World Academy of Science, Engineering and Technology
Published November 21, 2009 | Version 13458
Journal article Open

A New Approach for Predicting and Optimizing Weld Bead Geometry in GMAW

Description

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.

Files

13458.pdf

Files (273.2 kB)

Name Size Download all
md5:39d57fa2c3efa73d97190d92156f3302
273.2 kB Preview Download

Additional details

References

  • N. Christensen, V. Davies, & K. Gjermundsen, "Distribution of temperature in arc welding", Br Weld J vol.12(2), pp.54-75, 1965.
  • 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.
  • 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.
  • 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.
  • 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.
  • D.C. Montgomery, E.A. Peck, G.G. Vining, "Introduction to Linear Regression Analysis". third ed., Wiley, New York, 2003.
  • S. Kirkpatrick, C. Gelatt, & M. Vecchi, "Optimization by simulated annealing". Science, vol.220, pp.671-680, 1983.