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- Table of Contents
Advances in Materials Science and Engineering
Volume 2013 (2013), Article ID 574914, 7 pages
Sensitivity Analysis of the Artificial Neural Network Outputs in Friction Stir Lap Joining of Aluminum to Brass
1Department of Automotive Engineering, Iran University of Science and Technology, Tehran 16846-13114, Iran
2Faculty of New Sciences and Technologies (FNST), University of Tehran, Tehran, Iran
3Department of Mechanical Engineering, Iranian Research Organization for Science and Technology (IROST), Tehran, Iran
Received 15 December 2012; Revised 26 February 2013; Accepted 26 February 2013
Academic Editor: Rui Vilar
Copyright © 2013 Mohammad Hasan Shojaeefard et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citations to this Article [4 citations]
The following is the list of published articles that have cited the current article.
- Mohammad Hasan Shojaeefard, Abollfazl Khalkhali, Mostafa Akbari, and Mojtaba Tahani, “Application of Taguchi Optimization Technique in Determining Aluminum to Brass Friction Stir Welding Parameters,” Materials & Design, 2013.
- Samaneh Sanjari, Abbas Naderifar, and Gholamreza Pazuki, “Modeling and Optimization of β-Cyclodextrin Production by Bacillus licheniformis using Artiﬁcial Neural Network and Genetic Algorithm,” Iranian Journal of Biotechnology, vol. 11, no. 4, pp. 223–232, 2013.
- Strahinja Z. Kovačević, Sanja O. Podunavac-Kuzmanović, Lidija R. Jevrić, Evgenija A. Djurendić, and Jovana J. Ajduković, “Non-linear Assessment of Anticancer Activity of 17-Picolyl and 17-Picolinylidene Androstane Derivatives – Chemometric Guidelines for Further Syntheses,” European Journal of Pharmaceutical Sciences, 2014.
- Mohammad Hasan Shojaeefard, Mostafa Akbari, and Parviz Asadi, “Multi Objective Optimization of Friction Stir Welding Parameters Using FEM and Neural Network,” International Journal of Precision Engineering and Manufacturing, vol. 15, no. 11, pp. 2351–2356, 2014.