Improvement of the machine learning-based corrosion rate prediction model through the optimization of input features

Abstract

The corrosion resistance of low-alloy steel seriously influences its performance, particularly as a class of materials widely used in marine environments. In this study, we collected the marine corrosion data of low-alloy steels and established corrosion rate prediction models with machine learning algorithms. Both the chemical composition of low-alloy steel and environmental factors were used as input features, and the random forest algorithm was selected as the modeling algorithm. Feature reduction methods, including the gradient boosting decision tree, Kendall correlation analysis and principal component analysis, were first applied to select the dominating factors on the corrosion rate. Then, we proposed two feature creation methods to convert the chemical composition features into a set of atomic and physical property features. As a result, the feature creation method crafted a model no longer limited to materials with specific chemical compositions. The machine learning-based corrosion rate prediction model also showed good prediction accuracy of the corrosion rate. This study improved the generalization ability of the corrosion rate prediction model and proved the feasibility of machine learning in corrosion resistance evaluation.

Keywords

• Low-alloy steel
• Feature engineering
• Machine learning
• Random forest
• Marine corrosion