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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 [19 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.
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- Yeshona Sewsynker, Evariste Bosco Gueguim Kana, and Agbaje Lateef, “Modelling of biohydrogen generation in microbial electrolysis cells (MECs) using a committee of artificial neural networks (ANNs),” Biotechnology & Biotechnological Equipment, pp. 1–8, 2015.
- Strahinja Z. Kovačević, Sanja O. Podunavac-Kuzmanović, and Lidija R. Jevrić, “Linear and Nonlinear Structure–Retention Relationship Analysis of Different Classes of Pesticides Isolated From Groundwater,” Journal of Liquid Chromatography & Related Technologies, pp. 150610064856003, 2015.
- Farhana Tisa, Meysam Davoody, Abdul Aziz Abdul Raman, and Wan Mohd Ashri Wan Daud, “Degradation and Mineralization of Phenol Compounds with Goethite Catalyst and Mineralization Prediction Using Artificial Intelligence,” Plos One, vol. 10, no. 4, 2015.
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- Halil Ibrahim Kurt, and Murat Oduncuoglu, “Effects of Temperature, Time, Magnesium, and Copper on the Wettability of Al/TiC System,” Mathematical Problems in Engineering, vol. 2015, pp. 1–6, 2015.
- Chiara Zecchin, Andrea Facchinetti, Giovanni Sparacino, and Claudio Cobelli, “How Much Is Short-Term Glucose Prediction in Type 1 Diabetes Improved by Adding Insulin Delivery and Meal Content Information to CGM Data? A Proof-of-Concept Study,” Journal of Diabetes Science and Technology, vol. 10, no. 5, pp. 1149–1160, 2016.
- Katarzyna Pentoś, “The methods of extracting the contribution of variables in artificial neural network models – Comparison of inherent instability,” Computers and Electronics in Agriculture, vol. 127, pp. 141–146, 2016.
- Wen-Ping Tsai, Yen-Ming Chiang, Jun-Lin Huang, and Fi-John Chang, “Exploring the Mechanism of Surface and Ground Water through Data-Driven Techniques with Sensitivity Analysis for Water Resources Management,” Water Resources Management, 2016.
- Strahinja Z. Kovačević, Sanja O. Podunavac-Kuzmanović, Lidija R. Jevrić, Vladimir R. Vukić, Marina P. Savić, and Evgenija A. Djurendić, “Preselection of A- and B- modified d-homo lactone and d-seco androstane derivatives as potent compounds with antiproliferative activity against breast and prostate cancer cells – QSAR approach and molecular docking analysis,” European Journal of Pharmaceutical Sciences, 2016.
- Strahinja Z. Kovačević, Sanja O. Podunavac-Kuzmanović, Lidija R. Jevrić, Pavle T. Jovanov, Evgenija A. Djurendić, and Jovana J. Ajduković, “Comprehensive QSRR modeling as a starting point in characterization and further development of anticancer drugs based on 17α-picolyl and 17(E)-picolinylidene androstane structures,” European Journal of Pharmaceutical Sciences, 2016.
- Yeshona Sewsynker, and Evariste Bosco Gueguim Kana, “Intelligent models to predict hydrogen yield in dark microbial fermentations using existing knowledge,” International Journal of Hydrogen Energy, 2016.
- Ashkan Nabavi-Pelesaraei, Shahin Rafiee, Homa Hosseinzadeh-Bandbafha, and Shahaboddin Shamshirband, “Modeling energy consumption and greenhouse gas emissions for kiwifruit production using artificial neural networks,” Journal of Cleaner Production, 2016.
- Luigi De Filippis, Livia Serio, Francesco Facchini, Giovanni Mummolo, and Antonio Ludovico, “Prediction of the Vickers Microhardness and Ultimate Tensile Strength of AA5754 H111 Friction Stir Welding Butt Joints Using Artificial Neural Network,” Materials, vol. 9, no. 11, pp. 915, 2016.
- Yeshona Sewsynker-Sukai, and Evariste Bosco Gueguim Kana, “Does the volume matter in bioprocess model development? An insight into modelling and optimization of biohydrogen production,” International Journal of Hydrogen Energy, 2017.