A Study on Welding Quality of Robotic Arc Welding Process Using Mahalanobis Distance Method

Abstract:

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In robotic GMA (Gas Metal Arc) welding process, heat and mass inputs are coupled and transferred by the weld arc and molten base material to the weld pool. The amount and distribution of the input energy are basically controlled by the obvious and careful choices of welding process parameters in order to accomplish the optimal bead geometry and the desired mechanical properties of the quality weldment. To make effective use of automated and robotic GMA welding, it is imperative to predict online faults for bead geometry and welding quality with respect to welding parameters, applicable to all welding positions and covering a wide range of material thickness. To successfully accomplish this objective, two sets of experiment were performed with different welding parameters; the welded samples from SM 490A steel flats adopting the bead-on-plate technique were employed in the experiment. The experimental results of current and voltage waveforms were used to predict the magnitude of bead geometry and welding quality, and to establish the relationships between weld process parameters and online welding faults. MD (Mahalanobis Distance) technique is employed for investigating and modeling of GMA welding process and significance test techniques were applied for the interpretation of the experimental data. Statistical models developed from experimental results which can be used to control the welding process parameters in order to achieve the desired bead geometry based on weld quality criteria.

Info:

Periodical:

Materials Science Forum (Volumes 773-774)

Edited by:

A. Kiet Tieu, Hongtao Zhu and Qiang Zhu

Pages:

759-765

Citation:

R. R. Chand et al., "A Study on Welding Quality of Robotic Arc Welding Process Using Mahalanobis Distance Method", Materials Science Forum, Vols. 773-774, pp. 759-765, 2014

Online since:

November 2013

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$38.00

[1] S. Feng, O. Hiroyuki, T. Hidennori, K. Yomizo, and S. Hu, International Journal of Precision Engineering and Manufacturing, 12, 6(2011).

[2] C. S. Wu, J. Q. Gao and J. K. Hu, International Journal of Measurement Science and Technology. 18, 1 (2007).

[3] S. Adolfsson, A. Bahrami, G. Bolmsjo and I. Claeson, welding Journal. 78, 2 (1999).

[4] X. Li and S. W. Simpson, Science and Technology of welding and joining, 14, 2(2009).

[5] G. E. Cook, J. E. Maxwell, R. J. Barnett and A. M. Strauss, IEEE Transactions on Industry Applications, 33, 2 (1997).

[6] K. Pal, S. Bhattacharya and S. K. Pal, Journal Material Process and Technology, 210, 1 (2010).

[7] J. N. Pires, A. Loureiro, G. Bolmsjo, Welding Robots Technology, Systems Issues and Applications, Edited J. S. da Costa, Springer-Verlag London Limited, London (2006).

[8] G. Taguchi and R. Jugulum, The Mahalanobis –Taguchi strategy: A pattern technology system, John wiley & Sons, New York, (2002).

DOI: https://doi.org/10.1002/9780470172247

[9] P. Avishek and J. Maiti, Expert System with Applications, 37, 2, (2010).

[10] S. E. Abbasi, A. Aaghaie and M. Fazlali, Journal of Industrial Engineering, 45, (2011).

[11] J. Ruisz, J. Biber and M. Loiptsberger, International Journal of Advance Manufacturing Technology, 33, 9-10 (2007).

[12] Y. S. Tarng, W. H. Yang, S. C. Juang, International Journal Advance Manufacturing Technology, 16, (2000).

[13] S. W. Simpson, Science and Technology of welding and Joining, 13, 1, (2008).

[14] H. T. Lee, M. Wang, R. Maev, E. Maeva , International Journal Advance Manufacturing Technology, 20, 9-10, (2003).

[15] L. Zhiyong, W. Bao, D. Jingbin, Journal Material Process Technology, 209, (2009).