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ISSN: 2090-4886

International Journal of Sensor Networks and Data Communications
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Research Article

Modeling Mechanical Properties of FSW Thick Pure Copper Plates andOptimizing It Utilizing Artificial Intelligence Techniques

Aydin Azizi1,2*, Ali Vatankhah Barenji2, Reza Vatankhah Barenji3and Majid Hashemipour2

1Department of Engineering, German University of Technology, Oman

2Department of Mechanical Engineering, Eastern Mediterranean University, Famagusta, Turkey

3Department of Industrial Engineering, Hacettepe University, Beytepe Campus, Ankara, Turkey

Corresponding Author:
Aydin Azizi
Department of Engineering German University of Technology, Oman
Tel: 96822061000
E-mail: [email protected]

Received April 28, 2016; Accepted May 18, 2016; Published May 25, 2016

Citation: Azizi A, Barenji AV, Barenji RV, Hashemipour M (2016) Modeling Mechanical Properties of FSW Thick Pure Copper Plates and Optimizing It Utilizing Artificial Intelligence Techniques. Sensor Netw Data Commun 5:142. doi:10.4172/2090-4886.1000142

Copyright: © 2016 Azizi A, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

Abstract

Friction stir welding (FSW) is an innovative solid state joining technique and has been employed in aerospace, rail, automotive and marine industries for joining aluminum, magnesium, zinc and copper alloys. In this process, parameters play a major role in deciding the weld quality these parameters. Using predictive modelling for mechanical properties of FSW not only reduce experiments but also is created standard model for predict outcomes. Therefore, this paper is undertaken to develop a model to predict the microstructure and mechanical properties of FSW. The proposed model is based on Ring Probabilistic logic Neural Network (RPLNN) and optimize it utilizing Genetic Algorithms (GA). The simulation results show that performance of the RPLNN algorithm with utilizing Genetic Algorithm optimizing technique compared to real data is reliable to deal with function approximation problems, and it is capable of achieving a solution in few convergence time steps with powerful and reliable results.

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