Comparison of mathematical and supervised machine-learning models for ductile-to-brittle transition in bcc alloys

Abstract

This study focuses on modelling Ductile-to-Brittle Transition (DBT) curves using various mathematical and supervised machine learning models. Charpy impact energy values are converted to normalized energy values to account for reductions in upper-shelf energy. The research introduces a saturation parameter in mathematical models to capture these variations and examines the influence of alloying elements, microstructure, and neutron irradiation on DBT behaviour in nuclear structural materials. Detailed analyses reveal how fitting parameters vary with these factors and demonstrate that mathematical models’ fitting parameters generally align with observed DBT curve trends. The predictive capabilities of these mathematical models are also compared with those of supervised machine learning models, highlighting the strengths and limitations of each approach in modelling DBT behaviour. An explainable approach is used for interpretation of machine learning models and it is shown that this approach can be effectively used for the influence of various independent parameters on impact energy.