Approach for modelling the Taylor-Quinney coefficient of high strength steels

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Behrens, B.-A.; Chugreev, A.; Bohne, F.; Lorenz, R.: Approach for modelling the Taylor-Quinney coefficient of high strength steels. In: Procedia Manufacturing 29 (2019), S. 464-471. DOI: https://doi.org/10.1016/j.promfg.2019.02.163

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Precise knowledge of the temperature that arises in the material during plastic forming is of crucial importance, as it has a significant influence on material behaviour and therefore on the forming process. In order to describe the amount of heat that is generated during plastic forming accurately, the Taylor-Quinney coefficient β was introduced as the ratio of dissipated heat to plastic work and generally assumed to be a constant value. However, recent studies have shown that there is a dependency on material and process-specific parameters. In this study, the Taylor-Quinney coefficient β is shown as a function of strain and being influenced by the test specific strain rate and stress state. The tested material is a dual-phase steel HCT980X. The uniaxial tensile test and the Marciniak test with different tallied specimen at forming-relevant global strain rates were investigated. By means of thermographic and optical measuring systems the temperature and local strains were recorded during the tests. Based on an approach similar to the finite volume method, both experimental setups were modelled taking heat transfer effects into account. As a result, the Taylor-Quinney coefficient is calculated by means of experimental data. It is shown that the Taylor-Quinney coefficient is a variable value depending on the flow behaviour of the steel. The local strain rate and the specimen geometries of Marciniak test have a significant influence on the arising heat conduction. The stress state, however, has minor influence on β.
License of this version: CC BY-NC-ND 4.0 Unported
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2019
Appears in Collections:Fakultät für Maschinenbau

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pos. country downloads
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1 image of flag of Germany Germany 207 65.92%
2 image of flag of United States United States 47 14.97%
3 image of flag of France France 11 3.50%
4 image of flag of China China 9 2.87%
5 image of flag of No geo information available No geo information available 7 2.23%
6 image of flag of Austria Austria 6 1.91%
7 image of flag of Switzerland Switzerland 5 1.59%
8 image of flag of Vietnam Vietnam 3 0.96%
9 image of flag of Russian Federation Russian Federation 3 0.96%
10 image of flag of Finland Finland 3 0.96%
    other countries 13 4.14%

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