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Abstract and Applied Analysis
Volume 2014 (2014), Article ID 728564, 11 pages
Research Article

Wavelets Application in Prediction of Friction Stir Welding Parameters of Alloy Joints from Vibroacoustic ANN-Based Model

1Electrical Engineering Department, University of La Rioja, 26004 Logroño, Spain
2Faculty of Mechanical Engineering, University of Oriente, 90900 Santiago de Cuba, Cuba
3Mechanical Engineering Department, University of La Rioja, 26004 Logroño, Spain

Received 1 March 2014; Accepted 23 March 2014; Published 18 May 2014

Academic Editor: Eugene B. Postnikov

Copyright © 2014 Emilio Jiménez-Macías 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.


This paper analyses the correlation between the acoustic emission signals and the main parameters of friction stir welding process based on artificial neural networks (ANNs). The acoustic emission signals in Z and Y directions have been acquired by the AE instrument NI USB-9234. Statistical and temporal parameters of discomposed acoustic emission signals using Wavelet Transform have been used as input of the ANN. The outputs of the ANN model include the parameters of tool rotation speed and travel speed, and tool profile, as well as the tensile strength. A multilayer feed-forward neural network has been selected and trained, using Levenberg-Marquardt algorithm for different network architectures. Finally, an analysis of the comparison between the measured and the calculated data is presented. The model obtained can be used to model and develop an automatic control of the parameters of the process and mechanical properties of joint, based on the acoustic emission signals.