Publisher: Al-Kindi Center for Research and Development (Total: 14 journals)   [Sort by number of followers]

Showing 1 - 14 of 14 Journals sorted alphabetically
Intl. J. of Biological, Physical and Chemical Studies     Open Access   (Followers: 2)
Intl. J. of English Language Studies     Open Access   (Followers: 3)
Intl. J. of Law and Politics Studies     Open Access   (Followers: 2)
Intl. J. of Linguistics, Literature and Translation     Open Access   (Followers: 4)
J. of Business and Management Studies     Open Access   (Followers: 4)
J. of Computer Science and Technology Studies     Open Access   (Followers: 2)
J. of Economics, Finance and Accounting Studies     Open Access   (Followers: 2)
J. of English Language Teaching and Applied Linguistics     Open Access   (Followers: 1)
J. of Environmental and Agricultural Studies     Open Access   (Followers: 1)
J. of Humanities and Social Sciences Studies     Open Access   (Followers: 3)
J. of Mathematics and Statistics Studies     Open Access   (Followers: 3)
J. of Mechanical, Civil and Industrial Engineering     Open Access   (Followers: 1)
J. of Medical and Health Studies     Open Access   (Followers: 1)
J. of World Englishes and Education Practices     Open Access  
Similar Journals
Journal Cover
Journal of Mechanical, Civil and Industrial Engineering
Number of Followers: 1  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2710-1436
Published by Al-Kindi Center for Research and Development Homepage  [14 journals]
  • Determination of GTN Model Parameters Based on Artificial Neutral Network
           for a Ductile Failure

    • Authors: YASSINE CHAHBOUB, SZAVAI Szabolcs
      Pages: 01 - 05
      Abstract: The Gurson – Tvergaard – Needleman (GTN) mechanical model is widely used to predict the failure of materials based on laboratory specimens, direct identification of Gurson – Tvergaard – Needleman parameters is not easy and time-consuming, and the most used method to determine them is the combination between the experimental results and those of the finite elements, the process consists of repeating the simulations several times until the simulation data matches the experimental data obtained at the specimen level.
      This article aims to find GTN parameters for the Compact Tension (CT) and Single Edge Tensile Test (SENT) specimen based on the Notch Specimen (NT) using the Artificial Neural Network (ANN) approach. . This work presents how the ANN could help us determine the parameters of GTN in a very short period of time. The results obtained show that ANN is an excellent tool for determining GTN parameters.
      PubDate: 2021-01-15
      DOI: 10.32996/jmcie.2021.2.1.1
      Issue No: Vol. 2, No. 1 (2021)
       
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


Your IP address: 3.239.56.184
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-