Machine learning in the problem of recognition of pitting corrosion on aluminum surfaces

Abstract

The work is devoted to the problem of identification and quantitative estimation of pitting corrosion. The purpose of the work is to design an algorithm and create a set of programs for evaluating and predicting corrosion processes development on the aluminum surface. Object of a research is process of corrosion of aluminum with hydrogen depolarization. Results of work can be used for a research of corrosion processes and their mechanisms on the basis of the visual analysis.

Probing the randomness of the local current distributions of 316 L stainless steel corrosion in NaCl solution

“…On the other hand, only a limited amount of works in corrosion have made use of Machine Learning (ML) approaches [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55] so far, and the major reasons for their limited application are: 1. the community traditionally relies on low-throughput means for data generation, focusing on specific input-output relationships; 2. the lack of high-quality and public available corrosion datasets readily accessible by computational tools [41,56]. This work is an effort in these directions, reporting on a high-throughput local electrochemical technique capable of gathering corrosion "big data" as well as providing structured and downloadable experimental datasets enabling future ML modelling.…”