Abstract
In this work, the sensitivity-based virtual fields have been applied to identify two anisotropic plasticity models (Hill48, Yld2000-2D) using a deep-notched tensile test performed on flat samples of cold-rolled sheet of DC04 steel. The material was characterised using the standard protocol to obtain the reference sets of parameters. Deformation data was obtained during deep-notched tests using stereo digital image correlation and the virtual fields method was employed to identify material parameters. It was found that the sensitivity-based virtual fields outperform the standard user-defined virtual fields in terms of accuracy.
Highlights
To describe material behaviour accurately, models have become increasingly complex and involve more and more material parameters that need to be identified from mechanical tests
We present an experimental validation of the sensitivity-based virtual fields for anisotropic plasticity
The hardening law was identified using strain of up to 10%, as at the higher deformation the assumed hardening model does not capture the material behaviour accurately. This could be improved upon in the future, the main objective of this contribution is to demonstrate the effectiveness of the sensitivity-based virtual fields (SBVFs), as opposed to improving the constitutive description of the material
Summary
To describe material behaviour accurately, models have become increasingly complex and involve more and more material parameters that need to be identified from mechanical tests. Many tests are generally needed to fully characterise such material models. Developments in full-field measurements offer the ability to collect large amounts of data with the potential to improve identification of material properties. This can be used to design a new class of tests, where deformation is heterogeneous, leading to a range of multi-axial stress states within a single specimen. Probing material behaviour under such loading provides an opportunity for a reduction of the number of tests needed for characterisation, and the development of better models
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