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The aim is to optimize heat treatment schedules for improving the strength-ductility product in Al-Li alloys using semi-supervised learning.

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

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.

Jing et al. (2026) studied this question.

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