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.
Highlights
Special properties of copper such as high electrical and thermal conductivities, good combinations of strength and ductility, and excellent resistance to corrosion have made it an excellent applicant to be utilized in industrial areas
High thermal conductivity of copper causes the need for higher heat input during conventional fusion welding, which results in large distortion, solidification cracking, and high oxidation rate
We introduce the Ring Probabilistic logic Neural Network (RPLNN) by employing the concept of Probabilistic Logic Neuron (PLN) as powerful artificial intelligence technique that has been frequently used in pattern recognition problems [16,17]
Summary
Special properties of copper such as high electrical and thermal conductivities, good combinations of strength and ductility, and excellent resistance to corrosion have made it an excellent applicant to be utilized in industrial areas. Friction stir welding (FSW) which requires lower heat input for joining of the copper and copper alloys can overcome this problem [1,2]. Friction stir processing (FSP) is a new metal working method for producing surface composites, which is based on the concept of FSW [5]. During FSP, the stirred material undergoes severe plastic deformation. The material flow associated with stirring and severe plastic deformation can be used for bulk alloy modification by mixing in a second element. This mixing is followed by the precipitation of a second phase, distribution of fine particles of the second element, increased density of defects, and so forth. The stirred zone becomes a metal matrix composite with an improved hardness and wear resistance
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