From Chemical Composition to Residual Stress in Quenched Steels: Integrated Computational Framework

Prediction of residual stress evolution in quenched steels requires a consistent link between material composition, thermal history, and mechanical response. Comprehensive thermometallurgical models describe these phenomena in detail but are often limited in practice by modelling complexity and extensive material data requirements.

This work proposes an integrated computational framework that establishes a direct connection between chemical composition and residual stress development. Hardenability is predicted from alloy composition using a data-driven model, enabling reconstruction of Jominy hardenability curves without experimental input. The transient temperature field during quenching is obtained using an axisymmetric finite-volume formulation with adaptive time stepping, allowing accurate evaluation of local cooling histories.

Phase transformation effects are captured through hardness, obtained by combining predicted hardenability with local cooling conditions. The resulting hardness distribution is used to define local material strength within a thermo-elasto-plastic framework, enabling estimation of residual stress evolution.

The proposed approach reduces the need for detailed thermometallurgical modelling while retaining the dominant mechanisms governing stress formation, providing a computationally efficient framework for rapid assessment of quenching-induced residual stresses from chemical composition and process parameters.