis of the local current distributions of 316L stainless steel corrosion in NaCl solution

Abstract

Abstract This investigation proposes using Scanning Electrochemical Cell Microscopy (SECCM) as a high throughput tool to collect corrosion activity datasets from randomly probed locations on electropolished 316L SS. In the presence of chloride (varying concentrations), potentiodynamic polarisation tests (varied scan rates) triggered the development of pitting corrosion. Data science methods were deployed to handle, explore, and store the 955 j Vs E curves (public datasets). Normality tests and fitting with theoretical functions were used to understand the conditional log(j) distributions at different potentials. Unimodal and uniform distributions were assigned to the passive and pitting regions. Our local strategy aligned with “big-data” analysis revealed a potential-dependent distribution of log(j), with the amount of randomness increasing with the testing aggressiveness.