Combining Full-Field Measurements and Inverse Techniques for Smart Material Testing

Abstract

Traditionally, material properties (such as Young’s modulus, yield stress, etc.) are determined from a series of simple tensile and shear tests performed under various temperatures, strain rates, etc. This process is time-consuming and expensive. Additionally, material properties determined from simplified stress states may not adequately describe material behaviour under more complex loading conditions. In the past decade, full-field deformation measurements such as Digital Image Correlation (DIC) and inverse techniques such as the Virtual Fields Method (VFM) have reached a maturity level that takes them from the realm of development to the realm of application. This paper will present a concerted effort beginning at Sandia National Laboratory to explore a new experimental protocol for high-throughput, high-quality material identification by combining DIC, full-field temperature measurements, and VFM. Our current thrust is focused on identifying visco-plastic material parameters of 304L stainless steel. In particular, we will discuss the question of uniqueness of the material model and how parameter covariance affects material identification.