A study on prediction of bead height in robotic arc welding using a neural network

Yarlagadda, Prasad K., Kim, Ill-Soo, Son, Joon-Sik, & Lee, C.W. (2002) A study on prediction of bead height in robotic arc welding using a neural network. Journal of Materials Processing Technology, 130–131(-), pp. 229-234.

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This paper presents development of an intelligent algorithm to understand relationships between process parameters and bead height, and to predict process parameters on bead height through a neural network and multiple regression methods for robotic multi-pass welding process. Using a series of robotic arc welding, additional multi-pass butt welds were carried out in order to verify the performance of the neural network estimator and multiple regression methods as well as to select the most suitable model. The results show that not only the proposed models can predict the bead height with reasonable accuracy and guarantee the uniform weld quality, but also a neural network model could be better than the empirical models (linear and curvilinear equations).

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52 citations in Scopus
27 citations in Web of Science®
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ID Code: 6151
Item Type: Journal Article
Refereed: Yes
Additional Information: For more information, please refer to the journal’s website (see link) or contact the author. Author contact details: y.prasad@qut.edu.au
Keywords: Neural network, Method of least squares, Multi, pass welding, Bead height, Process parameters, Weldability
DOI: 10.1016/S0924-0136(02)00803-8
ISSN: 0924-0136
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Control Systems Robotics and Automation (090602)
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Copyright Owner: Copyright 2002 Elsevier
Deposited On: 15 Feb 2007 00:00
Last Modified: 10 Aug 2011 15:29

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