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Preprints, Working Papers, ... Year : 2023

Experimental characterization of material strain-rate dependence based on full-field Data-Driven Identification

Abstract

This study experimentally explores the performances of a rate-dependent formulation of the Data-Driven stress Identification method for characterizing, using a single test, the monotonic high strain-rate dependent response of a mild steel alloy. First the Data-Driven Identification (DDI) method is presented in its rate-dependent form. A digital twin of a high speed tensile test performed on a notched sample geometry is then used to assess the performances of the DDI. It allows defining confidence intervals depending on multiple indicators (stress magnitude, multiaxiality...) and evaluate the range of strain-rate levels simultaneously captured. At last, the method is applied to a real experiment instrumented with High spatial Resolution Ultra High Speed camera (HR-UHS). The kinematic data are retrieved using Digital Image Correlation (DIC) then used as input for the DDI. Stress tensor fields are identified then the material rate-dependence retrieved and compared to the literature.
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Dates and versions

hal-04048778 , version 1 (28-03-2023)
hal-04048778 , version 2 (11-04-2023)
hal-04048778 , version 3 (17-07-2023)
hal-04048778 , version 4 (26-03-2024)

Identifiers

  • HAL Id : hal-04048778 , version 4

Cite

Adrien Vinel, Rian Seghir, Julien Berthe, Gerald Portemont, Julien Réthoré. Experimental characterization of material strain-rate dependence based on full-field Data-Driven Identification. 2023. ⟨hal-04048778v4⟩
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