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Abstract
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Article Information:
Comparing the Regression Analysis and Artificial Neural Network in Modeling the Submerged Arc Welding (SAW) Process
Hossein Towsyfyan, Gholamreza Davoudi, Bahram Heidarian Dehkordy and Ahmad Kariminasab
Corresponding Author: Gholamreza Davoudi
Submitted: September 17, 2012
Accepted: October 24, 2012
Published: March 20, 2013 |
Abstract:
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Complexities of submerged arc welding variables on the one hand and its widespread use in producing the sensitive and expensive parts on the other hand have doubled the importance of precise control of its adjusting parameters. In general, in order to create high-quality joints in welding processes it is necessary to control three parameters of welding current, voltage and speed precisely from various variables. On this basis, the mentioned variables have been considered as the criteria for quality of the weld joints in this study as the adjusting parameters and weld bead geometry, which include the bead height, width and penetration. Thus, the accurate equations have been proposed for estimating the weld bead height, width and penetration based on the input parameters by the regression analysis and neural network. Based on the results, the designed neural network is markedly more accurate than the regression equations, but both models have high capabilities for optimizing the parameters of submerged arc welding and also predicting the weld bead geometry for a set of input values.
Key words: Neural Network, regression, submerged arc welding, , , ,
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Cite this Reference:
Hossein Towsyfyan, Gholamreza Davoudi, Bahram Heidarian Dehkordy and Ahmad Kariminasab, . Comparing the Regression Analysis and Artificial Neural Network in Modeling the Submerged Arc Welding (SAW) Process. Research Journal of Applied Sciences, Engineering and Technology, (09): 2701-2706.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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