Machine learning-enhanced optical monitoring for identifying pitting-susceptible zones in 316L stainless steel

Assessing the susceptibility of stainless steel (SS) to pitting corrosion remains challenging due to the difficulty in identifying nanometre-scale imperfections in the passive surface films. Traditional analytical methods are costly, time-consuming, and limited to model systems with adequate signal-to-noise ratios. We propose an alternative approach that leverages optical signatures of passive layer properties which, when enhanced with unsupervised machine learning (ML) to extract signals even at the noise level, successfully identifies pitting-susceptible zones (PSZs) in situ on industrial SS 316L substrates. Complementary optical modelling and X-ray Photoelectron Spectroscopy (XPS) reveal chromium oxide deficiency in surface films over PSZs, consistent with established pitting mechanisms. This proof of concept demonstrates that ML-enhanced optical methods can serve as accessible, precise tools for PSZ identification, advancing the development of optical corrosion monitoring systems. • Unsupervised ML enhances the optical signals of the passive layer on stainless steel. • ML-enhanced optical detection identifies pitting-susceptible zones. • Optical modelling and ex-situ XPS indicate chromium deficiency in pitting-susceptible zones.