Data assimilation of Elastoplastic Finite Element Analysis Based on the Ensemble Kalman Filter

Abstract

Sheet metal forming simulation based on the finite element method has been used to prevent the forming failure. The prediction accuracy of the sheet metal forming simulation is strongly influenced by material models, those parameters and the work hardening law used in the simulation. In the conventional parameter identification, the stress-strain curve after the necking has not been considered. However, in order to predict the large deformation behavior of the sheet metals accurately, we need to calibrate the material models and the hardening laws which reproduce the large deformation of the materials. In this study, we propose a data assimilation methodology (DA) to estimate the parameters of material models which enables us to predict the post-necking deformation behavior of the materials. This article presents the implementation of the ensemble Kalman filter to the elastoplastic finite element analysis. In order to validate the proposed DA method, we perform the numerical experiments where the parameter of the Swift hardening law is estimated.