alexa Abstract | Modeling Mechanical Properties of FSW Thick Pure Copper Plates and Optimizing It Utilizing Artificial Intelligence Techniques
ISSN: 2090-4886

International Journal of Sensor Networks and Data Communications
Open Access

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Research Article Open Access


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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Author(s): Aydin Azizi*, Ali Vatankhah Barenji, Reza Vatankhah Barenji and Majid Hashemipour


Friction stir welding, Artificial intelligence, Modeling, Optimization, Ring probabilistic logic, Neural networks, Genetic algorithms, Neural networks

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