The dependence of the strain induced surface roughness on the surface texture was investigated for finally annealed and subsequently stretched AA6xxx aluminum sheet. To characterize the surface texture, EBSD scans (performed at TUDelft by T. Bennett and L. Kestens) were analyzed. A statistical approach, based on two-dimensional correlation functions, was introduced to obtain quantitative information on the spatial arrangement of orientations and the possible clustering of similarly oriented grains. Clusters of grains exhibiting the Cube, the RD-rotated Cube or the Goss orientation are observed to align in the RD. Various approximating micromechanical models were implemented and validated with experimental data to acquire knowledge on which texture components can cause surface roughness under which loading conditions. The developed micromechanical models differ in the way the boundary conditions are implemented, to be able to investigate the influence of the mechanical constraints on the predicted surface roughness. Fourier analysis was applied to discriminate between low and high frequency surface roughness components and statistical methods were developed to correlate texture and predicted surface roughness profiles. It is shown that the a-SRM model yields the best correlation between the surface texture and simulated surface roughness.
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