journal article·2016-11

Springback prediction and reduction in deep drawing under influence of unloading modulus degradation

Hamad ul Hassan, Fawad Maqbool, Alper Güner, Alexander Hartmaier, Nooman Ben Khalifa, A. Erman Tekkaya

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Abstract

Springback is considered as one of the major problems in deep drawing of high-strength steels (HSS) and advanced high-strength steels (AHSS) which occurs during the unloading of part from the tools. With an ever increasing demand on the automotive manufactures for the production of lightweight automobile structures and increased crash performance, the use of HSS and AHSS is becoming extensive. For the accurate prediction of springback, unloading behavior of dual phase steels DP600, DP1000 and cold rolled steel DC04 for the deep drawing process is investigated and a strategy for the reduction of springback based on variable blankholder force is also presented. Cyclic tension compression tests and LS-Opt software are used for the identification of material parameters for Yoshida-Uemori (YU) model. Degradation of the Young’s modulus is found to be 28 and 26 and 14 % from the initial Young’s modulus for DP600, DP1000 and for the DC04 respectively for the saturated value. A finite element model is generated in LS-DYNA based on the kinematic hardening material model, namely Yoshida-Uemori (YU) model. The validation of numerical simulations is also carried out by the real deep drawing experiments. The springback could be predicted with the maximum deviation of 1.1 mm for these materials. For DP1000, the maximum springback is reduced by 24.5 %, for DP600 33.3 and 48.7 % for DC04 by the application of monotonic blankholder force instead of a constant blankholder force of 80 kN. It is concluded that despite the reduction of Young’s modulus, the springback can be reduced for these materials by increasing the blankholder force only in last 13 % of the punch travel.
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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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