Fuzzy modelling based estimation of short circuit severity in pulse gas metal arc welding

Abstract

Avoiding short circuit is an essential condition for achieving good quality welds in pulse gas metal arc welding (GMAW-P). Estimating short circuit in any welding process is dependent on proper selection and optimisation of welding process parameters. Such optimisation is critical in the GMAW-P, wherein wire melting is closely dictated by numerous pulsing parameters in comparison to the conventional GMAW process. Fuzzy logic-based models are an excellent alternative in such situations, where a complex relationship between the large number of predictor variables (independents, inputs) and predicted variables (dependents, outputs) exist and are not easy to articulate in the usual terms of correlations or differences between groups. In this paper, we have proposed an input-output fuzzy model for estimating the short circuit severity in terms of number of shorts per pulse for the GMAW-P process. Eighteen factors representing the characteristics of the pulse waveforms are employed as predictor variables and the short circuit severity (or number of shorts per pulse) is predicted on the basis of a modified exponential membership function fitted to the fuzzy sets derived from predictor variables. The exponential membership function is modified by two structural parameters that are estimated by optimising the criterion function associated with the fuzzy modelling. The experimental data consists of GMAW-P welding of 6XXX group of aluminium alloys. The results demonstrate that the proposed fuzzy model could estimate the short circuit severity with high accuracy.

Keywords:

• Pulse gas metal arc welding (GMAW-P)
• shorts per pulse
• fuzzy modelling

Additional information

Notes on contributors

V K Madasu

Dr Vamsi Krishna Madasu obtained his Bachelor of Technology degree in Electronics & Communication Engineering with distinction from Jawaharlal Nehru Technological University, India, in 2002, and PhD in Electrical Engineering from the University of Queensland, Australia, in 2006. From 2006-2008, he was a Research Associate in the School of Engineering Systems at Queensland University of Technology, where he developed innovative image processing and fuzzy logic based technologies for diverse industrial applications. Currently, he is a Senior Research Officer at TetraQ, University of Queensland, working in the field of digital pathology image analysis. Vamsi is a member of IEEE, Computer Society, and is listed in Who’s Who in the World.

P K D V Yarlagadda

Prof Prasad Yarlagadda is currently the Director of Smart Systems Research and Professor in the School of Engineering Systems, Queensland University of Technology (QUT), Brisbane. He possesses over 30 years of experience in the area of materials, manufacturing and infomechatronics.