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Accelerated discovery of high-strength aluminum alloys by machine learning

Engineering and Technology

Accelerated discovery of high-strength aluminum alloys by machine learning

J. Li, Y. Zhang, et al.

This study explores the innovative Al-Zn-Mg-Cu alloy system, achieving an impressive ultimate tensile strength of 952 MPa and 6.3% elongation through machine learning-based optimization. The research led by Jiaheng Li and colleagues showcases the potential of machine learning in advancing high-performance aluminum alloys.

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Playback language: English
Abstract
Developing new aluminum alloy compositions with high specific strength is a major goal. This study investigates the Al-Zn-Mg-Cu alloy system (7xxx series) using machine learning-based composition and process optimization. The optimized alloy exhibits a high ultimate tensile strength (UTS) of 952 MPa and 6.3% elongation using a cost-effective processing route. The study demonstrates the feasibility of machine learning in discovering high-performance 7xxx alloys.
Publisher
Communications Materials
Published On
Oct 12, 2020
Authors
Jiaheng Li, Yingbo Zhang, Xinyu Cao, Qi Zeng, Ye Zhuang, Xiaoying Qian, Hui Chen
Tags
Aluminum alloys
Machine learning
7xxx series
Ultimate tensile strength
Composition optimization
High-performance materials
Metallurgy

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