Prediction of Gurson damage model parameters coupled with hardening law identification of steel X70 pipeline using neural network

The Gurson–Tvergaard–Needleman damage model (GTN) describes the three stages of ductile tearing of steel: nucleation, growth and coalescence of micro-voids. This work is divided into two main parts. In the first part, based on the inverse analysis and the comparison between the experimental and numerical data, the parameters of the GTN damage model in conjunction with the hardening law are determined. The identification is broadened to include a considerable number of experimental tests drawn from our previous works and other works done at ALFAPIPE Ghardaia laboratory. In the second part, an Artificial Neural Network model is developed to predict the parameters of the (GTN) model coupled with the hardening law that goes through the prediction of traction and impact properties of API X70 steel pipe depending on its chemical composition. The weight of the chemical elements in percentages is considered as the inputs and the GTN parameters are considered as the outputs. In order to validate the obtained ANNGTN parameters, traction and impact tests are simulated. The numerical results are compared with the experimental ones and revealed that the developed model is very precise and has the potential to capture the interaction of GTN parameters coupled with hardening law and chemical composition of steel pipelines