Experimental and numerical study of DC04 sheet metal behaviour—plastic anisotropy identification and application to deep drawing - Université de Lorraine Access content directly
Journal Articles International Journal of Advanced Manufacturing Technology Year : 2019

Experimental and numerical study of DC04 sheet metal behaviour—plastic anisotropy identification and application to deep drawing

Abstract

This paper deals with the identification and modelling of the deformation behaviour of DC04 sheet metal. An application of the identified behaviour model in the framework of finite element (FE) simulation of deep drawing is performed. Uniaxial tensile tests in three directions are performed to reveal the in-plane plastic anisotropy of the DC04 sheet. The Hill48 quadratic criterion is introduced to represent the initial plastic anisotropy, while the work hardening is represented by power and exponential type laws. Parameters of the established behaviour model are identified using two methods based on Hill48 criterion. FE simulations of uniaxial tensile tests are carried out to validate the identified parameters and thus retain the identification method giving best fitting of performed tests. The identified plastic behaviour model, including initial plastic anisotropy and hardening, is then introduced in FE simulation of deep drawing of waterproof case for a truck, in which the effect of blank-holder force on the drawpiece quality is analysed in order to determine the optimum thickness. Then, the effect of combination between plastic anisotropy and friction anisotropy on the sheet thickness distribution is investigated. The performed analysis shows that the plastic and friction anisotropies have an effect on the DC04 sheet metal deformation.
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Dates and versions

hal-03325675 , version 1 (25-08-2021)

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Walid Ghennai, Ouzine Boussaid, Hocine Bendjama, Badis Haddag, Mohammed Nouari. Experimental and numerical study of DC04 sheet metal behaviour—plastic anisotropy identification and application to deep drawing. International Journal of Advanced Manufacturing Technology, 2019, 100 (1-4), pp.361-371. ⟨10.1007/s00170-018-2700-8⟩. ⟨hal-03325675⟩
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