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A Machine Learning Method for Predicting Corrosion Weight Gain of Uranium and Uranium Alloys
As an irreplaceable structural and functional material in strategic equipment, uranium and uranium alloys are generally susceptible to corrosion reactions during service, and predicting corrosion behavior has important research significance. There have been substantial studies conducted on metal cor...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9867464/ https://www.ncbi.nlm.nih.gov/pubmed/36676368 http://dx.doi.org/10.3390/ma16020631 |
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author | Wang, Xiaoyuan Zhang, Wanying Zhang, Weidong Ai, Yibo |
author_facet | Wang, Xiaoyuan Zhang, Wanying Zhang, Weidong Ai, Yibo |
author_sort | Wang, Xiaoyuan |
collection | PubMed |
description | As an irreplaceable structural and functional material in strategic equipment, uranium and uranium alloys are generally susceptible to corrosion reactions during service, and predicting corrosion behavior has important research significance. There have been substantial studies conducted on metal corrosion research. Accelerated experiments can shorten the test time, but there are still differences in real corrosion processes. Numerical simulation methods can avoid radioactive experiments, but it is difficult to fully simulate a real corrosion environment. The modeling of real corrosion data using machine learning methods allows for effective corrosion prediction. This research used machine learning methods to study the corrosion of uranium and uranium alloys in air and established a corrosion weight gain prediction model. Eleven classic machine learning algorithms for regression were compared and a ten-fold cross validation method was used to choose the highest accuracy algorithm, which was the extra trees algorithm. Feature selection methods, including the extra trees and Pearson correlation analysis methods, were used to select the most important four factors in corrosion weight gain. As a result, the prediction accuracy of the corrosion weight gain prediction model was 96.8%, which could determine a good prediction of corrosion for uranium and uranium alloys. |
format | Online Article Text |
id | pubmed-9867464 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98674642023-01-22 A Machine Learning Method for Predicting Corrosion Weight Gain of Uranium and Uranium Alloys Wang, Xiaoyuan Zhang, Wanying Zhang, Weidong Ai, Yibo Materials (Basel) Article As an irreplaceable structural and functional material in strategic equipment, uranium and uranium alloys are generally susceptible to corrosion reactions during service, and predicting corrosion behavior has important research significance. There have been substantial studies conducted on metal corrosion research. Accelerated experiments can shorten the test time, but there are still differences in real corrosion processes. Numerical simulation methods can avoid radioactive experiments, but it is difficult to fully simulate a real corrosion environment. The modeling of real corrosion data using machine learning methods allows for effective corrosion prediction. This research used machine learning methods to study the corrosion of uranium and uranium alloys in air and established a corrosion weight gain prediction model. Eleven classic machine learning algorithms for regression were compared and a ten-fold cross validation method was used to choose the highest accuracy algorithm, which was the extra trees algorithm. Feature selection methods, including the extra trees and Pearson correlation analysis methods, were used to select the most important four factors in corrosion weight gain. As a result, the prediction accuracy of the corrosion weight gain prediction model was 96.8%, which could determine a good prediction of corrosion for uranium and uranium alloys. MDPI 2023-01-09 /pmc/articles/PMC9867464/ /pubmed/36676368 http://dx.doi.org/10.3390/ma16020631 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wang, Xiaoyuan Zhang, Wanying Zhang, Weidong Ai, Yibo A Machine Learning Method for Predicting Corrosion Weight Gain of Uranium and Uranium Alloys |
title | A Machine Learning Method for Predicting Corrosion Weight Gain of Uranium and Uranium Alloys |
title_full | A Machine Learning Method for Predicting Corrosion Weight Gain of Uranium and Uranium Alloys |
title_fullStr | A Machine Learning Method for Predicting Corrosion Weight Gain of Uranium and Uranium Alloys |
title_full_unstemmed | A Machine Learning Method for Predicting Corrosion Weight Gain of Uranium and Uranium Alloys |
title_short | A Machine Learning Method for Predicting Corrosion Weight Gain of Uranium and Uranium Alloys |
title_sort | machine learning method for predicting corrosion weight gain of uranium and uranium alloys |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9867464/ https://www.ncbi.nlm.nih.gov/pubmed/36676368 http://dx.doi.org/10.3390/ma16020631 |
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