A fuzzy-regression-PSO based hybrid method for selecting welding conditions in robotic gas metal arc welding
ISSN: 0144-5154
Article publication date: 4 May 2020
Issue publication date: 17 June 2020
Abstract
Purpose
This paper aims to propose fuzzy-regression-particle swarm optimization (PSO) based hybrid optimization approach for getting maximum weld quality in terms of weld strength and bead depth of penetration.
Design/methodology/approach
The prediction of welding quality to achieve best of it is not possible by any single optimization technique. Therefore, fuzzy technique has been applied to predict the weld quality in terms of weld strength and weld bead geometry in combination with a multi-performance characteristic index (MPCI). Then regression analysis has been applied to develop relation between the MPCI output value and the input welding process parameters. Finally, PSO method has been used to get the optimal welding condition by maximizing the MPCI value.
Findings
The predicted weld quality or the MPCI values in terms of combined weld strength and bead geometry has been found to be highly co-related with the weld process parameters. Therefore, it makes the process easy for setting of weld process parameters for achieving best weld quality, as there is no need to finding the relation for individual weld quality parameter and weld process parameters although they are co-related in a complicated manner.
Originality/value
In this paper, a new hybrid approach for predicting the weld quality in terms of both mechanical properties and weld geometry and optimizing the same has been proposed. As these parameters are highly correlated and dependent on the weld process parameters the proposed approach can effectively analyzing the ambiguity and significance of each process and performance parameter.
Keywords
Citation
Rout, A., Bbvl, D., Biswal, B.B. and Mahanta, G.B. (2020), "A fuzzy-regression-PSO based hybrid method for selecting welding conditions in robotic gas metal arc welding", Assembly Automation, Vol. 40 No. 4, pp. 601-612. https://doi.org/10.1108/AA-12-2019-0223
Publisher
:Emerald Publishing Limited
Copyright © 2020, Emerald Publishing Limited