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Prediction of welding parameters for pipeline welding using an intelligent system

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Abstract

The determination of the welding parameters for pipeline welding is based on a skilled welder's know-how and long-term experiences rather than on theoretical and analytical techniques. In this paper, an intelligent system for the determination of welding parameters for each pass and welding position, for pipeline welding based on one database and a finite element method (FEM) model, and on two back-propagation (BP) neural network models and a corrective neural network (CNN) model was developed and validated. The preliminary test of the system has indicated that the system could determine the welding parameters for pipeline welding quickly, from which good weldments can be produced without experienced welding personnel. Experiments using the predicted welding parameters from the developed system proved the feasibility of interface standards and intelligent control technology to increase productivity, improve quality and reduce the cost of system integration.

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Acknowledgement

The work has been undertaken and done at QUT and funded by ARC during the stay in Australia.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Mokpo National University, 61 Dorimri, Chunhke-myun, Muan-gun, Chonam, 534–729, Korea

    I. S. Kim, Y. J. Jeong & C. W. Lee

  2. School of Mech. Mfg. & Med. Engineering, Queensland University of Technology, 2, George Street, GPO Box 2434, Brisbane QLD 4001, Australia

    P. K. D. V. Yarlagadda

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  1. I. S. Kim
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  2. Y. J. Jeong
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  3. C. W. Lee
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  4. P. K. D. V. Yarlagadda
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Correspondence to I. S. Kim.

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Kim, I.S., Jeong, Y.J., Lee, C.W. et al. Prediction of welding parameters for pipeline welding using an intelligent system. Int J Adv Manuf Technol 22, 713–719 (2003). https://doi.org/10.1007/s00170-003-1589-y

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  • DOI: https://doi.org/10.1007/s00170-003-1589-y

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