4.7 Article

Micromechanics-based simulation of quasi-static and dynamic crushing of double-chamber 6000-series aluminium profiles

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PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijimpeng.2023.104636

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Ductile fracture; Gurson-Tvergaard-Needleman (GTN) model; Solid elements; Computed tomography (CT)

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Numerical simulation is increasingly important in the automotive industry for the development of crash components, as it allows for accurate predictions of deformation and fracture behavior. This study aims to predict crash behavior using explicit finite element simulations and a porous plasticity model, and compares the results to CT scans of the profiles. While the simulations accurately represent important features, such as folding pattern and fracture initiation, there is still room for improvement in dynamic simulations.
Numerical simulation is becoming an increasingly important tool in the development of crash components in the automotive industry. With accurate numerical predictions of deformation and fracture, developers may be able to optimize and utilize the energy absorption capacity of the material to its full potential. This study aims to predict the crash behaviour of such components from previous experiments by Qvale et al. (2021) using explicit finite element simulations with a fine discretization of solid elements and the Gurson-Tvergaard- Needleman porous plasticity model. How well the experimental behaviour was predicted by the simulations was evaluated through comparison to computed tomography (CT) scans throughout the entire volume of the profiles. Despite the pragmatic approach of calibrating some parameters of the constitutive model to only uniaxial tension tests, and obtaining the remaining parameters from the literature, important features, such as local details of the folding pattern and the locations of fracture initiation, were represented remarkably well, while there is still room for improvement in the dynamic simulations, as the changes from quasi-static to dynamic axial crushing were slightly underestimated.

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