Model construction of corrosion resistance of alloying elements for low alloy steel in marine atmospheric corrosive environment based on machine learning
Author:
Wang Fulong1, Liu Wei1, Sun Yipu1, Zhang Bo1, Li Hai1, Chen Longjun1, Hou Bowen1, Zhang Haoyu1
Affiliation:
1. 12507 Corrosion and Protection Center, Institute for Advanced Materials and Technology, University of Science and Technology Beijing , Beijing 100083 , China
Abstract
Abstract
The study focused on constructing a machine learning model, considering the interaction of alloying elements on corrosion resistance of low alloy steels in the marine atmospheric environment. Spearman’s analysis was applied, and the relationship between alloying element and corrosion rate was evaluated based on random forest (RF) importance and Shapley additive explanation (SHAP) analysis. The prediction performance of the six models (RF, multilayer perceptron (MLP), ridge regression (RR), K-nearest neighbor regression (KNN), logistic regression (LR), and support vector machine (SVM) was compared by using the preferred dominant elements as input variables. Afterwards, a high-precision corrosion rate prediction model based on RF was constructed. Finally, the generalizability of the model was demonstrated using 10 lines of steel corrosion data from several new marine atmospheric environments.
Funder
National Key R&D Program of China National Natural Science Foundation of China
Publisher
Walter de Gruyter GmbH
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