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http://dx.doi.org/10.5781/KWJS.2011.29.4.403

A Experiment Study for Selection of Welding Condition of fillet Welded Structure  

Na, Hyun-Ho (Dept. of Mechanical Engineering, Mokpo National University)
Kim, Ill-Soo (Dept. of Mechanical Engineering, Mokpo National University)
Kim, Ji-Sun (Dept. of Mechanical Engineering, Mokpo National University)
Lee, Ji-Hye (Dept. of Mechanical Engineering, Mokpo National University)
Publication Information
Journal of Welding and Joining / v.29, no.4, 2011 , pp. 41-47 More about this Journal
Abstract
GMA welding process is a production process to improve productivity for the provision of higher welding quality of material. These includes numerous process variables that could affect welding quality, productivity and cost savings. Recently, the welding part of construction equipment had frequent failure of major components in the welding part of each subsidiary material due to shock which is very poor according to the welding part. Therefore, the implementation of sound welding procedure is the most decisive factor for the reliability of construction machinery. The data generated through experiments conducted in this study has validated its effectiveness for the optimization of bead geometry and process variables is presented. The criteria to control the process parameters, to achieve a good bead geometry. This study has developed mathematical models and algorithms to predict or control the bead geometry in GMA fillet welding process.
Keywords
Fillet welding; Taguchi method; Mathematical model; Sensitivity analysis;
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1 D.S. Nagesh and G.L. Datta : Prediction of weld bead geometry and prediction in shielded metal arc welding using artificial neural networks, Journal of Material Processing Technology, 99 (2002), 1-10
2 J.Y. Jeng, T.F. Mau and S.M. Leu : Prediction of laser butt joint welding parameters using backpropagation and learning vector quantisation network, Journal of Material Processing Technology, 79 (2000), 207-218
3 Y.S. Tang, S.C. Juang and C.H. Chang : The use of Grey-based Taguchi method to determine submerged arc welding process parameters in Hardfacing, Journal of MPT, 128 (2002), 1-6
4 D. Li and T. Srikanthan : Neural nerwork based self-organized fuzzy logic control for arc welding, Engineering applications of artificial Intelligence, 14 (2001), 115-124   DOI   ScienceOn
5 J.M. Viet et al : Weld pool shape prediction in plasma augmented laser welded steel, Science and Technology of Welding and Joining, 6 (2001), 305-314   DOI   ScienceOn
6 Chandel R, S : Effect of welding parameters and groove angle on the soundness of root beads deposited by the SAW process Proceedings of the Fourth International Conference on Modeling of Casting and Welding Processes, (1988), 109-120
7 Terng et al : Modelling, optimization and classification of weld quality in tungsten inert Gas welding, International Journal of Machine Tools and Manufacturing, 39 (1999), 1427-1438   DOI   ScienceOn
8 Le, et al : Modelling of submerged arc welding bead using self-adaptive offset neural network, Journal of Materials Process Technology, 71 (1999), 228-298.
9 C.K. Sun, J.W. Kim and S.J. Na : A Study on the Seam Tracking in CO2 Fillet Welding by Using an Arc Sensor , Journal of KWS, 17-6 (1990), 70-78 (in Korean)
10 Y.P. Kim, H.S. Kim, S.H. Hong and W.S. Kim : A Study on Mechanical Properties of Fillet Weldment in Pipeline Repair Welding Using Sleeve, Journal of KWS, 19-6 (1996), 49-58 (in Korean)
11 J. Hanright : Robotic arc welding under adaptive control - A surbey of current technology, Welding Journal, 65-11 (1986), 19-24
12 S.J. Marburger : Welding automation and computer control, Welding : Theory Practice, Elsevier Science Publisher, B. V., 1990, 209-233
13 I.S. Kim and C.E. Park : Use of a neural network to control bead width in GMA welding, Welding Research Supplement, 45-3 (2000), 33-37
14 Y.W. Park, H.S. Park, S. Rhee and M.J. Kang : Real time estimation of CO2 laser weld quality for automotive industry, Journal of Optics & Laser Technology, 34-2 (2002), 135-142   DOI   ScienceOn