Modelling bead width and bead hardness in submerged arc welding using dimensional analysis
Abhijit Saha and
Subhas Chandra Mondal
International Journal of Manufacturing Technology and Management, 2022, vol. 36, issue 1, 13-27
Abstract:
The submerged arc welding (SAW) process finds wide industrial application due to its easy applicability, high current density and ability to deposit a large amount of weld metal using more than one wire at the same time. This paper presents the application of two techniques, namely, Rayleigh's dimensional analysis for modelling and grey relational analysis (GRA) for multi-objective optimisation for bead width and bead hardness in SAW based on welding parameters such as, welding current, arc voltage, welding speed and electrode stick out respectively. Based on the experimental result, it was concluded that GRA is suitable for the optimisation of multi-response problem. The best-fitting curves were obtained for experimentally observed values of both bead width and bead hardness on the basis of the Rayleigh's model. The comparison with experimental results will also served as further validation of the model. The proposed methodology can be applied to other manufacturing processes dealing multivariate data.
Keywords: submerged arc welding; SAW; Rayleigh's theorem; dimensional analysis; grey relational analysis; GRA; bead width; bead hardness. (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:
Downloads: (external link)
http://www.inderscience.com/link.php?id=121578 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:ids:ijmtma:v:36:y:2022:i:1:p:13-27
Access Statistics for this article
More articles in International Journal of Manufacturing Technology and Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().