Improvement of Springback Prediction Accuracy Using Material Model Considering Elastoplastic Anisotropy and Bauschinger Effect

Abstract Springback prediction is necessary when applying high-strength steel sheets to automotive parts. The accuracy of springback prediction depends on the material model, which describes the deformation behavior of steel sheets. In this research, a material model which considers important material behaviors (Bauschinger effect, average Young’s modulus, elastic anisotropy and plastic anisotropy) was developed and implemented in FEM software. Springback analyses were performed for curved hat-shaped parts made of high-strength steel sheets. As a result, the effects of each material behavior on springback were clarified. It was found that not only the Bauschinger effect and average Young’s modulus but also elastic anisotropy and plastic anisotropy influenced the results of springback predictions, particularly in the case of anisotropic material. Springback analysis considering all four material behaviors yielded better springback prediction accuracy than those of conventional analyses.