To maintain the safety of social infrastructures, it is crucial to evaluate the corrosion state of materials and predict their lifespan. We aimed to make it easier by applying machine learning to visible light images of materials. In the present report, machine learning was applied to images of carbon steel specimens prepared by salt spray, to classify the corrosion products on the specimen surface and to predict their change over time. It was shown that the images could be reconstructed into label maps corresponding to the corrosion products on the surface by unsupervised learning on the pixel colour values, and that changes of the label distributions over time could also be predicted almost accurately by supervised learning. The results demonstrated the possibility of an easy and accurate method to evaluate and predict corrosion state of infrastructures using the combination of machine learning and visible light images taken by ordinary cameras.

Type 430 stainless steel showed different corrosion behavior from Type 304 in a non-aqueos electrolyte containing LiPF₆ in terms of potential dependency. At 4 V （Li/Li^＋）, Type 430 was highly susceptible to corrosion while Type 304 was resistant. At 5 V （Li/Li^＋）, Type 430 was passivated as well as Type 304. X-ray photoelectron spectroscopy （XPS） analysis revealed that Type 430 was protected by two-layer of passivation film at 5 V （Li/Li^＋）. The outer layer consited of Fe-fluoride （FeF₃） and smaller amount of oxide of Fe and Cr. It was suggested that FeF₃ played an essential role in the passivation of Type 430 at 5 V （Li/Li^＋）. The higher corrosion susceptiblity of Type 430 at 4 V （Li/Li^＋） was ascribed to the difficulty of forming of FeF₃, and the lower corrosion resistance of the pristine film as compared with Type 304.