Effect of constitutive model on springback prediction of MP980 and AA6022-T4
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Abstract
Springback simulation of stamped sheet metal components using finite element method depends on the accuracy of appropriate material models and consideration of appropriate experimental strategies. In this work, tension-compression tests with different strategies, e.g. tension-compression, compression-tension up to various strain levels and multicycle compression-tension tests were conducted to determine parameters of the Yoshida-Uemori (Y-U) nonlinear dynamic hardening model using optimization analysis software LS-OPT. Finite element simulations with LS-DYNA were performed to predict springback behavior of both the advanced high strength steel MP980 (a 980 MPa grade multiphase steel) and aluminum alloy 6022-T4, which was then compared to measurements of stamped U-channel specimens. Results suggest that although the various tension-compression testing strategies can significantly affect the determined values of Yoshida-Uemori model parameters, springback prediction accuracy with this model does not depend on the associated variation of model parameters, at least for the two-dimensional sidewall curl of a U-channel shape. For materials (e.g. MP980) exhibiting a clear Bauschinger effect but insignificant texture anisotropy, the selection of suitable yield criteria (e.g. Hill48), the consideration of elastic modulus degradation combined with the Y-U model can obviously increase the accuracy of springback prediction. In contrast, materials (e.g. AA6022-T4) that exhibit little Bauschinger effect but have significant texture anisotropy, the use of a yield criterion that accounts for anisotropy (e.g. YLD2000-2D) is more important for improving the accuracy of springback prediction.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Metal Forming Simulation Techniques · Metallurgy and Material Forming · Microstructure and Mechanical Properties of Steels
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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