Modelling and Pareto optimization of mechanical properties of friction stir welded AA7075/AA5083 butt joints using neural network and particle swarm algorithm

• Published in: Materials in engineering 2013-02, Vol.44, p.190-198
• Main Authors: Shojaeefard, Mohammad Hasan, Behnagh, Reza Abdi, Akbari, Mostafa, Givi, Mohammad Kazem Besharati, Farhani, Foad
• Format: Article
• Language: English
• Subjects: Aluminum base alloys; Computer simulation; Friction stir welding; Frictions Stir Welding; Learning theory; Mathematical models; Mechanical properties; Microstructure; Multi-objective optimization; Neural networks; Welded joints
• ISSN: 0261-3069
• DOI: 10.1016/j.matdes.2012.07.025

► Defect-free friction stir welds have been produced for AA5083-O/AA7075-O. ► Back-propagation was sufficient for predicting hardness and tensile strength. ► A hybrid multi-objective algorithm is proposed to deal with this MOP. ► Multi-objective particle swarm optimization was used to find the Pareto solutions. ► TOPSIS is used to rank the given alternatives of the Pareto solutions. Friction Stir Welding (FSW) has been successfully used to weld similar and dissimilar cast and wrought aluminium alloys, especially for aircraft aluminium alloys, that generally present with low weldability by the traditional fusion welding process. This paper focuses on the microstructural and mechanical properties of the Friction Stir Welding (FSW) of AA7075-O to AA5083-O aluminium alloys. Weld microstructures, hardness and tensile properties were evaluated in as-welded condition. Tensile tests indicated that mechanical properties of the joint were better than in the base metals. An Artificial Neural Network (ANN) model was developed to simulate the correlation between the Friction Stir Welding parameters and mechanical properties. Performance of the ANN model was excellent and the model was employed to predict the ultimate tensile strength and hardness of butt joint of AA7075–AA5083 as functions of weld and rotational speeds. The multi-objective particle swarm optimization was used to obtain the Pareto-optimal set. Finally, the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) was applied to determine the best compromised solution.