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Articles

Compact and Interpretable Strength--Ductility Prediction of Heat-Treatable Aluminum Alloys Using Physics-Retained Explainable Multi-Target Machine Learning

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By Tejinder Kaur, Ankita Chhikara, Krishnanjaneyulu Payala
| Preprints.org
You are already at the latest version Predicting yield strength (YS), ultimate tensile strength (UTS), and elongation (El) in heat-treatable aluminum alloys is challenging because of nonlinear interactions between alloy composition and heat-treatment parameters. This study proposes a physics-retained, explainable, and uncertainty-aware multi-target machine-learning framework for simultaneous strength–ductility prediction.
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