A quantitative study of texture evolution mechanisms in cold-rolled 6014 aluminum alloy using the viscoplastic self-consistent model with grain fragmentation

• A novel grain fragmentation mechanism enables mean-field VPSC to outperform full-field VPFFT predictions at only 0.14% of the computational cost. • GF-VPSC achieves quantitative agreement with experiments for the full-process evolution of key deformation texture components. • Cold-rolling texture evolution is governed by three reorientation mechanisms: stable, stable-rotating, and unstable-rotating. The development of cold-rolled textures in aluminum alloys affects their formability and surface quality. Accurate prediction of texture evolution, particularly at intermediate rolling reductions, is essential for optimizing industrial rolling schedules. This study establishes a simulation workflow based on the Grain-Fragmentation ViscoPlastic Self-Consistent (GF-VPSC) framework to predict the texture evolution of a 6014 aluminum alloy from the hot-band condition to a final thickness reduction of 90% in cold rolling. A genetic-algorithm-based optimization scheme is employed to identify the hardening parameters. Texture predictions are benchmarked against full-field ViscoPlastic Fast Fourier Transform (VPFFT) simulations and experimental measurements at multiple deformation stages. GF-VPSC matches and, in some cases, outperforms VPFFT in reproducing the volume fraction of β-fiber and Cube components while requiring significantly lower computational cost. Based on the stability of each orientation predicted by GF-VPSC, main texture components can be categorized into three groups: stable orientations, orientations with stable reorientation paths, and orientations with unstable reorientation paths. These results provide practical guidance for industrial texture design and process optimization.