Minimization of Residual Stresses in Submerged Arc Welding Process of Oil and Gas Steel Pipes by Committee Machine

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Submerged arc welding (SAW) is a well-known method to weld seam in manufacturing of large diameters steel pipes in oil and gas industry. The main subject of SAW design is selection of the optimum combination of input variables for achieving the desired output variables of weld. Input variables include voltage, amperage and speed of welding and output variables include residual stresses due to welding. On the other hand, main target in multi response optimization (MRO) problem is to find input variables values to achieve to desired output variables. Current study is a combination and modification of some works of authors in MRO and SAW subjects. This study utilizes an experiment design according to Taguchi arrays. Also a committee machine (CM) modeling the problem by CM using two approaches. The first CM consists eight experts with traditional approach in computation and second CM includes elite experts. Genetic algorithm was applied to find CM weights and desired responses. Results show that proposed approach in CM has a smaller root mean squire error (RMSE) than traditional approach. The validation of CM model is done by comparison of results with simulation of SAW process and residual stresses in a finite element environment. Finally, the results show few differences between the real case responses and the proposed algorithm responses.

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519-524

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June 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] M.R. Forouzan, M.R. Niroomand, S.J. Golestaneh, S.A.R. Tabatabaiee. Optimization of residual stresses o welding process of large diameter oil and gas pipes. 15th Annual (International) Conference on Mechanical Engineering (ISME 2007), Tehran, Iran (2007).

Google Scholar

[2] M.R. Forouzan, S.M. Mirfalah Nasiri, A. Mokhtari, A. Heidari, S.J. Golestaneh: Materials & Design (2012) Vol. 33, pp.384-94.

DOI: 10.1016/j.matdes.2011.04.016

Google Scholar

[3] M.R. Forouzan, M.R. Niroomand, A. Heidari, S.J. Golestaneh. Constrained Optimization of the Submerged Arc Welding Process of Oil and Gas Pipes by TAANGA Method. 2nd Iranian pipe & pipeline conference, Tehran, Iran (2009).

Google Scholar

[4] S.J. Golestaneh, N. Ismail, S.H. Tang, M.K.A.M. Ariffin, H.M. Naeini, A.A. Maghsoudi: International Journal of the Physical Sciences (2011) Vol. 6, pp.7935-49.

Google Scholar

[5] J. Antony, R.B. Anand, M. Kumar, M.K. Tiwari: Journal of Manufacturing Technology Management (2006) Vol. 17, pp.908-25.

Google Scholar

[6] H.H. Chang: Expert Systems with Applications (2008) Vol. 35, p.1095–103.

Google Scholar

[7] S. Kumanan, J.E.R. Dhas, K. Gowthaman: Indian Journal of Engineering & Materials Sciences (2007) Vol. 12, pp.177-83.

Google Scholar

[8] A.W.L. Yao, H.T. Liao, C.Y. Liu: The Open Automation and Control System Journal (2008) Vol. 1, pp.7-13.

Google Scholar

[9] E. Del Castillo, D.C. Montgomery, D.R. Mccarviille: Journal of quality technology (1996) Vol. 28, pp.337-45.

Google Scholar

[10] D. Lepadatu, A. Kobi, R. Hambli, A. Barreau. RAMS 2005 IEEE. (2005).

Google Scholar

[11] S.H.R. Pasandideh, S.T.A. Niaki: Applied Mathematics and Computation (2006) Vol. 175, p.366–82.

Google Scholar

[12] I. Mukherjee, P.K. Ray. International Journal of Intelligent Systems Technologies and Applications (2008) Vol. 4, pp.97-122.

Google Scholar

[13] Information from Sadid pipe and equipments co., Tehran, Iran, http: /www. sadidpipe. com.

Google Scholar

[14] M.R. Forouzan, A. Heidari, S.J. Golestaneh. Esteghlal journal (2009) Vol. 28, pp.93-110.

Google Scholar

[15] T. Kamo, C. Dagli: Expert Systems with Applications (2009) Vol. 36, pp.5023-30.

Google Scholar

[16] Matlab User's Guide. Version 2012b. Neural Network Toolbox Math Works, USA. Vol. (2012), p.

Google Scholar

[17] H.B. Celikoglu: Mathematical and Computer Modelling (2006) Vol. 44, p.640–58.

Google Scholar

[18] E. Ardil, P.S. Sandhu: International Journal of Physical Sciences (2010) Vol. 5, pp.074-85.

Google Scholar

[19] J. Bo, T. Yuchun, Z. Yan-Qing: International Journal of Computational Intelligence in Bioinformatics and Systems Biology (2009) Vol. 1, p.59.

Google Scholar

[20] N. Ismail, S.J. Golestaneh, S.H. Tang, M.K.A.M. Ariffin, H. Moslemi Naeini, D. Rouhani. Modified committee neural networks for prediction of machine failure times. The 3rd national intelligent systems and information technology symposium (ISITS 2010), Institute of Advanced Technology (ITMA), Universiti Putra Malaysia (UPM)-Malaysia (2010).

DOI: 10.35940/ijeat.a3025.109119

Google Scholar

[21] A. Kadkhodaie-Ilkhchi, M.R. Rezaee, H. Rahimpour-Bonab. Persian Gulf. Journal of Petroleum Science and Engineering (2009) Vol. 65, pp.23-32.

DOI: 10.1016/j.petrol.2008.12.012

Google Scholar

[22] K.Y. Benyounis, A.G. Olabi, M.S.J. Hashmi: Optics & Laser Technology (2008) Vol. 40, pp.76-87.

Google Scholar

[23] H. -H. Chang, Y. -K. Chen: Applied Soft Computing (2011) Vol. 11, p.436–42.

Google Scholar

[24] A. Haghizadeh, l. Teang shui, E. Goudarzi. Australian Journal of Basic and Applied Sciences (2010) Vol. 4, pp.1668-75.

Google Scholar

[25] P. Krause, D.P. Boyle, F. Bäse. Advances in Geosciences (2005) Vol. 5, p.89–97.

Google Scholar