A Data-Driven Process for Estimating Nonlinear Material Models

Driven by the wide range of new material properties offered by multi-material 3D printing, there is emerging need to create predictive material models for these materials. A data driven process for estimating nonlinear material model is presented in this paper. In contrast with classical methods which derive the engineering stress-strain relationship assuming constant cross-section area and fixed length of a specimen, the proposed approach takes full advantage of 3D geometry of the specimen to estimate the material models. Give a hypothetical material model, virtual tensile tests are performed using Finite Element Analysis (FEA) method, and the parameters of the material model are estimated by minimizing the discrepancies of the virtual responses and the experimental results. The detailed material models, numerical algorithms as well as the optimization approaches are presented and finally preliminary results are offered.