Multi objective optimization of friction stir welding parameters using FEM and neural network

In this study the inuence of rotational and traverse speed on the friction stir welding of AA5083 aluminum alloy has been investigated. For this purpose a thermo-mechanically coupled, 3D FEM analysis was used to study the effect of rotational and traverse speed on welding force, peak temperature and...

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Bibliographic Details
Published in:International journal of precision engineering and manufacturing 2014-11, Vol.15 (11), p.2351-2356
Main Authors: Shojaeefard, Mohammad Hasan, Akbari, Mostafa, Asadi, Parviz
Format: Article
Language:English
Subjects:
Engineering
Industrial and Production Engineering
Materials Science
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Summary:In this study the inuence of rotational and traverse speed on the friction stir welding of AA5083 aluminum alloy has been investigated. For this purpose a thermo-mechanically coupled, 3D FEM analysis was used to study the effect of rotational and traverse speed on welding force, peak temperature and HAZ width. Then, an Articial Neural Network (ANN) model was employed to understand the correlation between the welding parameters (rotational and traverse speed) and peak temperature, HAZ width and welding force values in the weld zone. Performance of the ANN model was found excellent and the model can be used to predict peak temperature, HAZ width and welding force. Furthermore, in order to find optimum values of traverse and rotational speed, the multi-objective optimization was used to obtain the Pareto front. Finally, the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) was employed to obtain the best compromised solution.
ISSN:2234-7593
2005-4602
DOI:10.1007/s12541-014-0600-x