A data-driven approach for modeling tension–compression asymmetric material behavior: numerical simulation and experiment

In this paper, a direct data-driven approach for the modeling of isotropic, tension–compression asymmetric, elasto-plastic materials is proposed. Our approach bypasses the conventional construction of explicit mathematical function-based elasto-plastic models, and the need for parameter-fitting. In it, stress update is driven directly by a set of stress–strain data that is generated from uniaxial tension and compression experiments (physical). Particularly, for compression experiments, digital image correlation and homogenization are combined to further improve modeling accuracy. Two representative tension–compression asymmetric materials, titanium alloy TC4ELI and high-density polyethylene, are chosen to illustrate the effectiveness and accuracy of our proposed approach. Results indicate that our data-driven approach can predict the mechanical response of elasto-plastic materials that exhibit tension–compression asymmetry, within the small deformation regime. This data-driven approach provides a practical way to model such materials directly from physical experimental data. Our current implementation is limited, however, by a small reduction to computational efficiency, when compared to typical function-based approaches. Moreover, our present formulation is focused on tension–compression asymmetric elasto-plastic materials that are isotropic.