Open AccessUpset Prediction in Friction Welding Using Radial Basis Function Neural Network
Author(s) -
Wei Liu,
Feifan Wang,
Xiawei Yang,
Wenya Li
Publication year - 2013
Publication title -
advances in materials science and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.356
H-Index - 42
eISSN - 1687-8442
pISSN - 1687-8434
DOI - 10.1155/2013/196382
Subject(s) - upset , welding , friction welding , artificial neural network , materials science , radial basis function , inertia , finite element method , joint (building) , rotational speed , mechanical engineering , computer science , structural engineering , artificial intelligence , composite material , engineering , physics , classical mechanics
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
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