Jian Yao, Wanchan Yu, Juncheng Wang, Longfei Zhang, Feng Liu, Weifu Li, Liming Tan, Lan Huang, Yong Liu
{"title":"集成机器学习和热力学描述符增强镍基单晶高温合金蠕变寿命预测和合金设计","authors":"Jian Yao, Wanchan Yu, Juncheng Wang, Longfei Zhang, Feng Liu, Weifu Li, Liming Tan, Lan Huang, Yong Liu","doi":"10.1007/s12540-025-01906-x","DOIUrl":null,"url":null,"abstract":"<div><p>Ni-based single crystal superalloys play a vital role in critical areas such as aerospace and gas turbines due to their superior high-temperature strength. However, accurately predicting the creep rupture life of these alloys has been a challenge. In this study, an artificial neural network-based prediction model was developed to effectively improve the accuracy of creep life prediction for Ni-based single crystal superalloys by incorporating 15 new descriptors. The R<sup>2</sup> for the test set was 0.8595. Further, the SHAP value results guided the design of new low-cost, high-performance alloys, among which the new designed alloy (5.91 Cr, 6.21 Co, 1.62 Mo, 6.37 W, 5.64 Al, 7.22 Ta, 1.45 Re, 0.52 Ti and Ni balance, wt%) showed a higher creep life than the existing alloy CMSX-4, while having a Re content < 1.5 wt%. The results not only provide new tools for superalloy design, but also confirm the practical value of machine learning in materials science.</p><h3>Graphical Abstract</h3>\n<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":703,"journal":{"name":"Metals and Materials International","volume":"31 9","pages":"2730 - 2748"},"PeriodicalIF":4.0000,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Integrating Machine Learning and Thermodynamic Descriptors for Enhanced Ni-Based Single Crystal Superalloys Creep Life Prediction and Alloy Design\",\"authors\":\"Jian Yao, Wanchan Yu, Juncheng Wang, Longfei Zhang, Feng Liu, Weifu Li, Liming Tan, Lan Huang, Yong Liu\",\"doi\":\"10.1007/s12540-025-01906-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Ni-based single crystal superalloys play a vital role in critical areas such as aerospace and gas turbines due to their superior high-temperature strength. However, accurately predicting the creep rupture life of these alloys has been a challenge. In this study, an artificial neural network-based prediction model was developed to effectively improve the accuracy of creep life prediction for Ni-based single crystal superalloys by incorporating 15 new descriptors. The R<sup>2</sup> for the test set was 0.8595. Further, the SHAP value results guided the design of new low-cost, high-performance alloys, among which the new designed alloy (5.91 Cr, 6.21 Co, 1.62 Mo, 6.37 W, 5.64 Al, 7.22 Ta, 1.45 Re, 0.52 Ti and Ni balance, wt%) showed a higher creep life than the existing alloy CMSX-4, while having a Re content < 1.5 wt%. The results not only provide new tools for superalloy design, but also confirm the practical value of machine learning in materials science.</p><h3>Graphical Abstract</h3>\\n<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>\",\"PeriodicalId\":703,\"journal\":{\"name\":\"Metals and Materials International\",\"volume\":\"31 9\",\"pages\":\"2730 - 2748\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2025-03-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Metals and Materials International\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s12540-025-01906-x\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Metals and Materials International","FirstCategoryId":"88","ListUrlMain":"https://link.springer.com/article/10.1007/s12540-025-01906-x","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
Integrating Machine Learning and Thermodynamic Descriptors for Enhanced Ni-Based Single Crystal Superalloys Creep Life Prediction and Alloy Design
Ni-based single crystal superalloys play a vital role in critical areas such as aerospace and gas turbines due to their superior high-temperature strength. However, accurately predicting the creep rupture life of these alloys has been a challenge. In this study, an artificial neural network-based prediction model was developed to effectively improve the accuracy of creep life prediction for Ni-based single crystal superalloys by incorporating 15 new descriptors. The R2 for the test set was 0.8595. Further, the SHAP value results guided the design of new low-cost, high-performance alloys, among which the new designed alloy (5.91 Cr, 6.21 Co, 1.62 Mo, 6.37 W, 5.64 Al, 7.22 Ta, 1.45 Re, 0.52 Ti and Ni balance, wt%) showed a higher creep life than the existing alloy CMSX-4, while having a Re content < 1.5 wt%. The results not only provide new tools for superalloy design, but also confirm the practical value of machine learning in materials science.
期刊介绍：
Metals and Materials International publishes original papers and occasional critical reviews on all aspects of research and technology in materials engineering: physical metallurgy, materials science, and processing of metals and other materials. Emphasis is placed on those aspects of the science of materials that are concerned with the relationships among the processing, structure and properties (mechanical, chemical, electrical, electrochemical, magnetic and optical) of materials. Aspects of processing include the melting, casting, and fabrication with the thermodynamics, kinetics and modeling.
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