Data and Knowledge Dual-Driven Creep Life Prediction for Austenitic Heat-Resistance Steel
Traditional creep life prediction methods are generally difficult for researchers to fully consider the key factors affecting the creep performance, which limits their application in the research and development of new alloys. The artificial intelligence method can skip the complex mechanism and dir...
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| Format: | Article |
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MDPI AG
2025-01-01
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| Series: | Metals |
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| Online Access: | https://www.mdpi.com/2075-4701/15/2/120 |
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| author | Xiaochang Xie Mutong Liu Ping Yang Zenan Yang Chengbo Pan Chenchong Wang Xiaolu Wei |
| author_facet | Xiaochang Xie Mutong Liu Ping Yang Zenan Yang Chengbo Pan Chenchong Wang Xiaolu Wei |
| author_sort | Xiaochang Xie |
| collection | DOAJ |
| description | Traditional creep life prediction methods are generally difficult for researchers to fully consider the key factors affecting the creep performance, which limits their application in the research and development of new alloys. The artificial intelligence method can skip the complex mechanism and directly establish the mathematical correlation between the composition/process and the target performance. The accuracy, universality, and development efficiency of the model are better than the traditional material development strategy. In this study, we collected 216 creep data of austenitic heat-resistant steel, selected a variety of different machine learning algorithms to establish creep life prediction models, calculated and introduced a large amount of physical metallurgy knowledge highly related to creep based on Thermo-Calc, and converted the creep life into the form of the Larson–Miller parameter to optimize the data distribution, which effectively improved the prediction accuracy and interpretability of the model. In addition, the optimal model was combined with a genetic algorithm to obtain the best composition and process scheme with high-creep-performance potential, providing guidance for the design of austenitic heat-resistant steel. |
| format | Article |
| id | doaj-art-24c2bf8871b2407db2f47e3e77daa707 |
| institution | DOAJ |
| issn | 2075-4701 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metals |
| spelling | doaj-art-24c2bf8871b2407db2f47e3e77daa7072025-08-20T02:44:39ZengMDPI AGMetals2075-47012025-01-0115212010.3390/met15020120Data and Knowledge Dual-Driven Creep Life Prediction for Austenitic Heat-Resistance SteelXiaochang Xie0Mutong Liu1Ping Yang2Zenan Yang3Chengbo Pan4Chenchong Wang5Xiaolu Wei6Department of Steel and Rare-Noble Metals, AECC Beijing Institute of Aeronautical Materials, Beijing 100095, ChinaDepartment of Steel and Rare-Noble Metals, AECC Beijing Institute of Aeronautical Materials, Beijing 100095, ChinaDepartment of Steel and Rare-Noble Metals, AECC Beijing Institute of Aeronautical Materials, Beijing 100095, ChinaDepartment of Steel and Rare-Noble Metals, AECC Beijing Institute of Aeronautical Materials, Beijing 100095, ChinaState Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, ChinaState Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, ChinaState Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, ChinaTraditional creep life prediction methods are generally difficult for researchers to fully consider the key factors affecting the creep performance, which limits their application in the research and development of new alloys. The artificial intelligence method can skip the complex mechanism and directly establish the mathematical correlation between the composition/process and the target performance. The accuracy, universality, and development efficiency of the model are better than the traditional material development strategy. In this study, we collected 216 creep data of austenitic heat-resistant steel, selected a variety of different machine learning algorithms to establish creep life prediction models, calculated and introduced a large amount of physical metallurgy knowledge highly related to creep based on Thermo-Calc, and converted the creep life into the form of the Larson–Miller parameter to optimize the data distribution, which effectively improved the prediction accuracy and interpretability of the model. In addition, the optimal model was combined with a genetic algorithm to obtain the best composition and process scheme with high-creep-performance potential, providing guidance for the design of austenitic heat-resistant steel.https://www.mdpi.com/2075-4701/15/2/120austenitic heat-resistant steelcreep lifeexpert knowledgemachine learningalloy design |
| spellingShingle | Xiaochang Xie Mutong Liu Ping Yang Zenan Yang Chengbo Pan Chenchong Wang Xiaolu Wei Data and Knowledge Dual-Driven Creep Life Prediction for Austenitic Heat-Resistance Steel Metals austenitic heat-resistant steel creep life expert knowledge machine learning alloy design |
| title | Data and Knowledge Dual-Driven Creep Life Prediction for Austenitic Heat-Resistance Steel |
| title_full | Data and Knowledge Dual-Driven Creep Life Prediction for Austenitic Heat-Resistance Steel |
| title_fullStr | Data and Knowledge Dual-Driven Creep Life Prediction for Austenitic Heat-Resistance Steel |
| title_full_unstemmed | Data and Knowledge Dual-Driven Creep Life Prediction for Austenitic Heat-Resistance Steel |
| title_short | Data and Knowledge Dual-Driven Creep Life Prediction for Austenitic Heat-Resistance Steel |
| title_sort | data and knowledge dual driven creep life prediction for austenitic heat resistance steel |
| topic | austenitic heat-resistant steel creep life expert knowledge machine learning alloy design |
| url | https://www.mdpi.com/2075-4701/15/2/120 |
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