The Gurson-Tvergaard-Needleman (GTN) model, is widely used to predict the failure of materials based on lab specimens. The direct identification of the GTN parameters is not easy and its time consuming. The most used method to determine the GTN parameters is the combination between the experimental and Finite Element Modeling results and we have to repeat the simulations for many times until the simulation data fits the experimental data in the specimen level. In this paper, we determine the GTN parameters for the SENT specimen based on the fracture toughness test, and we are going to present how the artificial neural network (ANN) method could help us to determine the GTN parameters in a short time. The results obtained from this work show that the ANN is a great tool to determine the GTN parameters in addition to this the determined parameters respect very well the literature.
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