Open access
Sep 2026
Compact and interpretable strength–ductility prediction of heat-treatable aluminum alloys using physics-retained explainable multi-target machine learning
A physics-retained, explainable, and uncertainty-aware multi-target machine-learning framework for simultaneous strength–ductility prediction in heat-treatable aluminum alloys and assigned the largest model attributions to Zn, Cu, and selected heat-treatment conditions, without implying causal metallurgical effects.
S. Mallidi, Tejinder Kaur, Ankita Chhikara et al.
· Engineering Research Express · 0 citations