Prediction of Flux Cored Arc Welding (FCAW) Parameters and Bead Geometry in Downhill Position (3F)

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The robot can perform Flux Cored Arc Welding (FCAW) at high productivity and consistency in quality. The quality of the welding depend on the selection of welding parameter and deposition geometry. These input has to be known before the start of production, generally the welding operator will obtain the information through experimental trial and error. This project planned to develop a tool that can advise the choice of welding parameter that produce quality weld bead with desired geometry. This research focused on the correlation of heat input on weld bead geometry and the range of welding parameter for fillet design welded in downhill direction (3F). From the correlation trend-line equations and welding parameter population boundary, the weld bead geometry and welding parameter for quality deposit are predicted. Consequently two calculators were developed to display the values digitally. The deviation of predicted bead geometry from actual welding is less than 1mm. Mean Absolute Deviation (MAD) is less than 0.4mm, accuracy is good. A wide range of welding parameters can be generated for quality welding at desired bead geometry.

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342-346

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October 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] AWS A2 Committee on Definitions and Symbols, Standard Welding Terms and Definitions. AWS Board of Directors, 2001, p.147.

Google Scholar

[2] V. M. Radhakrishnan, Welding Processes, in Welding Technology and Design, New Age International (P) Ltd., Publishers, 2005, p.1–65.

Google Scholar

[3] L. J. lawrence Bower, Flux Cored Arc Welding Equipment, Setup, and Operation, in Welding Skills, Processes and Practices for Entry-Level Welders Book, (2010).

Google Scholar

[4] D. Katherasan, J. V. Elias, P. Sathiya, and a. N. Haq, Flux Cored Arc Welding Parameter Optimization Using Particle Swarm Optimization Algorithm, Procedia Eng., vol. 38, p.3913–3926, Jan. (2012).

DOI: 10.1016/j.proeng.2012.06.449

Google Scholar

[5] B. Y. K. and H. J. K. S. Son', S. Kim, H. H. Kim, Kim, A study on the prediction of bead geometry in the robotic welding system, J. Mech. Sci. Technol., vol. 21, p.1726–1731, (2007).

DOI: 10.1007/bf03177401

Google Scholar

[6] H. H. Na, I. S. Kim, B. Y. Kang, and J. Y. Shim, A experiment study for welding optimization of fillet welded structure, J. Achiev. Mater. Manuf. Eng., vol. 45, no. 2, p.178–187, (2011).

Google Scholar

[7] P. Mukhopadhyay, S. Chattopadhyaya, S. Bhatia, N. K. Singh, and A. K. Mukhopadhyay, Prediction of Weld Parameters in Gas Metal Arc Welding Process Using Curve Arc Fitting Techniques and Graphical Methods, Adv. Mater. Res., vol. 652–654, p.2352–2356, Jan. (2013).

DOI: 10.4028/www.scientific.net/amr.652-654.2352

Google Scholar

[8] D. Katherasan, J. V. Elias, P. Sathiya, and a. N. Haq, Simulation and parameter optimization of flux cored arc welding using artificial neural network and particle swarm optimization algorithm, J. Intell. Manuf., no. 2007, p.0–9, Jul. (2012).

DOI: 10.1007/s10845-012-0675-0

Google Scholar

[9] Y. Shi, Z. Zheng, and J. Huang, Sensitivity model for prediction of bead geometry in underwater wet flux cored arc welding, Trans. Nonferrous Met. Soc. China, vol. 23, no. 7, p.1977–1984, Jul. (2013).

DOI: 10.1016/s1003-6326(13)62686-2

Google Scholar

[10] G. Tham, M. Y. Yaakub, S. K. Abas, Y. H. P. Manurung, and B. A. Jalil, Predicting the GMAW 3F T-Fillet Geometry and Its Welding Parameter, Procedia Eng., vol. 41, p.1794–1799, Jan. (2012).

DOI: 10.1016/j.proeng.2012.07.385

Google Scholar

[11] S. H. A. Hamidim, G. Tham, Y. H. P. Manurung, and S. K. Abas, Predicting Bead Geometry of 2F-Fillet Joint Welded by Small Wire SAW, Adv. Mater. Res., vol. 576, p.185–188, Oct. (2012).

DOI: 10.4028/www.scientific.net/amr.576.185

Google Scholar