Parametric Design for Purged Submerged Arc Welding on Strength of Low Carbon Steel
Mamunuri Sailender,
G Chandra Mohan Reddy and
S Venkatesh
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Mamunuri Sailender: Aircraft Research and Design Center, Bangalore, India
G Chandra Mohan Reddy: Mahatma Gandhi Institute of Technology, Hyderabad, TS
S Venkatesh: Mechanical Engineering Department, University College of Engineering, Osmania University, Hyderabad, TS
European Journal of Engineering and Technology Research, 2018, vol. 1, issue 3, 1-6
Abstract:
Low carbon steel is most widely used materials in the industry for moderate and service requirements. For strong and continuous weld, submerged arc welding (SAW) is widely used process in fabrication industry. It has inherent advantages like deep penetration, smooth bead, reliability and high quality. It is prominent with large number of process parameters, which act together in an intricate manner and subsequently influence the output performance. So, it is important to select process parameters for maintaining the required quality. In the present work, parametric optimization of main parameters, such as open circuit voltage (OCV), wire feed rate (WFR), welding speed (WS) and nozzle-to-plate distance (NPD) and thereby to study influences on the weldment strength. Experiments are conducted using Taguchi’s L9 orthogonal array. The study is made through conducting experiments in ‘purging with gas’ condition and compared with traditional ‘as weld’ experiments. The performance measure of control levels to select is made from S/N ratio. Through ANOVA, significance of the parameter and their contribution is calculated. The correlations between process parameters and performance outputs are established using Regression Analysis. Mathematical models are developed; checked for their adequacy using F-test and determined quantitatively, same presented graphically. These models are validated by confirmation tests and the predicted results are within the limits.
Keywords: ANOVA; Orthogonal array; Regression Analysis; Submerged Arc Welding (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:epw:ejeng0:v:1:y:2018:i:3:id:60132
DOI: 10.24018/ejeng.2016.1.3.132
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