Efficient multiscale design of microstructure evolution in shape rolling: a validated FEM-FCA framework

Accurate prediction and design of microstructure evolution are essential for optimizing the mechanical performance of shape-rolled components. This work presents an efficient multiscale FEM–FCA computational framework that enables proactive microstructure design in shape rolling processes. The framework integrates Finite Element Method (FEM)-derived thermomechanical fields with a computationally efficient Frontal Cellular Automata (FCA) approach that captures grain nucleation, growth, and static recrystallization (SRX). Unlike traditional CA methods that suffer from excessive computational costs, the FCA approach reduces simulation time by an order of magnitude while maintaining physical accuracy through front-tracking mechanisms. Originally validated for flat rolling, the framework has been extended to complex shape rolling geometries (square–oval–round sequences) through seamless multiscale data coupling. Comprehensive validation against experimental data from AISI 304 L stainless steel demonstrates excellent predictive capability, achieving RMSE of 1.099 μm (8.12%) for grain size and accurate flow stress evolution. This validated framework bridges the gap between detailed microstructural modeling and industrial applicability, offering a scalable tool for process optimization across diverse rolling operations and materials.