Identification of hardening parameters using FEMU and DIC-levelled FEA data

Computer-aided engineering systems play a key role in the simulation of manufacturing processes, to reduce costs and time waste in the development of high[1]quality products. The material constitutive model and its parameters influence the reliability and accuracy of metal forming simulations. Although finite element analysis (FEA) is widely used to simulate processes, the accuracy of the simulation highly depends on the calibration of the model that describes the material behaviour. Classical approaches use simplified geometry and loading conditions, however, only a limited number of parameters can be identified per test configuration. There has been an increase in the use of novel optical, which are contact-free and provide full-field data, such as digital image correlation (DIC) [1]. This full-field measurement data when coupled with inverse identification techniques such as the finite element method updating technique (FEMU) has the potential to reduce the number of experimental tests required to accurately identify all material properties, assuming that the test configuration is rich enough that all material properties play a role in mechanical behaviour. Classically, experimental DIC measurements and FEA results were compared directly in the FEMU approach. However, numerous inconsistencies must be addressed before doing this comparison, including different coordinate systems, data locations, strain formulation, spatial resolutions, and data filtering. To address these issues, recent studies used an approach based on deforming the reference image of a DIC speckle pattern synthetically, using the FEA mesh and displacements [2,3,4]. This work aims to identify the Swift hardening law parameters of a DP600 steel using a virtual tensile test on a dogbone specimen with a non-constant section under uniaxial and quasi-static loading conditions using the FEMU technique. To simulate a real experiment, the numerical results were used to generate synthetic images, which were then processed by DIC and used as the reference in the identification procedure. Two identification approaches are used: first, by using the direct comparison between the DIC measurements and FEA results, and second, a comparison using DIC-leveled FEA data. The identification results of both approaches are compared.