Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30184
A New Approach for Predicting and Optimizing Weld Bead Geometry in GMAW

Authors: Farhad Kolahan, Mehdi Heidari

Abstract:

Gas Metal Arc Welding (GMAW) processes is an important joining process widely used in metal fabrication industries. This paper addresses modeling and optimization of this technique using a set of experimental data and regression analysis. The set of experimental data has been used to assess the influence of GMAW process parameters in weld bead geometry. The process variables considered here include voltage (V); wire feed rate (F); torch Angle (A); welding speed (S) and nozzle-to-plate distance (D). The process output characteristics include weld bead height, width and penetration. The Taguchi method and regression modeling are used in order to establish the relationships between input and output parameters. The adequacy of the model is evaluated using analysis of variance (ANOVA) technique. In the next stage, the proposed model is embedded into a Simulated Annealing (SA) algorithm to optimize the GMAW process parameters. The objective is to determine a suitable set of process parameters that can produce desired bead geometry, considering the ranges of the process parameters. Computational results prove the effectiveness of the proposed model and optimization procedure.

Keywords: Weld Bead Geometry, GMAW welding, Processparameters Optimization, Modeling, SA algorithm

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1081165

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1828

References:


[1] N. Christensen, V. Davies, & K. Gjermundsen, "Distribution of temperature in arc welding", Br Weld J vol.12(2), pp.54-75, 1965.
[2] R.S. Chandel, H.P. Seow, F.L. Cheong, "Effect of increasing deposition rate on the bead geometry of submerged arc welds", J Mater Process Technol, vol.72, pp.124-128, 1997.
[3] F. Markelj, J. Tusek, "Algorithmic optimization of parameters in tungsten inert gas welding of stainless-steel sheet", Sci Technol Weld Join vol.6(6), pp.375-382, 2001.
[4] I.S. Kim, Y.J. Jeong, I.J. Son, I. J. Kim, J.Y. Kim,I.K. Kim, P.K. Yarlagadda, "Sensitivity analysis for process parameters influencing weld quality in robotic GMA welding process", J Mater Process Technol vol.140, pp.676-681, 2003.
[5] I.S. Kim, K.J. Son, Y.S. Yang, P.K. Yarlagadda, "Sensitivity analysis for process parameters in GMAwelding processes using a factorial design method", Int J Mach Tools Manuf, vol.43, pp.763- 769, 2003.
[6] D.C. Montgomery, E.A. Peck, G.G. Vining, "Introduction to Linear Regression Analysis". third ed., Wiley, New York, 2003.
[7] S. Kirkpatrick, C. Gelatt, & M. Vecchi, "Optimization by simulated annealing". Science, vol.220, pp.671-680, 1983.