Abstract

One of the challenges in material design is to rapidly develop new materials or improve the performance of materials by utilizing the data and knowledge of existing materials. Here, a rapid and effective method of alloy material design via data transfer learning is proposed to efficiently design new alloys using existing data. A new type of aluminum alloy (E2 alloy) with ultra strength and high toughness previously developed by the authors is used as an example. An optimal three-stage solution-aging treatment process (T66R) was efficiently designed transferring 1053 pieces of process-property relationship data of existing AA7xxx commercial aluminum alloys. It realizes the substantial improvement of strength and plasticity of E2 alloy simultaneously, which is of great significance for lightweight of high-end equipment. Meanwhile, the microstructure analysis clarifies the mechanism of alloy performance improvement. This study shows that transferring the existing alloy data is an effective method to design new alloys.

Details

Title
A rapid and effective method for alloy materials design via sample data transfer machine learning
Author
Jiang, Lei 1 ; Zhang, Zhihao 2 ; Hu, Hao 1 ; He, Xingqun 1 ; Fu, Huadong 2   VIAFID ORCID Logo  ; Xie, Jianxin 2 

1  University of Science and Technology Beijing, Beijing Advanced Innovation Center for Materials Genome Engineering, Beijing, China (GRID:grid.69775.3a) (ISNI:0000 0004 0369 0705) 
2  University of Science and Technology Beijing, Beijing Advanced Innovation Center for Materials Genome Engineering, Beijing, China (GRID:grid.69775.3a) (ISNI:0000 0004 0369 0705); University of Science and Technology Beijing, Beijing Laboratory of Metallic Materials and Processing for Modern Transportation, Institute for Advanced Materials and Technology, Beijing, China (GRID:grid.69775.3a) (ISNI:0000 0004 0369 0705); University of Science and Technology Beijing, Key Laboratory for Advanced Materials Processing (MOE), Beijing, China (GRID:grid.69775.3a) (ISNI:0000 0004 0369 0705) 
Pages
26
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20573960
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1038/s41524-023-00979-9
ProQuest document ID
2778776365
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.