A study on on-line mathematical model to control of bead width for arc welding process

• Autorzy: Son J. S. , Lee J. P. , Park M. H. , Kim D. H. , Jin B. J. , Ki K. S. , Kim C. G. , Kim I. S.
• Języki publikacji: EN

Abstrakty

EN

Purpose: Recently, not only robotic welders have replaced human welders in many welding applications, but also reasonable seam tracking systems are commercially available. However, fully adequate control systems have not been developed due to a lack of reliable sensors and mathematical models that correlate welding parameters to the bead geometry for the automated welding process. Design /meth o d o lo g y/ap p ro ach : In this paper, two on-line empirical models using multiple regression analysis are proposed in order to be applicable for the prediction of bead width. For development of the proposed models, an attempt has been made to apply for a several methods. For the more accurate prediction, the prediction variables are first used to the surface temperatures measured using infrared thermometers with the welding parameters (welding current, arc voltage) because the surface temperature are strongly related to the formation of the bead geometry. The developed models are applied to monitor and control the bead width as welding quality. Findings: The developed two on-line empirical models are able to predict the optimal welding parameters required to achieve desired bead width and weld criteria, help the development of automatic control system and expert system and establish guidelines and criteria for the most effective joint design. Research lim ita tio n s /im p lic a tio n s : This research was concentrated to develop on the on-line empirical models that can predict bead width in robotic GMA welding process. The developed empirical models can only be employed to control the bead width for butt welding. O rig in ality /va lu e : It has been realized that with the use of the developed algorithms, the prediction of bead width becomes much simpler to even a novice user who has no prior knowledge of the robotic GMA welding process and optimization techniques.

Słowa kluczowe

EN

on-line empirical model; optimization; bead width; robotic arc welding; welding quality

PL

model empiryczny on-line; optymalizacja; robot spawania łukowego; jakość spawania

• Wydawca: International OCSCO World Press
• Czasopismo: Archives of Materials Science and Engineering
• Rocznik: 2015
• Tom: Vol. 72, nr 2
• Strony: 78--85
• Opis fizyczny: Bibliogr. 31 poz.

Twórcy

autor

Son J. S.

• Research Institute of Medium& Small Shipbuilding, 1 7 0 3 -8 Yongang-ri, Samho-eup, Yeongam, Jeonnam, 5 2 6 -8 9 7 , South Korea

autor

Lee J. P.

• Department of Mechanical Engineering, Mokpo National University, 1666 Youngsan-ro, Chungkye-myun, Muan-gun, Jeonnam, 5 3 4 -7 2 9 , South Korea

autor

Park M. H.

• Department of Mechanical Engineering, Mokpo National University, 1666 Youngsan-ro, Chungkye-myun, Muan-gun, Jeonnam, 5 3 4 -7 2 9 , South Korea

autor

Kim D. H.

• Department of Mechanical Engineering, Mokpo National University, 1666 Youngsan-ro, Chungkye-myun, Muan-gun, Jeonnam, 5 3 4 -7 2 9 , South Korea

autor

Jin B. J.

• Department of Mechanical Engineering, Mokpo National University, 1666 Youngsan-ro, Chungkye-myun, Muan-gun, Jeonnam, 5 3 4 -7 2 9 , South Korea

autor

Ki K. S.

• Department of Mechanical Engineering, Mokpo National University, 1666 Youngsan-ro, Chungkye-myun, Muan-gun, Jeonnam, 5 3 4 -7 2 9 , South Korea

autor

Kim C. G.

• Hyundai Samho Heavy Industries Co. LTD ., 1700, Yongdang-ri, Samho-eup, Yeongamgun, Jeonnam, 5 2 6 -7 0 1 , South Korea

autor

Kim I. S.

• Department of Mechanical Engineering, Mokpo National University, 1666 Youngsan-ro, Chungkye-myun, Muan-gun, Jeonnam, 5 3 4 -7 2 9 , South Korea

Bibliografia

• [1] J.J. Hunter, G.W. Bryce, J. Doherty, On-line control of the arc welding process, Proceedings of the 2nd International Conference on Computer Technology in Welding, Cambridge, UK, 1988, 1-12.
• [2] R.W. Richardson, A. Gutow, R.A. Anderson, D.F. Farson, Coaxial weld pool viewing for process monitoring and control, Welding Journal 63/3 (1984) 43-50.
• [3] H.B. Smartt, P. Einerson, A.D. Watkins, R.A. Morris, Gas metal arc welding process sensing and control, Advances in Welding Science and Technology, Proceedings of an International Conference on Trends in Welding Research, Gatlinburg, Tennessee, USA, 1986,461-465.
• [4] G. Doumanidis, M. Hale, D.E. Hardt, Multivariable control of arc welding processes, Advances in Welding Science and Technology, Proceedings of an International Conference on Trends in Welding Research, Gatlinburg, Tennessee, USA, 1986,449-457.
• [5] H.B. Smartt, Arc-welding process, Welding, Theory and Practice, Elsevier Science Publishers, 1990,175-208.
• [6 ] J.J. Hunter, G.W. Bryce, J. Doherty, On-line control of the arc welding process, Proceedings of the 2nd International Conference on Computer Technology in Welding, Cambridge, UK, 1988, 1-12.
• [7] Y.M. Zhang, R. Kovacevic, L. Li, Characterization and real-time measurement of geometrical appearance of the weld pool, International Journal of Machine Tools and Manufacture 36/7 (1996) 799-816.
• [8] K. Lei, Y. Qiu, Y. He, A new adaptive well-chosen inertia weight strategy to automatically harmonize global and local search ability in particle swarm optimization, International Symposium on Systems and Control in Aerospace and Astronautics (2006) 977-980.
• [9] M. Melanie, An Introduction to genetic algorithms, Massachusetts, MIT Press, 1999.
• [10] D.S. Nagesh, G.L. Datta, Genetic algorithm for optimization of welding variables for height to width ratio and application of AN N for prediction of bead geometry for TIG welding process, Applied Soft Computing Journal 10/3 (2010) 897-907.
• [11] E. Atashpaz-Gargari, C. Lucas, Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition, IEEE congress on Evolutionary computation (2007) 4661-4667.
• [12] A. Kaveh, S. Talatahar, Optimum design of skeletal structures using imperialist competitive algorithm, Computers, Structures 88/22 (2010) 1220-1229.
• [13] M. Yousefi, A.N. Darns, H. Mohammadi, An imperialist competitive algorithm for optimal design of plate-fin heat exchangers, International Journal of Heat and Mass Transfer 55/11 (2012) 3178-3185.
• [14] R.W. Richardson, Robotic weld joint tracking systems - Theory and implementation methods, Welding Journal 65/11 (1986) 43-51.
• [15] D.E. Hardt, J.M. Katz, Ultrasonic measurement of weld penetration, Welding Journal 63/9 (1984) 273-281.
• [16] Y.W. Park, H.S. Park, S.H. Rhee, M.J. Kang, Real time estimation of C02 laser weld quality for automotive industry, Optics Laser Technology 34/2 (2002) 135-142.
• [17] J. Mirapeix, A. Cobo, O.M. Conde, C. Jauregui, J.M. Higuera, Real-time arc welding defect detection technique by means of plasma spectrum optical analysis, NDT&E International 39/5 (2006) 356-360.
• [18] C.H. Tsai, K.H. Hou, H. Chuang, Fuzzy control of pulsed GTA welds by using real-time root bead image feedback, Journal of Materials Processing Technology 176/1 (2006) 158-167.
• [19] D.K. Roberts, A.A. Wells, Fusion welding of aluminium alloys, British Welding Journal 12 (1954) 553-559.
• [20] N. Christensen, L. Davies, K. Gjermundsen, Distribution of temperatures in arc welding, British Welding Journal 2 (1965) 54-75.
• [21] E. Friedman, S.S. Glickstein, An investigation of the thermal response of stationary gas tungsten arc welds, Welding Journal 55/12 (1976) 408-420.
• [22] RS. Chandel, 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.
• [23] J. Raveendra, RS. Parmar, Mathematical models to predict bead geometry for flux cored arc welding, Metal Construction 19/2 (1987) 31-35.
• [24] L.J. Yang, R.S. Chandel, M.J. Bibby, The effects of process variables on the bead height of submerged-arc weld deposits, Canadian Metallurgical Quarterly 31/4 (1992) 289-297.
• [25] J.T. Liu, D.C. Weckman, H.W. Kerr, The effects of process variables on pulsed Nd: YAG laser spot welding: Part 1. A IS I409 stainless steel, Metallurgical Transactions 24/12 (1993) 1065-1076.
• [26] 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 41/4 (1994) 381-398.
• [27] K.Y. Benyounis, A.G. Olabi, M.S.J. Hashmi, Effect of laser welding parameters on the heat input and weldbead profile, Journal of Materials Processing Technology 164 (2005) 978-985.
• [28] N. Murugan, V. Gunaraj, Prediction and control of weld bead geometry and shape relationships in submerged arc welding of pipes, Journal of Materials Processing Technology 168 (2005) 478-487.
• [29] R Winiczeko, R Salat, M. Awtoniuk, Estimation of tensile strength of ductile iron friction welded joints using hybrid intelligent methods, Transactions of Nonferrous Metals Society of China 23/2 (2013) 385-391.
• [30] C. Lucas, Z. Nasiri-Gheidari, F. Tootoonchian, Application of an imperialist competitive algorithm to the design of a linear induction motor, Energy Convers Manag 51/7 (2010) 1407-1411.
• [31] S.M. Hosseini, A. A1 Khaed, A survey on the imperialist competitive algorithm metaheuristic: Implementation in engineering domain and directions for future research, Applied Soft Computing 24 (2014) 1078-1094.