Prediction of Grain Structure and Texture in Twin-Roll Cast Aluminum Alloys Using Cellular Automaton-Finite Element Method

Abstract

The twin-roll casting (TRC) process has gained significant attention for aluminum sheet production due to its cost-effectiveness and high processing efficiency. However, controlling the initial grain structure of TRC strips remains challenging due to the absence of a hot rolling stage, necessitating an advanced predictive modeling approach. In this study, a cellular automaton-finite element (CA-FE) model was developed to predict the grain structure and texture of aluminum strips fabricated via TRC. Both pure Al and AA7075 alloys were cast under identical conditions using a pilot-scale horizontal twin-roll caster, and their microstructures were characterized experimentally. The developed model incorporated a Gaussian nucleation distribution function and an equivalent binary approach to account for the solidification behavior of multicomponent alloys. The CA-FE simulation results successfully reproduced the key aspects of solidification, grain structure, and texture evolution of TRC strips. The predicted temperature distribution and solid fraction evolution showed distinct differences between the alloys, with pure Al forming columnar grains and AA7075 developing a fully equiaxed structure, which closely matched the experimental findings. Additionally, texture analysis using inverse pole figures (IPFs) and pole figures (PFs) revealed a clear <001> orientation in pure Al, whereas AA7075 exhibited a random texture, both of which were well captured by the CA-FE model. The findings indicate that the developed model offers a reliable prediction of the solidification microstructure and texture evolution in TRC strips, making it a valuable tool for optimizing continuous casting processes.