Prediction of welding parameters for pipeline welding using an intelligent system
Kim, Ill-Soo, Jeong, Y. J., Yarlagadda, Prasad K., & Lee, C.W (2003) Prediction of welding parameters for pipeline welding using an intelligent system. The International Journal of Advanced Manufacturing Technology, 22(9-10), pp. 713-719.
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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|Item Type:||Journal Article|
|Additional Information:||For more information please refer to the publisher's website (link above) or contact the author: email@example.com|
|Subjects:||Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > MANUFACTURING ENGINEERING (091000) > Manufacturing Processes and Technologies (excl. Textiles) (091006)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Engineering Systems
|Copyright Owner:||Copyright 2003 Springer|
|Copyright Statement:||The original publication is available at SpringerLink http://www.springerlink.com|
|Deposited On:||07 Mar 2007 00:00|
|Last Modified:||29 Feb 2012 13:00|
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