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Modeling mechanical properties of FSW thick pure copper plates and optimizing it utilizing artificial intelligence techniques
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International Journal of Sensor Networks and Data Communications

ISSN: 2090-4886

Open Access

Modeling mechanical properties of FSW thick pure copper plates and optimizing it utilizing artificial intelligence techniques


2nd International Conference and Business Expo on Wireless & Telecommunication

April 21-22, 2016 Dubai, UAE

Aydin Azizi, Ali Vatankhah Barenji, Reza Vatankhah Barenji and Majid Hashemipour

German University of Technology in Oman
Eastern Mediterranean University, Turkey
Hacettepe University, Turkey

Posters & Accepted Abstracts: Sensor Netw Data Commun

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.

Biography :

Email: Aydin.Azizi@gutech.edu.om

Google Scholar citation report
Citations: 343

International Journal of Sensor Networks and Data Communications received 343 citations as per Google Scholar report

International Journal of Sensor Networks and Data Communications peer review process verified at publons

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