Integrating texture evolution into a deep-drawing simulation via viscoplastic self-consistent model

New fields of application in the area of electric mobility (e.g. battery housings, …) justify in-depth (numerical) investigations of semi-finished products. Due to strongly directional deformation processes, such as rolling and extrusion, materials develop anisotropic mechanical properties (i.e. texture, …). Texture can have a considerable effect on the results of follow-up processes, such as deep-drawing of sheet metals. It can therefore be necessary to consider the anisotropic material behavior during the forming process, which can be realized by texture-based finite-element (FE) method simulations. However, current applications of FEM in forming simulations of industrial-sized parts either neglect anisotropic material properties or incorporate a fixed texture by special material models. This is caused by the considerable numerical efforts necessary for full-field solutions, such as crystal plasticity FE (CPFE). To address this major issue, we use a mean-field approach by implementing a viscoplastic self-consistent (VPSC) model into a FE simulation framework, which is numerically more efficient than full-field methods. Nevertheless, the number of considered grains per finite element is a limiting factor. Therefore, our work studies the influence of a reduced, i.e. clustered, number of input grains on the simulation performance. This is done via a comparison of simulation results for cup drawing as well as the respective numerical effort needed. • Reduction of efforts by texture clustering for crystal plasticity simulations. • Prediction accuracy maintained to 10 % of the original grains or even less. • Effects of clustered input texture visible in properties, geometry, and texture. • Efficient simulations by combining the VPSC approach with clustered input texture.