Sensitivity Analysis of the Artificial Neural Network Outputs in Friction Stir Lap Joining of Aluminum to Brass

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 prob...

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Main Authors: Mohammad Hasan Shojaeefard, Mostafa Akbari, Mojtaba Tahani, Foad Farhani
Format: Article
Language:English
Published: Wiley 2013-01-01
Series:Advances in Materials Science and Engineering
Online Access:http://dx.doi.org/10.1155/2013/574914
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author Mohammad Hasan Shojaeefard
Mostafa Akbari
Mojtaba Tahani
Foad Farhani
author_facet Mohammad Hasan Shojaeefard
Mostafa Akbari
Mojtaba Tahani
Foad Farhani
author_sort Mohammad Hasan Shojaeefard
collection DOAJ
description 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.
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institution Kabale University
issn 1687-8434
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spelling doaj-art-5108aa2ed3624c48b80aa55317d13d9e2025-02-03T01:32:32ZengWileyAdvances in Materials Science and Engineering1687-84341687-84422013-01-01201310.1155/2013/574914574914Sensitivity Analysis of the Artificial Neural Network Outputs in Friction Stir Lap Joining of Aluminum to BrassMohammad Hasan Shojaeefard0Mostafa Akbari1Mojtaba Tahani2Foad Farhani3Department of Automotive Engineering, Iran University of Science and Technology, Tehran 16846-13114, IranDepartment of Automotive Engineering, Iran University of Science and Technology, Tehran 16846-13114, IranDepartment of Automotive Engineering, Iran University of Science and Technology, Tehran 16846-13114, IranDepartment of Mechanical Engineering, Iranian Research Organization for Science and Technology (IROST), Tehran, IranAl-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.http://dx.doi.org/10.1155/2013/574914
spellingShingle Mohammad Hasan Shojaeefard
Mostafa Akbari
Mojtaba Tahani
Foad Farhani
Sensitivity Analysis of the Artificial Neural Network Outputs in Friction Stir Lap Joining of Aluminum to Brass
Advances in Materials Science and Engineering
title Sensitivity Analysis of the Artificial Neural Network Outputs in Friction Stir Lap Joining of Aluminum to Brass
title_full Sensitivity Analysis of the Artificial Neural Network Outputs in Friction Stir Lap Joining of Aluminum to Brass
title_fullStr Sensitivity Analysis of the Artificial Neural Network Outputs in Friction Stir Lap Joining of Aluminum to Brass
title_full_unstemmed Sensitivity Analysis of the Artificial Neural Network Outputs in Friction Stir Lap Joining of Aluminum to Brass
title_short Sensitivity Analysis of the Artificial Neural Network Outputs in Friction Stir Lap Joining of Aluminum to Brass
title_sort sensitivity analysis of the artificial neural network outputs in friction stir lap joining of aluminum to brass
url http://dx.doi.org/10.1155/2013/574914
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AT mojtabatahani sensitivityanalysisoftheartificialneuralnetworkoutputsinfrictionstirlapjoiningofaluminumtobrass
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