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| Titre: | Sensitivity analysis of the gtn damage parameters at different temperature for dynamic fracture propagation in x70 pipeline steel using neural network |
| Auteur(s): | Abdelmoumin Ouladbrahim, Abdelmoumin
Belaidi, Idir
Khatir, Samir
Magagnini, Erica
Capozucca, Roberto
Wahab, Magd Abdel |
| Mots-clés: | Artificial neural network
FEM
GTN parameters
Impact test (CVN)
Steel X70 |
| Date de publication: | 2021 |
| Editeur: | Gruppo Italiano Frattura |
| Collection/Numéro: | Frattura ed Integrita Strutturale/ Vol.15, N°58 (2021);pp. 442-452 |
| Résumé: | 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 |
| URI/URL: | https://www.fracturae.com/index.php/fis/article/view/3253
http://dlibrary.univ-boumerdes.dz:8080/handle/123456789/7494 |
| ISSN: | 19718993
DOI 10.3221/IGF-ESIS.58.32 |
| Collection(s) : | Publications Internationales
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