Transferable Hot‐Deformation Flow‐Curve Modeling Across Steels Through Transfer Learning

ABSTRACT

Hot‐deformation flow modeling in steels is dominated by physics‐based constitutive equations, whereas machine‐learning surrogates are trained in a “one‐case, one‐model” manner—leveraging fitting capacity rather than reducing the calibration burden. Here we propose a transferable sequence framework which learns shared flow‐curve priors across diverse compositions and processing conditions and is then personalized to a new steel using only four extreme‐condition curves. The transfer‐adapted model achieves high accuracy and substantially improved robustness compared with target‐only training, providing a practical alternative to steel‐by‐steel re‐calibration. Strain‐resolved global KernelSHAP offers a compact physical interpretation: temperature contributes predominantly negatively (thermal softening) and strain rate positively (rate hardening), and few‐shot adaptation systematically shifts attribution from high‐dimensional chemistry toward measurable process variables. Practically, this enables fast deployment for new steels, new heats/batches, and new thermomechanical processing windows with only a small set of hot‐compression tests, supporting accelerated process‐window design and thermomechanical simulation.