Upset Prediction in Friction Welding Using Radial Basis Function Neural Network

Advances in Materials Science and Engineering. 2013;2013 DOI 10.1155/2013/196382

 

Journal Homepage

Journal Title: Advances in Materials Science and Engineering

ISSN: 1687-8434 (Print); 1687-8442 (Online)

Publisher: Hindawi Publishing Corporation

LCC Subject Category: Technology: Electrical engineering. Electronics. Nuclear engineering: Materials of engineering and construction. Mechanics of materials | Science: Physics

Country of publisher: Egypt

Language of fulltext: English

Full-text formats available: PDF, HTML, ePUB, XML

 

AUTHORS

Wei Liu (State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an, Shaanxi 710071, China)
Feifan Wang (State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi’an, Shaanxi 710072, China)
Xiawei Yang (State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi’an, Shaanxi 710072, China)
Wenya Li (State Key Laboratory of Solidification Processing, Northwestern Polytechnical University, Xi’an, Shaanxi 710072, China)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 19 weeks

 

Abstract | Full Text

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.