Prediction of oxidation resistance of Ti-V-Cr burn resistant titanium alloy based on machine learning
Abstract A machine learning model was developed to predict the oxidation resistance of Ti-V-Cr burn-resistant titanium alloy, and the natural logarithm of the parabolic oxidation rate constant (lnk p ) was utilized as the model output. The results show that the two algorithms based on multiple learn...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | npj Materials Degradation |
| Online Access: | https://doi.org/10.1038/s41529-025-00553-2 |
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| author | Yuanzhi Sun Guangbao Mi Peijie Li Liangju He |
| author_facet | Yuanzhi Sun Guangbao Mi Peijie Li Liangju He |
| author_sort | Yuanzhi Sun |
| collection | DOAJ |
| description | Abstract A machine learning model was developed to predict the oxidation resistance of Ti-V-Cr burn-resistant titanium alloy, and the natural logarithm of the parabolic oxidation rate constant (lnk p ) was utilized as the model output. The results show that the two algorithms based on multiple learners, gradient boosting decision tree (GBDT) and eXtreme Gradient Boosting (XGBoost), show better performance. The coefficient of determination R 2 of the models are 0.98 and the maximum error is 6.57 and 6.40%, respectively. The importance and interpretability of the input features were analyzed. The trend of the model analysis results was the same as that of the experimental conclusions, which further revealed the mechanism of the influence of element content and temperature changes on the oxidation resistance of Ti-V-Cr alloys and verified the effectiveness of the model. This study is of great significance for the discovery, prediction, and quantification of new high-temperature oxidation-resistant Ti-V-Cr alloys. |
| format | Article |
| id | doaj-art-b93f95dad1ce451d8d88fa230e0ac6b8 |
| institution | Kabale University |
| issn | 2397-2106 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Materials Degradation |
| spelling | doaj-art-b93f95dad1ce451d8d88fa230e0ac6b82025-01-19T12:33:52ZengNature Portfolionpj Materials Degradation2397-21062025-01-019111210.1038/s41529-025-00553-2Prediction of oxidation resistance of Ti-V-Cr burn resistant titanium alloy based on machine learningYuanzhi Sun0Guangbao Mi1Peijie Li2Liangju He3National Center of Novel Materials for International Research, Tsinghua UniversityAviation Key Laboratory of Science and Technology on Advanced Titanium Alloys, AECC Beijing Institute of Aeronautical MaterialsNational Center of Novel Materials for International Research, Tsinghua UniversityNational Center of Novel Materials for International Research, Tsinghua UniversityAbstract A machine learning model was developed to predict the oxidation resistance of Ti-V-Cr burn-resistant titanium alloy, and the natural logarithm of the parabolic oxidation rate constant (lnk p ) was utilized as the model output. The results show that the two algorithms based on multiple learners, gradient boosting decision tree (GBDT) and eXtreme Gradient Boosting (XGBoost), show better performance. The coefficient of determination R 2 of the models are 0.98 and the maximum error is 6.57 and 6.40%, respectively. The importance and interpretability of the input features were analyzed. The trend of the model analysis results was the same as that of the experimental conclusions, which further revealed the mechanism of the influence of element content and temperature changes on the oxidation resistance of Ti-V-Cr alloys and verified the effectiveness of the model. This study is of great significance for the discovery, prediction, and quantification of new high-temperature oxidation-resistant Ti-V-Cr alloys.https://doi.org/10.1038/s41529-025-00553-2 |
| spellingShingle | Yuanzhi Sun Guangbao Mi Peijie Li Liangju He Prediction of oxidation resistance of Ti-V-Cr burn resistant titanium alloy based on machine learning npj Materials Degradation |
| title | Prediction of oxidation resistance of Ti-V-Cr burn resistant titanium alloy based on machine learning |
| title_full | Prediction of oxidation resistance of Ti-V-Cr burn resistant titanium alloy based on machine learning |
| title_fullStr | Prediction of oxidation resistance of Ti-V-Cr burn resistant titanium alloy based on machine learning |
| title_full_unstemmed | Prediction of oxidation resistance of Ti-V-Cr burn resistant titanium alloy based on machine learning |
| title_short | Prediction of oxidation resistance of Ti-V-Cr burn resistant titanium alloy based on machine learning |
| title_sort | prediction of oxidation resistance of ti v cr burn resistant titanium alloy based on machine learning |
| url | https://doi.org/10.1038/s41529-025-00553-2 |
| work_keys_str_mv | AT yuanzhisun predictionofoxidationresistanceoftivcrburnresistanttitaniumalloybasedonmachinelearning AT guangbaomi predictionofoxidationresistanceoftivcrburnresistanttitaniumalloybasedonmachinelearning AT peijieli predictionofoxidationresistanceoftivcrburnresistanttitaniumalloybasedonmachinelearning AT liangjuhe predictionofoxidationresistanceoftivcrburnresistanttitaniumalloybasedonmachinelearning |