Zhiyuan Liu, Tianyou Wang, Li Jin, Jian Zeng, Shuai Dong, Fenghua Wang, Fulin Wang, Jie Dong
{"title":"实现高刚度和延展性--通过机器学习设计 Mg-Al-Y 合金","authors":"Zhiyuan Liu, Tianyou Wang, Li Jin, Jian Zeng, Shuai Dong, Fenghua Wang, Fulin Wang, Jie Dong","doi":"10.1016/j.jmst.2024.09.038","DOIUrl":null,"url":null,"abstract":"In traditional trial-and-error method, enhancing the Young's modulus of magnesium alloys while maintaining a favorable ductility has consistently been a challenge. It is a need to explore more efficient and expedited methods to design magnesium alloys with high modulus and ductility. In this study, machine learning (ML) and assisted microstructure control methods are used to design high modulus magnesium alloys. Six key features that influence stiffness and ductility have been extracted in this ML model based on abundant data from literature sources. As a result, predictive models for Young's modulus and elongation are established, with errors less than 2.4% and 4.5% through XGBoost machine learning model, respectively. Within the given range of six features, the magnesium alloys can be fabricated with the Young's modulus exceeding 50 GPa and an elongation surpassing 6%. As a validation, Mg-Al-Y alloys were experimentally prepared to meet the criteria of six features, achieving Young's modulus of 51.5 GPa, and the elongation of 7%. Moreover, the SHapley Additive exPlanation (SHAP) is introduced to boost the model interpretability. This indicates that balancing the volume fraction of reinforcement, the most important feature, is key to achieve Mg-Al-Y alloys with high Young's modulus and favorable elongation through the two models. Enhancing reinforcement dispersion and reducing the size of reinforcement and grain can further improve the elongation of high-stiffness Mg alloy.","PeriodicalId":16154,"journal":{"name":"Journal of Materials Science & Technology","volume":null,"pages":null},"PeriodicalIF":11.2000,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Towards high stiffness and ductility-The Mg-Al-Y alloy design through machine learning\",\"authors\":\"Zhiyuan Liu, Tianyou Wang, Li Jin, Jian Zeng, Shuai Dong, Fenghua Wang, Fulin Wang, Jie Dong\",\"doi\":\"10.1016/j.jmst.2024.09.038\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In traditional trial-and-error method, enhancing the Young's modulus of magnesium alloys while maintaining a favorable ductility has consistently been a challenge. It is a need to explore more efficient and expedited methods to design magnesium alloys with high modulus and ductility. In this study, machine learning (ML) and assisted microstructure control methods are used to design high modulus magnesium alloys. Six key features that influence stiffness and ductility have been extracted in this ML model based on abundant data from literature sources. As a result, predictive models for Young's modulus and elongation are established, with errors less than 2.4% and 4.5% through XGBoost machine learning model, respectively. Within the given range of six features, the magnesium alloys can be fabricated with the Young's modulus exceeding 50 GPa and an elongation surpassing 6%. As a validation, Mg-Al-Y alloys were experimentally prepared to meet the criteria of six features, achieving Young's modulus of 51.5 GPa, and the elongation of 7%. Moreover, the SHapley Additive exPlanation (SHAP) is introduced to boost the model interpretability. This indicates that balancing the volume fraction of reinforcement, the most important feature, is key to achieve Mg-Al-Y alloys with high Young's modulus and favorable elongation through the two models. Enhancing reinforcement dispersion and reducing the size of reinforcement and grain can further improve the elongation of high-stiffness Mg alloy.\",\"PeriodicalId\":16154,\"journal\":{\"name\":\"Journal of Materials Science & Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":11.2000,\"publicationDate\":\"2024-10-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Materials Science & Technology\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.1016/j.jmst.2024.09.038\",\"RegionNum\":1,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Materials Science & Technology","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1016/j.jmst.2024.09.038","RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
Towards high stiffness and ductility-The Mg-Al-Y alloy design through machine learning
In traditional trial-and-error method, enhancing the Young's modulus of magnesium alloys while maintaining a favorable ductility has consistently been a challenge. It is a need to explore more efficient and expedited methods to design magnesium alloys with high modulus and ductility. In this study, machine learning (ML) and assisted microstructure control methods are used to design high modulus magnesium alloys. Six key features that influence stiffness and ductility have been extracted in this ML model based on abundant data from literature sources. As a result, predictive models for Young's modulus and elongation are established, with errors less than 2.4% and 4.5% through XGBoost machine learning model, respectively. Within the given range of six features, the magnesium alloys can be fabricated with the Young's modulus exceeding 50 GPa and an elongation surpassing 6%. As a validation, Mg-Al-Y alloys were experimentally prepared to meet the criteria of six features, achieving Young's modulus of 51.5 GPa, and the elongation of 7%. Moreover, the SHapley Additive exPlanation (SHAP) is introduced to boost the model interpretability. This indicates that balancing the volume fraction of reinforcement, the most important feature, is key to achieve Mg-Al-Y alloys with high Young's modulus and favorable elongation through the two models. Enhancing reinforcement dispersion and reducing the size of reinforcement and grain can further improve the elongation of high-stiffness Mg alloy.
期刊介绍:
Journal of Materials Science & Technology strives to promote global collaboration in the field of materials science and technology. It primarily publishes original research papers, invited review articles, letters, research notes, and summaries of scientific achievements. The journal covers a wide range of materials science and technology topics, including metallic materials, inorganic nonmetallic materials, and composite materials.