Corrosion rate prediction and influencing factors evaluation of low-alloy steels in marine atmosphere using machine learning approach

Luchun Yan; Yupeng Diao; Zhaoyang Lang; Kewei Gao

doi:10.1080/14686996.2020.1746196

Corrosion rate prediction and influencing factors evaluation of low-alloy steels in marine atmosphere using machine learning approach

Sci Technol Adv Mater. 2020 Jun 19;21(1):359-370. doi: 10.1080/14686996.2020.1746196.

Authors

Luchun Yan¹, Yupeng Diao¹, Zhaoyang Lang¹, Kewei Gao^{1

2}

Affiliations

¹ School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing, China.
² Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, China.

Abstract

The empirical modeling methods are widely used in corrosion behavior analysis. But due to the limited regression ability of conventional algorithms, modeling objects are often limited to individual factors and specific environments. This study proposed a modeling method based on machine learning to simulate the marine atmospheric corrosion behavior of low-alloy steels. The correlations between material, environmental factors and corrosion rate were evaluated, and their influences on the corrosion behavior of steels were analyzed intuitively. By using the selected dominating factors as input variables, an optimized random forest model was established with a high prediction accuracy of corrosion rate (R² values, 0.94 and 0.73 to the training set and testing set) to different low-alloy steel samples in several typical marine atmospheric environments. The results demonstrated that machine learning was efficient in corrosion behavior analysis, which usually involves a regression analysis of multiple factors.

Keywords: 106 Metallic materials; 212 Surface and interfaces; 404 Materials informatics / Genomics; Atmospheric corrosion; corrosion; data mining; materials informatics; random forest; regression analysis.

Grants and funding

This project is supported by the National Key R&D Program of China (2017YFB0702100), and the National Natural Science Foundation of China (No. 51601014; No. 51771026).