Tube hydroforming compression test for friction estimation—numerical inverse method, application, and analysis View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2013-02

AUTHORS

A. Fiorentino, E. Ceretti, C. Giardini

ABSTRACT

Friction plays an important role in forming processes, in fact it influences the material flow and therefore it affects the process and part characteristics. In particular, friction is a very influencing factor in tube hydroforming (THF), where high die–part contact pressure and area make the material sliding very difficult. As a consequence, the material hardly flows to the expansion zones and the part formability can be compromised. To obtain sound parts, FEM models allow the study of the process and optimize its parameters, but they require the right definition of the friction at tube–die interface. For these reasons, friction represents a key-point in THF processes and its knowledge and prediction are very important even if, nowadays, a comprehensive friction test for THF is not available in literature. With this paper, the authors want to propose and evaluate a method to estimate friction for THF processes. In particular, a numerical inverse method allowing the estimation of the Coulombian friction coefficient combining experimental test and FE simulation results will be described. The method is based on the effects of friction on the tube final thickness distribution when it is pressurized and compressed by two punches under different lubrication conditions without expansion. In particular, how the use of few and fast FE simulations allows to estimate an analytical function that takes into account the process conditions and that can be used in combination with experimental results in order to estimate the friction coefficient in THF processes will be shown. More... »

PAGES

695-705

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00170-012-4044-0

DOI

http://dx.doi.org/10.1007/s00170-012-4044-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1002616337


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