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A rapid and effective method for alloy materials design via sample data transfer machine learning

Engineering and Technology

A rapid and effective method for alloy materials design via sample data transfer machine learning

L. Jiang, Z. Zhang, et al.

Discover a groundbreaking method for alloy material design utilizing data transfer learning, as demonstrated by Lei Jiang, Zhihao Zhang, Hao Hu, Xingqun He, Huadong Fu, and Jianxin Xie. This research introduces an innovative aluminum alloy (E2 alloy) that combines ultra-strength with high toughness through a novel treatment approach, proving the potential of advanced data-driven techniques in material science.

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Playback language: English
Abstract
This paper proposes a rapid and effective method for alloy material design using data transfer learning. A new aluminum alloy (E2 alloy) with ultra-strength and high toughness is used as an example. By transferring process-property relationship data from existing AA7xxx aluminum alloys, an optimal three-stage solution-aging treatment (T66R) was designed. This treatment significantly improved both the strength and plasticity of the E2 alloy, demonstrating the effectiveness of data transfer learning in alloy design.
Publisher
npj Computational Materials
Published On
Feb 22, 2023
Authors
Lei Jiang, Zhihao Zhang, Hao Hu, Xingqun He, Huadong Fu, Jianxin Xie
Tags
data transfer learning
aluminum alloy
E2 alloy
process-property relationship
strength
toughness
aging treatment

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