On modelling of shear fracture in deep drawing of a high-strength dual-phase sheet steel

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Behrens, B.-A.; Bonk, C.; Peshekhodov, I.: On modelling of shear fracture in deep drawing of a high-strength dual-phase sheet steel. In: Journal of Physics: Conference Series 896 (2017), Nr. 1, 12125. DOI: https://doi.org/10.1088/1742-6596/896/1/012125

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Sum total of downloads: 186




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The paper presents application of fracture behaviour characterisation results of a dual-phase sheet steel DP600 to an FEA of its deep-drawing for shear fracture prediction. The characterisation results were obtained with the help of a characterisation method based on a tensile test on a novel butterfly specimen and published previously by the authors. The aim of the present paper is to evaluate that characterisation method on a deep-drawing process. Based on the previous results of the authors, the fracture behaviour is modelled here with the help of the modified Mohr-Coloumb fracture model. The obtained FEA results reveal that shear fracture of the studied material is predicted too early by the used MMC fracture model. A novel adjustment of the model is proposed yielding infinitely high fracture strains at strongly pressure-superimposed stress states. As it is often the case in the state-of-the-art fracture characterisation of high-strenght sheet steels, such stress states were not tested during the previously performed fracture characterisation but occur during the studied deep drawing process. With the help of the adjusted MMC fracture model, it is possible to predict the crack initiation moment very accurately and the crack initiation location sufficiently accurately. © Published under licence by IOP Publishing Ltd.
License of this version: CC BY 3.0 Unported
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2017
Appears in Collections:Fakultät für Maschinenbau

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pos. country downloads
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1 image of flag of Germany Germany 116 62.37%
2 image of flag of China China 30 16.13%
3 image of flag of United States United States 24 12.90%
4 image of flag of Singapore Singapore 3 1.61%
5 image of flag of Russian Federation Russian Federation 2 1.08%
6 image of flag of Brazil Brazil 2 1.08%
7 image of flag of Peru Peru 1 0.54%
8 image of flag of Netherlands Netherlands 1 0.54%
9 image of flag of Korea, Republic of Korea, Republic of 1 0.54%
10 image of flag of Iran, Islamic Republic of Iran, Islamic Republic of 1 0.54%
    other countries 5 2.69%

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