Sensitivity analysis of the GTN damage parameters at different temperature for dynamic fracture propagation in X70 pipeline steel using neural network

Abdelmoumin Ouladbrahim; Idir Belaidi; Samir Khatir; Erica Magagnini; Roberto  Capozucca; Magd  Abdel Wahab

doi:10.3221/IGF-ESIS.58.32

Sensitivity analysis of the GTN damage parameters at different temperature for dynamic fracture propagation in X70 pipeline steel using neural network

Authors

Abdelmoumin Ouladbrahim Department of Mechanical Engineering, University M’hamed Bougara Boumerdes, LEMI Laboratory, 35000 Boumerdes, Algeria. 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

DOI:

https://doi.org/10.3221/IGF-ESIS.58.32

Keywords:

Steel X70, Impact test (CVN), GTN parameters, FEM, Artificial neural network

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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Published

25-09-2021

Issue

Vol. 15 No. 58 (2021): October 2021

Section

SI: Steels and Composites for Engineering Structures

License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright
Authors are allowed to retain both the copyright and the publishing rights of their articles without restrictions.

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How to Cite

Sensitivity analysis of the GTN damage parameters at different temperature for dynamic fracture propagation in X70 pipeline steel using neural network. (2021). Frattura Ed Integrità Strutturale, 15(58), 442-452. https://doi.org/10.3221/IGF-ESIS.58.32