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International Journal of Technology
Year : 2018, Volume : 8, Issue : 1
First page : ( 41) Last page : ( 46)
Print ISSN : 2231-3907. Online ISSN : 2231-3915.
Article DOI : 10.5958/2231-3915.2018.00007.X

Quality improvement in submerged arc welding process using taguchi method and regression analysis

Daharwal Praful1,*, Awchat Prashant2

1Student, M. Tech., G.H. Raisoni Academy of Engineering and Technology, Nagpur

2Professor, G.H. Raisoni Academy of Engineering and Technology, Nagpur

*Corresponding Author E-mail: prafuldaharwal@gmail.com

Online published on 21 August, 2018.

Abstract

Welding process for joining similar and different materials is one of the economical and is a much faster process of joining. Because of its reliability, submerge arc welding is one of the chief metal joining process employed in industry. With the proper selection of equipment, submerge arc welding can be applied to a wide range of industrial applications. The high quality of welds, the high depositional rates, the deep penetration and the adaptability to automatic operation makes it the process particularly suitable for fabrication of large weldments. The quality of SAW is mainly influenced by independent variables such as welding current, voltage, and travel speed and electrode stickout. The automation of SAW application is often desirable due to somewhat unpleasant working condition that the process creates. The application of Taguchi techniques and regression analysis to determine the optimal process parameters for the submerge arc welding and the quality improvement. The quality engineering method of Taguchi employing of experiment provides are efficient and systematically way to optimise design for performing, quality and cost. It is one of the most important statical tools for designing high quality system at reduced cost. The use of Taguchi method simplifies the optimization procedure for determining the optimal welding parameter in the submerge arc welding.

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Keywords

Submerge arc welding, Fusion welding, Autogenous, Homogeneous, Regression Analysis, Quality management.

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