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 inZandYdirections 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.

Understanding Machine Learning for Friction Stir Welding Technology

“…Mishra et al [13] implemented the Convolutional Neural Network for identification of the texture of Friction Stir Welded joints and Conventional Welded joints. Macias et al [14] established a correlation between the acoustic emission signals and the various main parameters of friction stir welding process based on artificial neural networks (ANNs) trained on Levenberg-Marquardt algorithm. Figure 13 and 14 shows the methodology and the development of Artificial Neural Network architecture respectively.…”

“…Artificial Neural Network development methodology[14] Artificial Neural Network architecture[14] Figure 15 shows the predicted and measured results on the given dataset. It is clearly observed that the results obtained from the new model obtained, based on Neural Network architecture is an effective technique for the prediction of Friction Stir Welding process parameters and tensile strength of joint.…”