Speaker
Description
An Integrated Computational Materials Engineering (ICME) framework is presented for physics-based prediction of microstructure evolution in industrial steel processing, including ingot casting, reheating, and forging. The framework explicitly links macroscale process simulations with mesoscale, physics‑based, and CALPHAD‑based thermodynamic and kinetic models, enabling consistent mapping from processing conditions and thermomechanical histories to microstructural outcomes.
During casting, the transient temperature field is calculated using a macroscale finite element method (FEM) implemented in an in-house code. The resulting thermal histories are used as input to CALPHAD‑based models to predict the solidification path under non‑equilibrium conditions. Microsegregation is evaluated to capture local compositional variations arising during solidification, while precipitation behavior is predicted using CALPHAD thermodynamics and kinetics to quantify precipitate evolution as a function of composition and cooling rate.
During the reheating stage prior to forging, dissolution phenomena are modeled within the same CALPHAD framework, using reheating temperature profiles as input. This enables consistent tracking of the precipitate state and matrix chemistry and provides physically meaningful initial microstructural conditions for subsequent deformation.
During forging, temperature, strain, and strain-rate fields are calculated using a coupled thermomechanical FEM, developed in an in-house code. These fields are linked to microstructure evolution models to predict deformation‑induced phenomena, with particular emphasis on static and metadynamic recrystallization. The evolution of recrystallized fraction and grain size is calculated as a function of local thermomechanical conditions and the inherited microstructure from casting and reheating.
The proposed ICME‑enabled digital framework provides a unified and robust platform for predicting microstructure evolution across industrial steel casting and forging routes, enabling improved microstructural control, process optimization, and reduced reliance on empirical trial and error.
| Speaker Company/University | Chunhui Luo, Swerim AB |
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