Identification of material parameters of the Gurson-Tvergaard-Needleman damage law by combined experimental, numerical sheet metal blanking techniques and artificial neural networks approach

This paper presents a method for the identification of coupled damage model parameters in sheet metal blanking and a study of their sensitivity to the blanking clearance. The existing finite element models easily describe the elastoplastic behaviour occurring during the sheet metal blanking. However, the description of the damage evolution is much more delicate to appreciate. The proposed method combines finite element method (FEM) with artificial neural networks (ANN) analysis in order to identify the values of the Gurson-Tvergaard-Needleman (GTN) parameters. The blanking tests are carried out to obtain the experimental material response under loading (blanking force-blanking penetration curves). A finite element model is used to compute the load displacement curve depending on a systematic variation of GTN parameters. Via a full factorial design, a numerical database is built up and is used for the ANN training. The identification of the damage properties (for a fixed clearance) is done by minimizing the error between an experimental load displacement curve and a predicted one by the ANN function. The identified damage law parameters are validated on the other experimental configurations of blanking tests (fixed clearance, different punch velocities). Varying the blanking clearance allows us to study its impact on the damage law parameters.