Identification of Swift Law Parameters Using FEMU by a Synthetic Image DIC-Based Approach

Abstract

Computer-aided engineering systems rely on constitutive models and their parameters to describe the material behaviour. The calibration of more elaborated material models with a larger number of parameters becomes very time and cost consuming. The development of image-based technology has enhanced the interest in inverse identification methods, which, when coupled with full-field measurements, have the potential to reduce the number of experimental tests required to accurately identify material properties. This work aims to identify the Swift hardening law parameters of a dual-phase steel using a tensile test on a heterogeneous dogbone specimen under uniaxial and quasi-static loading conditions using the finite element model updating (FEMU) technique. The numerical results were used to generate synthetic images, which were then processed by digital image correlation (DIC) and used as the reference in the identification procedure. Two different approaches were tested: (i) directly comparing the numerical results to the reference; (ii) using DIC-levelled numerical data by iteratively generating synthetic images and using the DIC filter with the same settings as were used on the reference (virtual experiment). The identification results obtained from both approaches are compared and discussed.