Publication Type:

Journal Article

Source:

Arabian Journal for Science and Engineering, Springer Verlag, Volume 42, Number 11, p.4649-4665 (2017)

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031508431&doi=10.1007%2fs13369-017-2609-9&partnerID=40&md5=d82d9ae128b0cc3e7c5964bbf951131c

Abstract:

Welding is one of the major metal-joining process employed in fabrication industries, especially in manufacturing of boilers and pressure vessels. Control of weld quality is very important for such industries considering the severe operating conditions. Industries are looking for some kind of real-time process monitoring/control that will ensure the weld quality online and prevent the occurrence of defects. In this paper an attempt is made to establish a correlation between the current and voltage signatures with the good weld and weld with porosity and burn through defect during the welding of carbon steel using gas metal arc welding (GMAW) process. Experimental setup has been established and experiments were conducted using a welding robot integrated with GMAW power source. The experimental setup includes online current and voltage sensors, data loggers, and signal processing hardware and software. Welding conditions are carefully designed to produce good weld and weld with defects such as burn through and porosity. Current and voltage signatures are captured using data acquisition system (DAS). Software has been developed to analyze the data captured by the DAS. Statistical methods are employed to study the transient data. The probability density distributions of the current and voltage signature demonstrates a good correspondence between the current and voltage signatures with the welding defect. © 2017, King Fahd University of Petroleum & Minerals.

Notes:

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Cite this Research Publication

A. Sumesh, Rameshkumar, K., Raja, A., Mohandas, K., Santhakumari, A., and Shyambabu, R., “Establishing Correlation Between Current and Voltage Signatures of the Arc and Weld Defects in GMAW Process”, Arabian Journal for Science and Engineering, vol. 42, pp. 4649-4665, 2017.

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