Prediction model for corrosion rate of low-alloy steels under atmospheric conditions using machine learning algorithms

作     者：Jingou Kuang Zhilin Long Jingou Kuang;Zhilin Long

作者机构：School of Civil EngineeringXiangtan UniversityXiangtan 411105China 

出 版 物：《International Journal of Minerals,Metallurgy and Materials》 (矿物冶金与材料学报（英文版）)

年 卷 期：2024年第31卷第2期

页      面：337-350页

核心收录：

学科分类：12[管理学] 080503[工学-材料加工工程] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：the National Key R&D Program of China(No.2021YFB3701705) 

主　　题：machine learning low-alloy steel atmospheric corrosion prediction corrosion rate feature fusion 

摘      要：This work constructed a machine learning(ML)model to predict the atmospheric corrosion rate of low-alloy steels(LAS).The material properties of LAS,environmental factors,and exposure time were used as the input,while the corrosion rate as the output.6 dif-ferent ML algorithms were used to construct the proposed *** optimization and filtering,the eXtreme gradient boosting(XG-Boost)model exhibited good corrosion rate prediction *** features of material properties were then transformed into atomic and physical features using the proposed property transformation approach,and the dominant descriptors that affected the corrosion rate were filtered using the recursive feature elimination(RFE)as well as XGBoost *** established ML models exhibited better predic-tion performance and generalization ability via property transformation *** addition,the SHapley additive exPlanations(SHAP)method was applied to analyze the relationship between the descriptors and corrosion *** results showed that the property transformation model could effectively help with analyzing the corrosion behavior,thereby significantly improving the generalization ability of corrosion rate prediction models.