Exploration of Alloying Elements of High Specific Modulus Al–Li Alloy Based on Machine Learning
In the aerospace sector, the development of lightweight aircraft heavily relies on the utilization of advanced aluminum–lithium alloys as primary structural materials. This study introduces an investigation aimed at optimizing the composition of an Al-2.32Li-1.44Cu-2.78Mg-0.3Ag-0.3Mn-0.1Zr alloy. Th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Online Access: | https://www.mdpi.com/1996-1944/17/1/92 |
| _version_ | 1797358511343009792 |
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| author | Huiyu Li Xiwu Li Yanan Li Guanjun Gao Kai Wen Zhihui Li Yongan Zhang Baiqing Xiong |
| author_facet | Huiyu Li Xiwu Li Yanan Li Guanjun Gao Kai Wen Zhihui Li Yongan Zhang Baiqing Xiong |
| author_sort | Huiyu Li |
| collection | DOAJ |
| description | In the aerospace sector, the development of lightweight aircraft heavily relies on the utilization of advanced aluminum–lithium alloys as primary structural materials. This study introduces an investigation aimed at optimizing the composition of an Al-2.32Li-1.44Cu-2.78Mg-0.3Ag-0.3Mn-0.1Zr alloy. The optimization process involves the selection of alloying elements through the application of machine learning techniques, with a focus on expected improvements in the specific modulus of these alloys. Expanding upon the optimization of the benchmark alloy’s components, a more generalized modulus prediction model for Al–Li alloys was formulated. This model was then employed to evaluate the anticipated specific modulus of alloys within a virtual search space, encompassing substitutional elements. The study proceeded to validate six Al–Li alloys with a notably high potential for achieving an improved specific modulus. The results revealed that an alloy incorporating 0.96 wt.% of Ga as a substitutional element exhibited the most favorable microstructure. This alloy demonstrated optimal tensile strength (523 MPa) and specific modulus (31.531 GPa/(g·cm<sup>−3</sup>)), closely resembling that of the benchmark alloy. This research offers valuable insights into the application of compositional optimization to enhance the mechanical properties of Al–Li alloys. It emphasizes the significance of selecting alloying elements based on considerations such as their solid solubility thresholds and the expected enhancement of the specific modulus in Al–Li alloys. |
| first_indexed | 2024-03-08T15:03:04Z |
| format | Article |
| id | doaj.art-1912b66e3b38498dbf6d5817d76f9da2 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-08T15:03:04Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-1912b66e3b38498dbf6d5817d76f9da22024-01-10T15:02:34ZengMDPI AGMaterials1996-19442023-12-011719210.3390/ma17010092Exploration of Alloying Elements of High Specific Modulus Al–Li Alloy Based on Machine LearningHuiyu Li0Xiwu Li1Yanan Li2Guanjun Gao3Kai Wen4Zhihui Li5Yongan Zhang6Baiqing Xiong7State Key Laboratory of Nonferrous Metals and Processes, China GRINM Group Co., Ltd., Beijing 100088, ChinaState Key Laboratory of Nonferrous Metals and Processes, China GRINM Group Co., Ltd., Beijing 100088, ChinaState Key Laboratory of Nonferrous Metals and Processes, China GRINM Group Co., Ltd., Beijing 100088, ChinaState Key Laboratory of Nonferrous Metals and Processes, China GRINM Group Co., Ltd., Beijing 100088, ChinaState Key Laboratory of Nonferrous Metals and Processes, China GRINM Group Co., Ltd., Beijing 100088, ChinaState Key Laboratory of Nonferrous Metals and Processes, China GRINM Group Co., Ltd., Beijing 100088, ChinaState Key Laboratory of Nonferrous Metals and Processes, China GRINM Group Co., Ltd., Beijing 100088, ChinaState Key Laboratory of Nonferrous Metals and Processes, China GRINM Group Co., Ltd., Beijing 100088, ChinaIn the aerospace sector, the development of lightweight aircraft heavily relies on the utilization of advanced aluminum–lithium alloys as primary structural materials. This study introduces an investigation aimed at optimizing the composition of an Al-2.32Li-1.44Cu-2.78Mg-0.3Ag-0.3Mn-0.1Zr alloy. The optimization process involves the selection of alloying elements through the application of machine learning techniques, with a focus on expected improvements in the specific modulus of these alloys. Expanding upon the optimization of the benchmark alloy’s components, a more generalized modulus prediction model for Al–Li alloys was formulated. This model was then employed to evaluate the anticipated specific modulus of alloys within a virtual search space, encompassing substitutional elements. The study proceeded to validate six Al–Li alloys with a notably high potential for achieving an improved specific modulus. The results revealed that an alloy incorporating 0.96 wt.% of Ga as a substitutional element exhibited the most favorable microstructure. This alloy demonstrated optimal tensile strength (523 MPa) and specific modulus (31.531 GPa/(g·cm<sup>−3</sup>)), closely resembling that of the benchmark alloy. This research offers valuable insights into the application of compositional optimization to enhance the mechanical properties of Al–Li alloys. It emphasizes the significance of selecting alloying elements based on considerations such as their solid solubility thresholds and the expected enhancement of the specific modulus in Al–Li alloys.https://www.mdpi.com/1996-1944/17/1/92Al–Li alloysmachine learningspecific modulus predictioncomposition optimization |
| spellingShingle | Huiyu Li Xiwu Li Yanan Li Guanjun Gao Kai Wen Zhihui Li Yongan Zhang Baiqing Xiong Exploration of Alloying Elements of High Specific Modulus Al–Li Alloy Based on Machine Learning Materials Al–Li alloys machine learning specific modulus prediction composition optimization |
| title | Exploration of Alloying Elements of High Specific Modulus Al–Li Alloy Based on Machine Learning |
| title_full | Exploration of Alloying Elements of High Specific Modulus Al–Li Alloy Based on Machine Learning |
| title_fullStr | Exploration of Alloying Elements of High Specific Modulus Al–Li Alloy Based on Machine Learning |
| title_full_unstemmed | Exploration of Alloying Elements of High Specific Modulus Al–Li Alloy Based on Machine Learning |
| title_short | Exploration of Alloying Elements of High Specific Modulus Al–Li Alloy Based on Machine Learning |
| title_sort | exploration of alloying elements of high specific modulus al li alloy based on machine learning |
| topic | Al–Li alloys machine learning specific modulus prediction composition optimization |
| url | https://www.mdpi.com/1996-1944/17/1/92 |
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