Towards understanding and prediction of corrosion degradation of organic coatings under tropical marine atmospheric environment via a data-driven approach  

作　　者：Shaopeng Liu Lingwei Ma Jinke Wang Yiran Li Haiyan Gong Haitao Ren Xiaogang Li Dawei Zhang 

机构地区：[1]Beijing Advanced Innovation Center for Materials Genome Engineering,Institute for Advanced Materials and Technology,University of Science and Technology Beijing,Beijing 100083,China [2]National Materials Corrosion and Protection Data Center,University of Science and Technology Beijing,Beijing 100083,China [3]Institute of Materials Intelligent Technology,Liaoning Academy of Materials,Shenyang 110004,China [4]Luoyang Ship Material Research Institute,Sanya 572032,China

出　　处：《International Journal of Minerals,Metallurgy and Materials》2025年第5期1151-1161,共11页矿物冶金与材料学报(英文版)

基　　金：supported by the National Key R&D Program of China(No.2022YFB3808803);the National Natural Science Foundation of China(No.52371049);the National Science and Technology Resources Investigation Program of China(No.2021FY100603).

摘　　要：The corrosion degradation of organic coatings in tropical marine atmospheric environments results in substantial economic losses across various industries.The complexity of a dynamic environment,combined with high costs,extended experimental periods,and limited data,places a limit on the comprehension of this process.This study addresses this challenge by investigating the corrosion de-gradation of damaged organic coatings in a tropical marine environment using an atmospheric corrosion monitoring sensor and a random forest(RF)model.For damage simulation,a polyurethane coating applied to a Fe/graphite corrosion sensor was intentionally scratched and exposed to the marine atmosphere for over one year.Pearson correlation analysis was performed for the collection and filtering of en-vironmental and corrosion current data.According to the RF model,the following specific conditions contributed to accelerated degrada-tion:relative humidity(RH)above 80%and temperatures below 22.5℃,with the risk increasing significantly when RH exceeded 90%.High RH and temperature exhibited a cumulative effect on coating degradation.A high risk of corrosion occurred in the nighttime.The RF model was also used to predict the coating degradation process using environmental data as input parameters,with the accuracy show-ing improvement when the duration of influential environmental ranges was considered.

关 键 词：organic coating degradation atmospheric corrosion machine learning exposure test random forest coating sensor 

分 类 号：TG174.4[金属学及工艺—金属表面处理]