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:: Volume 7, Issue 2 (Manufacturing and Production 2012) ::
2012, 7(2): 45-56 Back to browse issues page
Influence of Shielded Metal Arc Welding Parameters on the Bead Geometry, Based on Artificial Neural Network
Abstract:   (5073 Views)
This paper presents an artificial neural network simulation, based on 216 set of experimental data for prediction of SMAW bead geometry. Input parameters consist of electrode type, current, travel speed, arc length, transverse motion, and angle of the electrode with respect to the vertical plane along the weld line. outputs of the neural network were bead width, bead height, penetration, and the rate of electrode consumption. The Error analysis in the neural network was performed by well-known back-propagation error reduction method containing two hidden layers. The results showed that the results were basically in good agreements with the available experimental data, indicating a suitable technique for simulation of the SMAW. It is concluded that the established neural network system, in conjunction with back-propagation analysis, is able to determine the bead geometry for a set of specified input parameters, facilitating the design of special purpose arc welds.
Keywords: Artificial Neural Network, Shielded Metal Arc Welding, Bead Geometry, Penetration Depth
Full-Text [PDF 267 kb]   (5256 Downloads)    
Type of Study: Research | Subject: Manufacturing And Production
Received: 2012/02/13
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Influence of Shielded Metal Arc Welding Parameters on the Bead Geometry, Based on Artificial Neural Network. Aerospace Mechanics Journal. 2012; 7 (2) :45-56
URL: http://measej.ir/article-1-1007-en.html


Volume 7, Issue 2 (Manufacturing and Production 2012) Back to browse issues page
                         مجله علمی-پژوهشی مکانیک هوافضا                                Aerospace Mechanics  Journal Aerospace Mechanics Journal
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