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Parameter identification of a mechanical ductile damage using Artificial Neural Networks in sheet metal forming

Abbassi, Fethi and Belhadj, Touhami and Mistou, Sébastien and Zghal, Ali Parameter identification of a mechanical ductile damage using Artificial Neural Networks in sheet metal forming. (2013) Materials & Design, 45. 605-615. ISSN 0261-3069

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Official URL: http://dx.doi.org/10.1016/j.matdes.2012.09.032

Abstract

In this paper, we report on the developed and used of finite element methods, have been developed and used for sheet forming simulations since the 1970s, and have immensely contributed to ensure the success of concurrent design in the manufacturing process of sheets metal. During the forming operation, the Gurson–Tvergaard–Needleman (GTN) model was often employed to evaluate the ductile damage and fracture phenomena. GTN represents one of the most widely used ductile damage model. In this investigation, many experimental tests and finite element model computation are performed to predict the damage evolution in notched tensile specimen of sheet metal using the GTN model. The parameters in the GTN model are calibrated using an Artificial Neural Networks system and the results of the tensile test. In the experimental part, we used an optical measurement instruments in two phases: firstly during the tensile test, a digital image correlation method is applied to determinate the full-field displacements in the specimen surface. Secondly a profile projector is employed to evaluate the localization of deformation (formation of shear band) just before the specimen’s fracture. In the validation parts of this investigation, the experimental results of hydroforming part and Erichsen test are compared with their numerical finite element model taking into account the GTN model. A good correlation was observed between the two approaches.

Item Type:Article
Additional Information:Thanks to Elsevier editor. The definitive version is available at http://www.sciencedirect.com The original PDF of the article can be found at Materials & Design website : http://dx.doi.org/10.1016/j.matdes.2012.09.032
Audience (journal):International peer-reviewed journal
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Institution:Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Other partners > Université de Tunis (TUNISIA)
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Deposited On:18 Apr 2013 09:50

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