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Advances in Materials Science and Engineering
Volume 2013, Article ID 196382, 9 pages
http://dx.doi.org/10.1155/2013/196382
Research Article

Upset Prediction in Friction Welding Using Radial Basis Function Neural Network

1State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an, Shaanxi 710071, China
2Department of Physics, Qinghai Normal University, Xining, Qinghai 810008, China
3State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi’an, Shaanxi 710072, China
4School of Materials Science and Engineering, Northwestern Polytechnical University, Xi’an, Shaanxi 710072, China

Received 18 August 2013; Accepted 9 October 2013

Academic Editor: Achilleas Vairis

Copyright © 2013 Wei Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper addresses the upset prediction problem of friction welded joints. Based on finite element simulations of inertia friction welding (IFW), a radial basis function (RBF) neural network was developed initially to predict the final upset for a number of welding parameters. The predicted joint upset by the RBF neural network was compared to validated finite element simulations, producing an error of less than 8.16% which is reasonable. Furthermore, the effects of initial rotational speed and axial pressure on the upset were investigated in relation to energy conversion with the RBF neural network. The developed RBF neural network was also applied to linear friction welding (LFW) and continuous drive friction welding (CDFW). The correlation coefficients of RBF prediction for LFW and CDFW were 0.963 and 0.998, respectively, which further suggest that an RBF neural network is an effective method for upset prediction of friction welded joints.