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...
Saved in:
| Main Authors: | , , , |
|---|---|
| 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 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832558384745807872 |
|---|---|
| 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. |
| format | Article |
| id | doaj-art-5108aa2ed3624c48b80aa55317d13d9e |
| institution | Kabale University |
| issn | 1687-8434 1687-8442 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| 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 |
| work_keys_str_mv | AT mohammadhasanshojaeefard sensitivityanalysisoftheartificialneuralnetworkoutputsinfrictionstirlapjoiningofaluminumtobrass AT mostafaakbari sensitivityanalysisoftheartificialneuralnetworkoutputsinfrictionstirlapjoiningofaluminumtobrass AT mojtabatahani sensitivityanalysisoftheartificialneuralnetworkoutputsinfrictionstirlapjoiningofaluminumtobrass AT foadfarhani sensitivityanalysisoftheartificialneuralnetworkoutputsinfrictionstirlapjoiningofaluminumtobrass |