Sensitivity analysis of the GTN damage parameters at different temperature for dynamic fracture propagation in X70 pipeline steel using neural network
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https://orcid.org/0000-0003-4729-298X
Idir Belaidi
Department of Mechanical Engineering, University M’hamed Bougara Boumerdes, LEMI Laboratory, 35000 Boumerdes, Algeria.
Samir Khatir
Soete Laboratory, Faculty of Engineering and Architecture, Ghent University, Technologiepark Zwijnaarde 903, B-9052, Zwijnaarde, Belgium;
Erica Magagnini
DICEA, Structural Section, Polytechnic University of Marche, Italy
Roberto Capozucca
DICEA, Structural Section, Polytechnic University of Marche, Italy
Magd Abdel Wahab
Soete Laboratory, Faculty of Engineering and Architecture, Ghent University, Technologiepark Zwijnaarde 903, B-9052, Zwijnaarde, Belgium;
https://orcid.org/0000-0002-3610-865X
Abstract
In this paper, the initial and maximum load was studied using the Finite Element Modeling (FEM) analysis during impact testing (CVN) of pipeline X70 steel. The Gurson-Tvergaard-Needleman (GTN) constitutive model has been used to simulate the growth of voids during deformation of pipeline steel at different temperatures. FEM simulations results used to study the sensitivity of the initial and maximum load with GTN parameters values proposed and the variation of temperatures.
Finally, the applied artificial neural network (ANN) is used to predict the initial and maximum load for a given set of damage parameters X70 steel at different temperatures, based on the results obtained, the neural network is able to provide a satisfactory approximation of the load initiation and load maximum in impact testing of X70 Steel.
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