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April 23, 2026Materials & DesignOpen Access

Heat treatment design guided by semi-supervised learning for strength-ductility optimization in an Al-Cu-Li alloy

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Authors

LJLyu JingYLYanan LiLXLi Xiwu

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Overview

Experimental optimization enhances strength-ductility in Al-Cu-Li alloys, suggesting new strategies for alloy design.

Key Points

  • The aim is to optimize heat treatment schedules for improving the strength-ductility product in Al-Li alloys using semi-supervised learning.
  • Developed a dual-stage aging schedule following initial experiments.
  • Utilized active learning and self-training for parameter optimization.
  • Iteratively validated conditions through a series of experiments.
  • Achieved an 11.5% improvement in the strength-ductility product compared to conventional methods.
  • Refined θ′ precipitates while maintaining stable T1 populations.
  • Demonstrated effective decoupled control over precipitation processes.

Cite This Study

Jing et al. (2026) studied this question.

synapsesocial.com/papers/69e9b6aa85696592c86eb0a6https://doi.org/10.1016/j.matdes.2026.116054
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