A Methodology for Control of the Robotic Welding Process Using Infrared Sensors

Abstract:

Article Preview

With the advance of the robotic arc welding process, procedure optimization which selects the welding procedure and predicts bead geometry that will be deposited is increased. A major concern involving procedure optimization should define a welding procedure which can be shown to be the best with respect to some standard and chosen combination of process parameters which give an acceptance balance between production rate and the scope of defects for a given situation. In this paper, bead-on-plate welding using an infrared thermography during robotic CO2 welding process has been conducted to obtain the thermal profile characteristics and develop the empirical models which influence process parameters on bead width with the help of a standard statistical package program SAS. The comparison with values of coefficient of multiple correlations for curvilinear and linear equations presents no differences, which indicate that the developed equations are reasonably suitable.

Info:

Periodical:

Advanced Materials Research (Volumes 83-86)

Edited by:

M. S. J. Hashmi, B. S. Yilbas and S. Naher

Pages:

261-268

Citation:

H.H. Kim et al., "A Methodology for Control of the Robotic Welding Process Using Infrared Sensors", Advanced Materials Research, Vols. 83-86, pp. 261-268, 2010

Online since:

December 2009

Export:

Price:

$38.00

[1] D.K. Roberts and A.A. Wells, Fusion welding of aluminium alloys, British Welding Journal, No. 12 (1954), 553-559.

[2] N. Christensen, V. de L. Davies and K. Gjermundsen, Distribution of temperatures in arc welding, British Welding Journal, No. 2 (1965), 54-75.

[3] E. Friedman and S. S. Glickstein, An investigation of the thermal response of stationary gas tungsten arc welds, Welding Journal, Vol. 55, No. 12 (1976), 408-s-420-s.

[4] R.S. Chandel and S.R. Bala, Effect of welding parameters and groove angle on the soundness of root beads deposited by the SAW process, Proceedings of an International Conference on Trends in Welding Research, (1986), 379-385.

[5] J. Raveendra and R.S. Parmar, Mathematical models to predict bead geometry for flux cored arc welding, Metal Construction, Vol. 19, No. 2 (1987), 31R-35R.

[6] L.J. Yang, R.S. Chandel and M.J. Bibby, The effects of process variables on the bead height of submerged-arc weld deposits, Canadian Metallurgical Quarterly, Vol. 31, No. 4 (1992), 289297.

DOI: https://doi.org/10.1179/cmq.1992.31.4.289

[7] J.T. Liu, D.C. Weckman and H. W. Kerr, The effects of process variables on pulsed Nd: YAG laser spot welding: Part 1. AISI 409 stainless steel, Metallurgical Transactions, Vol. 24B, No. 12 (1993), 1065-1076.

DOI: https://doi.org/10.1007/bf02660998

[8] N. Murugan, R.S. Parmar, Effects of MIG process parameters on the geometry of the bead in the automatic surfacing of stainless steel, Journal of Materials Processing Technology, Vol. 41, No. 4 (1994), 381-398.

DOI: https://doi.org/10.1016/0924-0136(94)90003-5

[9] K.Y. Benyounis, A.G. Olabi and M.S.J. Hashmi, Effect of laser welding parameters on the heat input and weld-bead profile, Journal of Materials Processing Technology, Vol. 164 (2005), 978-985.

DOI: https://doi.org/10.1016/j.jmatprotec.2005.02.060

[10] N. Murugan and V. Gunaraj, Prediction and control of weld bead geometry and shape relationships in submerged arc welding of pipes, Journal of Materials Processing Technology, Vol. 168 (2005), 478-487.

DOI: https://doi.org/10.1016/j.jmatprotec.2005.03.001

[11] J.P. Ganjigatti, D.K. Pratihar and A.R. Choudhury, Global versus cluster-wise regression analyses for prediction of bead geometry in MIG welding process, Journal of Materials Processing Technology, Vol. 189 (2007), 352-366.

DOI: https://doi.org/10.1016/j.jmatprotec.2007.02.006

[12] K. Palani, N. Murugan, Optimization of weld bead geometry for stainless steel claddings deposited by FCAW, Journal of Materials Processing Technology, Vol. 190 (2007), 291-299.

DOI: https://doi.org/10.1016/j.jmatprotec.2007.02.035

[13] K.M. Kanti and P.S. Rao, Prediction of bead geometry in pulsed GMA welding using back propagation neural network, Journal of Materials Processing Technology, Vol. 200 (2008), 300-305.

DOI: https://doi.org/10.1016/j.jmatprotec.2007.09.034

[14] J.C. McGlone and B.D. Chadwick, The Submerged Arc Butt Welding of Mild Steel Part 2: The Prediction of Weld Bead Geometry from the Procedure Parameters, The Welding Institute Report 80/1978/PE.

[15] SAS Institute, Inc., SAS/STAT User's Guide, SAS Institute Inc., Cary, NC, (1988).

DOI: https://doi.org/10.1177/0894439314544925

[16] C.L. Lawson and R.J. Hanson, Solving least square problem, Englewood Cliffs, N. J. USA (1974).

Fetching data from Crossref.
This may take some time to load.