A New Approach for Predicting and Optimizing Weld Bead Geometry in GMAW
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.
[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.
[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.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:62473", author = "Farhad Kolahan and Mehdi Heidari", title = "A New Approach for Predicting and Optimizing Weld Bead Geometry in GMAW", 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", volume = "3", number = "11", pages = "1434-4", }