Experimental Characterization and Computer Vision-Assisted Detection of Pitting Corrosion on Stainless Steel Structural Members

Abstract

Pitting corrosion is a prevalent form of corrosive damage that can weaken, damage, and initiate failure in corrosion-resistant metallic materials. For instance, 304 stainless steel is commonly utilized in various structures (e.g., miter gates, heat exchangers, and storage tanks), but is prone to failure through pitting corrosion and stress corrosion cracking under mechanical loading, regardless of its high corrosion resistance. The pit growth typically follows a sigmoidal trend with an initial high growth rate during nucleation, followed by an eventual saturation limit, which will ultimately lead to material failure. In this study, to better understand the pitting corrosion damage development, controlled corrosion experiments were conducted to generate pits on 304 stainless steel specimens with and without mechanical loading. The pit development over time was characterized using a high-resolution laser scanner. In addition, to achieve scalable and automatic assessment of pitting corrosion conditions, a convolutional neural network-based computer vision algorithm was adopted and implemented to identify the existence of pitting damage.