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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 (UGent) , Erica Magagnini, Roberto Capozucca and Magd Abdel Wahab (UGent)
(2021) FRATTURA ED INTEGRITA STRUTTURALE-FRACTURE AND STRUCTURAL INTEGRITY. 58. p.442-452
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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.
Keywords
Mechanical Engineering, Mechanics of Materials, Steel X70, Impact test (CVN), GTN parametersFEM, Artificial neural network

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MLA
Ouladbrahim, Abdelmoumin, et al. “Sensitivity Analysis of the GTN Damage Parameters at Different Temperature for Dynamic Fracture Propagation in X70 Pipeline Steel Using Neural Network.” FRATTURA ED INTEGRITA STRUTTURALE-FRACTURE AND STRUCTURAL INTEGRITY, vol. 58, 2021, pp. 442–52, doi:10.3221/igf-esis.58.32.
APA
Ouladbrahim, A., Belaidi, I., Khatir, S., Magagnini, E., Capozucca, R., & Abdel Wahab, M. (2021). Sensitivity analysis of the GTN damage parameters at different temperature for dynamic fracture propagation in X70 pipeline steel using neural network. FRATTURA ED INTEGRITA STRUTTURALE-FRACTURE AND STRUCTURAL INTEGRITY, 58, 442–452. https://doi.org/10.3221/igf-esis.58.32
Chicago author-date
Ouladbrahim, Abdelmoumin, Idir Belaidi, Samir Khatir, Erica Magagnini, Roberto Capozucca, and Magd Abdel Wahab. 2021. “Sensitivity Analysis of the GTN Damage Parameters at Different Temperature for Dynamic Fracture Propagation in X70 Pipeline Steel Using Neural Network.” FRATTURA ED INTEGRITA STRUTTURALE-FRACTURE AND STRUCTURAL INTEGRITY 58: 442–52. https://doi.org/10.3221/igf-esis.58.32.
Chicago author-date (all authors)
Ouladbrahim, Abdelmoumin, Idir Belaidi, Samir Khatir, Erica Magagnini, Roberto Capozucca, and Magd Abdel Wahab. 2021. “Sensitivity Analysis of the GTN Damage Parameters at Different Temperature for Dynamic Fracture Propagation in X70 Pipeline Steel Using Neural Network.” FRATTURA ED INTEGRITA STRUTTURALE-FRACTURE AND STRUCTURAL INTEGRITY 58: 442–452. doi:10.3221/igf-esis.58.32.
Vancouver
1.
Ouladbrahim A, Belaidi I, Khatir S, Magagnini E, Capozucca R, Abdel Wahab M. Sensitivity analysis of the GTN damage parameters at different temperature for dynamic fracture propagation in X70 pipeline steel using neural network. FRATTURA ED INTEGRITA STRUTTURALE-FRACTURE AND STRUCTURAL INTEGRITY. 2021;58:442–52.
IEEE
[1]
A. Ouladbrahim, I. Belaidi, S. Khatir, E. Magagnini, R. Capozucca, and M. Abdel Wahab, “Sensitivity analysis of the GTN damage parameters at different temperature for dynamic fracture propagation in X70 pipeline steel using neural network,” FRATTURA ED INTEGRITA STRUTTURALE-FRACTURE AND STRUCTURAL INTEGRITY, vol. 58, pp. 442–452, 2021.
@article{8726345,
  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.}},
  author       = {{Ouladbrahim, Abdelmoumin and Belaidi, Idir and Khatir, Samir and Magagnini, Erica and Capozucca, Roberto and Abdel Wahab, Magd}},
  issn         = {{1971-8993}},
  journal      = {{FRATTURA ED INTEGRITA STRUTTURALE-FRACTURE AND STRUCTURAL INTEGRITY}},
  keywords     = {{Mechanical Engineering,Mechanics of Materials,Steel X70,Impact test (CVN),GTN parametersFEM,Artificial neural network}},
  language     = {{eng}},
  pages        = {{442--452}},
  title        = {{Sensitivity analysis of the GTN damage parameters at different temperature for dynamic fracture propagation in X70 pipeline steel using neural network}},
  url          = {{http://doi.org/10.3221/igf-esis.58.32}},
  volume       = {{58}},
  year         = {{2021}},
}
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