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Advances in Materials Science and Engineering
Volume 2013, Article ID 574914, 7 pages
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.


Al-Mg and CuZn34 alloys were lap joined using friction stir welding while the aluminum alloy sheet was placed on the CuZn34. In addition, the mechanical properties of each sample were characterized using shear tests. Scanning electron microscopy (SEM) and X-ray diffraction analysis were used to probe chemical compositions. An artificial neural network model was developed to simulate the correlation between the Friction Stir Lap Welding (FSLW) parameters and mechanical properties. Subsequently, a sensitivity analysis was performed to investigate the effect of each input parameter on the output in terms of magnitude and direction. Four methods, namely, the “PaD” method, the “Weights” method, the “Profile” method, and the “backward stepwise” method, which can give the relative contribution and/or the contribution profile of the input factors, were compared. The PaD method, giving the most complete results, was found to be the most useful, followed by the Profile method that gave the contribution profile of the input variables.