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Updated: Sep 15, 2025

Intermediate Strain Rate Material Characterization with Digital Image Correlation
Published on: March 1, 2019
High temperature stress-strain data for SAE 5120 steel under various strain rates
Philippe Moreau1, Jean-Dominique Guérin1, José Grégorio La Barbara Sosa1
1UMR 8201, LAMIH, Laboratoire d'Automatique de Mécanique et d'Informatique Industrielles et Humaines, CNRS, Université Polytechnique Hauts-de-France, Valenciennes F-59313, France.
None:
SAE 5120 is a low-alloy chromium steel widely used in automotive, aerospace, and construction industries for mechanically loaded components. To preserve its mechanical properties and prevent cracking or poor grain structure, it is typically hot-formed between 850°C and 1200°C through forging or rolling. Finite Element simulations are often used to model and optimize these processes, requiring accurate material flow stress and strain hardening data under varying deformation conditions and microstructures. Traditional constitutive models, often based on parametric laws, have limitations: they assume flow stress depends solely on temperature and strain rate, neglecting softening effects from dynamic recrystallization (DRX) and failing to capture stress evolution during transient loading conditions. This work aims to use raw rheological data of SAE 5120 to develop a model based on an incremental formulation that closely reflects the experimental behavior. The dataset includes raw data from axisymmetric compression tests conducted on a Gleeble 3500 system under vacuum, with temperatures ranging from 850°C to 1200°C and strain rates from 0.01 s⁻¹ to 10 s⁻¹. Corrections were applied to account for adiabatic heating and strain rate variations during compression. The processed data, averaged from raw tests, were then used to characterize austenite flow stress as a function of strain rate and temperature using the incremental approach. This model incorporates DRX and the evolution of the recrystallized volume fraction. The resulting data are suitable for direct use in finite element simulations and can enhance material databases for machine learning and deep learning applications.
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