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

Abstract

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.