Abstract
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 it is time-consuming. The Gurson model is based on micro-mechanical behaviour of ductile fracture, containing void nucleation, growth and coalescence. The most used method to determine the GTN parameters is the combination between the experimental and FEM results but its time consuming as we have to repeat the simulations for many times until the simulation data fits the experimental data in the specimen level (axisymmetric tensile bar and CT specimens), but there are also other methods used to determine the GTN parameters, the aim of these methods is to determine the parameters in a short time as Artificial Neural Network, Hybrid Particle Swarm Optimization, Metallographic Method. In this paper, we determine the GTN parameters for the SENT specimen based on the fracture toughness test of CT specimen. The reason behind choosing the SENT specimen is because it can be a very good representative of the pipe for both uniaxial and biaxial loading conditions. The Results show that the GTN parameters concluded from CT simulations, predict very well the crack initiation and propagation of SENT specimen which confirms the validity of this model.
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