Prediction of coating degradation based on “Environmental Factors–Physical Property–Corrosion Failure” two-stage machine learning
Abstract The corrosion failure prediction of coating materials in diverse environments is of great significance for service performance evaluation. This work proposes a two-stage machine learning method that makes use of various data, including environmental factors, physical properties, and coating...
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | npj Materials Degradation |
| Online Access: | https://doi.org/10.1038/s41529-025-00614-6 |
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| _version_ | 1850102482430066688 |
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| author | Weiting Chen Lingwei Ma Yiran Li Dequan Wu Kun Zhou Jinke Wang Zhibin Chen Xin Guo Zongbao Li Thee Chowwanonthapunya Xiaogang Li Dawei Zhang |
| author_facet | Weiting Chen Lingwei Ma Yiran Li Dequan Wu Kun Zhou Jinke Wang Zhibin Chen Xin Guo Zongbao Li Thee Chowwanonthapunya Xiaogang Li Dawei Zhang |
| author_sort | Weiting Chen |
| collection | DOAJ |
| description | Abstract The corrosion failure prediction of coating materials in diverse environments is of great significance for service performance evaluation. This work proposes a two-stage machine learning method that makes use of various data, including environmental factors, physical properties, and coating barrier performance, to accurately evaluate the corrosion degradation of coatings. In the first stage, a 1-year outdoor exposure experiment of polyurethane coatings was conducted in nine representative climatic environments. A semi-supervised collaborative training regression model is established between key environmental data and physical properties of coatings (i.e., glossiness, adhesion, water contact angle, and yellowness). In the second stage, using the predicted physical property data as inputs, a machine learning model is constructed that links physical properties to the barrier performance of coatings and develops binary classification models that can distinguish between intact and damaged coatings. This two-stage modeling strategy provides enhanced prediction accuracy and scientific interpretability by incorporating intermediate physical property parameters. |
| format | Article |
| id | doaj-art-30f66b6098d248f58c111ee305f2a4db |
| institution | DOAJ |
| issn | 2397-2106 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Materials Degradation |
| spelling | doaj-art-30f66b6098d248f58c111ee305f2a4db2025-08-20T02:39:44ZengNature Portfolionpj Materials Degradation2397-21062025-06-019111310.1038/s41529-025-00614-6Prediction of coating degradation based on “Environmental Factors–Physical Property–Corrosion Failure” two-stage machine learningWeiting Chen0Lingwei Ma1Yiran Li2Dequan Wu3Kun Zhou4Jinke Wang5Zhibin Chen6Xin Guo7Zongbao Li8Thee Chowwanonthapunya9Xiaogang Li10Dawei Zhang11Beijing Advanced Innovation Center for Materials Genome Engineering, School of Advanced Materials Innovation, University of Science and Technology BeijingBeijing Advanced Innovation Center for Materials Genome Engineering, School of Advanced Materials Innovation, University of Science and Technology BeijingBeijing Advanced Innovation Center for Materials Genome Engineering, School of Advanced Materials Innovation, University of Science and Technology BeijingSouthwest Institute of Technology and EngineeringSouthwest Institute of Technology and EngineeringBeijing Advanced Innovation Center for Materials Genome Engineering, School of Advanced Materials Innovation, University of Science and Technology BeijingBeijing Advanced Innovation Center for Materials Genome Engineering, School of Advanced Materials Innovation, University of Science and Technology BeijingBeijing Advanced Innovation Center for Materials Genome Engineering, School of Advanced Materials Innovation, University of Science and Technology BeijingBeijing Advanced Innovation Center for Materials Genome Engineering, School of Advanced Materials Innovation, University of Science and Technology BeijingFaculty of International Maritime Studies, Kasetsart University, Sriracha Campus, 199 Tungsukla, SrirachaBeijing Advanced Innovation Center for Materials Genome Engineering, School of Advanced Materials Innovation, University of Science and Technology BeijingBeijing Advanced Innovation Center for Materials Genome Engineering, School of Advanced Materials Innovation, University of Science and Technology BeijingAbstract The corrosion failure prediction of coating materials in diverse environments is of great significance for service performance evaluation. This work proposes a two-stage machine learning method that makes use of various data, including environmental factors, physical properties, and coating barrier performance, to accurately evaluate the corrosion degradation of coatings. In the first stage, a 1-year outdoor exposure experiment of polyurethane coatings was conducted in nine representative climatic environments. A semi-supervised collaborative training regression model is established between key environmental data and physical properties of coatings (i.e., glossiness, adhesion, water contact angle, and yellowness). In the second stage, using the predicted physical property data as inputs, a machine learning model is constructed that links physical properties to the barrier performance of coatings and develops binary classification models that can distinguish between intact and damaged coatings. This two-stage modeling strategy provides enhanced prediction accuracy and scientific interpretability by incorporating intermediate physical property parameters.https://doi.org/10.1038/s41529-025-00614-6 |
| spellingShingle | Weiting Chen Lingwei Ma Yiran Li Dequan Wu Kun Zhou Jinke Wang Zhibin Chen Xin Guo Zongbao Li Thee Chowwanonthapunya Xiaogang Li Dawei Zhang Prediction of coating degradation based on “Environmental Factors–Physical Property–Corrosion Failure” two-stage machine learning npj Materials Degradation |
| title | Prediction of coating degradation based on “Environmental Factors–Physical Property–Corrosion Failure” two-stage machine learning |
| title_full | Prediction of coating degradation based on “Environmental Factors–Physical Property–Corrosion Failure” two-stage machine learning |
| title_fullStr | Prediction of coating degradation based on “Environmental Factors–Physical Property–Corrosion Failure” two-stage machine learning |
| title_full_unstemmed | Prediction of coating degradation based on “Environmental Factors–Physical Property–Corrosion Failure” two-stage machine learning |
| title_short | Prediction of coating degradation based on “Environmental Factors–Physical Property–Corrosion Failure” two-stage machine learning |
| title_sort | prediction of coating degradation based on environmental factors physical property corrosion failure two stage machine learning |
| url | https://doi.org/10.1038/s41529-025-00614-6 |
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