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Advances in Materials Science and Engineering
Volume 2013 (2013), Article ID 574914, 7 pages
http://dx.doi.org/10.1155/2013/574914
Research Article

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.

Linked References

  1. A. Esmaeili, M. K. B. Givi, and H. R. Z. Rajani, “A metallurgical and mechanical study on dissimilar Friction Stir welding of aluminum 1050 to brass (CuZn30),” Materials Science and Engineering A, vol. 528, no. 22-23, pp. 7093–7102, 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. H. Okuyucu, A. Kurt, and E. Arcaklioglu, “Artificial neural network application to the friction stir welding of aluminum plates,” Materials & Design, vol. 28, no. 1, pp. 78–84, 2007. View at Publisher · View at Google Scholar · View at Scopus
  3. M. H. Shojaeefard, R. A. Behnagh, M. Akbari, M. K. B. Givi, and F. Farhani, “Modelling and Pareto optimization of mechanical properties of friction stir welded AA7075/AA5083 butt joints using neural network and particle swarm algorithm,” Materials & Design, vol. 44, pp. 190–198, 2013.
  4. P. Asadi, M. K. B. Givi, A. Rastgoo, M. Akbari, V. Zakeri, and S. Rasouli, “Predicting the grain size and hardness of AZ91/SiC nanocomposite by artificial neural networks,” International Journal of Advanced Manufacturing Technology, vol. 63, pp. 1095–1107, 2012.
  5. M. Lu, S. M. AbouRizk, and U. H. Hermann, “Sensitivity analysis of neural networks in spool fabrication productivity studies,” Journal of Computing in Civil Engineering, vol. 15, no. 4, pp. 299–308, 2001. View at Publisher · View at Google Scholar · View at Scopus
  6. Y. Dimopoulos, P. Bourret, and S. Lek, “Use of some sensitivity criteria for choosing networks with good generalization ability,” Neural Processing Letters, vol. 2, no. 6, pp. 1–4, 1995. View at Publisher · View at Google Scholar · View at Scopus
  7. G. D. Garson, “Interpreting neural-network connection weights,” AI Expert, vol. 6, pp. 46–51, 1991.
  8. S. Lek, A. Belaud, P. Baran, I. Dimopoulos, and M. Delacoste, “Role of some environmental variables in trout abundance models using neural networks,” Aquatic Living Resources, vol. 9, no. 1, pp. 23–29, 1996. View at Scopus
  9. G. R. Balls, D. Palmer-Brown, and G. E. Sanders, “Investigating microclimatic influences on ozone injury in clover (Trifolium subterraneum) using artificial neural networks,” New Phytologist, vol. 132, no. 2, pp. 271–280, 1996. View at Scopus
  10. M. Akbari and R. Abdi Behnagh, “Dissimilar friction-stir lap joining of 5083 aluminum alloy to CuZn34 brass,” Metallurgical and Materials Transactions B, vol. 43, pp. 1177–1186, 2012.
  11. W. Y. K. Chiang, D. Zhang, and L. Zhou, “Predicting and explaining patronage behavior toward web and traditional stores using neural networks: a comparative analysis with logistic regression,” Decision Support Systems, vol. 41, no. 2, pp. 514–531, 2006. View at Publisher · View at Google Scholar · View at Scopus
  12. S. Dutta and J. P. Gupta, “PVT correlations for Indian crude using artificial neural networks,” Journal of Petroleum Science and Engineering, vol. 72, no. 1-2, pp. 93–109, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. M. Gevrey, I. Dimopoulos, and S. Lek, “Review and comparison of methods to study the contribution of variables in artificial neural network models,” Ecological Modelling, vol. 160, no. 3, pp. 249–264, 2003. View at Publisher · View at Google Scholar · View at Scopus
  14. T. Tchaban, M. J. Taylor, and J. P. Griffin, “Establishing impacts of the inputs in a feedforward neural network,” Neural Computing and Applications, vol. 7, no. 4, pp. 309–317, 1998. View at Scopus
  15. J. J. Montaño and A. Palmer, “Numeric sensitivity analysis applied to feedforward neural networks,” Neural Computing and Applications, vol. 12, pp. 119–125, 2003.
  16. W. Wang, P. Jones, and D. Partridge, “Assessing the impact of input features in a feedforward neural network,” Neural Computing and Applications, vol. 9, no. 2, pp. 101–112, 2000. View at Scopus