Composition Analysis of Corrosion Products on Steel by Machine Learning of Optical Microscopic Images

Abstract

Analysis of the corrosion distribution and composition of corrosion products on steel surfaces using supervised machine learning of optical microscopic images was investigated. The accuracy of the artificial intelligence in evaluating the composition of iron compound reference samples was affected by the illumination intensity and surface roughness during image capture. The evaluation accuracy was high for compounds with a wide distribution of R value such as Fe₂O₃ and FeOOH, but low for compounds with a narrow distribution such as Fe₃O₄. The results of wet-dry cycling tests on weathering steel with NaCl particles on the surface showed that the transition of corrosion products during the corrosion progress can be analyzed from optical microscope images.