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

Abstract

Springback prediction is necessary for the application of 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, material model taking into consideration important material behaviors (Bauschinger effect, average Young’s modulus, elastic anisotropy and plastic anisotropy) was developed and implemented into FEM software. Moreover, springback analyses were carried out 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 and plastic anisotropies influenced the result of springback prediction, particularly in the case of anisotropic material. Springback analysis taking into consideration all four material behaviors yielded better springback prediction accuracy than those of conventional analyses.