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Research Article
Friction stir-welding of AZ31B Mg and 6061-T6 Al alloys optimization using Box-Behnken design (BBD) and Artificial Neural network (ANN)
Dame Alemayehu Efa1, Endalkachew Mosisa Gutema1, Hirpa G. Lemu2, Mahesh Gopal1
1Dept. of Mechanical Eng., College of Eng. and Technology, Wollega University, Nekemte, Ethiopia
2Dept. of Mechanical and Structural Eng. and Materials Science, Faculty of Science and Technology, University of Stavanger, N-4036 Stavanger, Norway
Keywords
Abstract
Box-Behnken Design;
Response Surface Methodology;
Friction Stir-Welding;
COMSOL Multiphysics® 6.0 Software;
Artificial Neural Network;
Peak Temperature
The primary goal of the study is to optimize the welding parameters using Friction Stir Welding (FSW) to join AZ31B Mg and AA 6061 alloys considering input parameters such as rotational speed, welding speed, shoulder-to-pin diameter ratio and plunge force and output parameters as peak temperature. The simulation experiment is carried out using COMSOL Multiphysics® 6.0 Software. The simulation experiment is designed using the Box-Behnken design (BBD) of Response Surface Methodology (RSM) and mathematical models were developed. The Analysis of Variance (ANOVA) is used to assess the features of the performance effectiveness of the parameters. Both direct and indirect interaction effects are investigated; the results indicate that the rotational speed is the most influential parameter when compared to other factors; as rotational speed increases consequently; there is an increase in temperature. Finally, the Artificial Neural Network was trained and tested in MATLAB software to optimize the parameters. The validation was performed to predict the minimal predicted temperature value. The confirmatory tests reveal that the predicted results are extremely close to the experimental values from the simulation.
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