期刊
JOURNAL OF MATERIALS PROCESSING TECHNOLOGY
卷 216, 期 -, 页码 385-404出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.jmatprotec.2014.10.009
关键词
Wire drawing; Wire flat rolling; Fracture prediction; Ductile damage; Lode parameter; mixed FEM
资金
- ArcelorMittal
- Cezus-Areva
- Ugitech via the METAL project
Damage growth and ductile fracture prediction is still an open question for complex stress state applications. A lot of models, both phenomenological and micromechanical, have been extensively developed. There is a real need to compare them to choose the best suitable for complex loading applications. This is done here taking examples in cold metal forming, namely wire drawing and wire flat rolling. In the present study, the prediction of damage for the ultimate wire drawing and the wire flat rolling processes of a high carbon steel is investigated, using three different approaches of ductile damage: uncoupled phenomenological models (or fracture criteria), coupled phenomenological models (accounting for the softening effect of damage), and micromechanical models (accounting for damage associated microstructure evolution). These models were first implemented in a finite element code dedicated to forming process simulations, then calibrated via different mechanical tests exhibiting different stress states. Numerical results of the applications of these models to the two above-mentioned forming processes simulations were compared with experimental ones. These applications help comparing different approaches for fracture prediction in multi-stage forming processes and also in the process that involves important shear effect. The present study supplies important data for the characterization of ductile failure in forming processes, as well as an effective assessment of different phenomenological and micromechanical models, characterizing their performance for different stress states. It also suggests the use of modular models for complex loading cases, by combining different driving factors of damage accumulation at different stress states. (C) 2014 Elsevier B.V. All rights reserved.
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