Monitoring and processing the acoustic emission signals from the friction-stir-welding process

Abstract

This paper discusses the detection and analysis of the acoustic emission (AE) signals to investigate the possibility of applying the AE technique for the in-process monitoring of the friction-stir-welding process. Tests are carried out for joining similar and dissimilar metals using a high-speed rotating tool under various rotational speeds and traverse speeds and for different tool penetration depths. The results of fast Fourier transform show that the amplitude of the AE signal in the frequency domain is sensitive to the change in the depth of penetration of the tool. Signals in certain frequency ranges disappear when the tool loses contact with the workpiece during the process. Discrete wavelet transform indicates significant sudden changes in the decomposed signal in the lower frequency ranges (higher levels) when the shoulder makes contact with or detaches itself from the workpieces. By identifying the frequencies during the process and analysing the wavelet decomposed signals in various levels or frequency bands, it is possible to monitor effectively the transient welding state and to identify quickly 2the process changes.

Application of vibroacoustic signals to evaluate tools profile changes in friction stir welding on AA 1050 H24 alloy

“…11 Therefore, other signal processing techniques are utilised to characterise the AE signal over these frequency ranges. The wavelet transformation has been utilised in this work to obtain a series of decomposed vibroacoustical signals.…”

Defect identification in friction stir welding using discrete wavelet analysis

“…They showed the presence of sudden changes in signal but those changes can also correspond to some other stresses as well as they have not shown the exact location of defects. Soundararajan et al [19] showed that acoustic emissions produced are time variant only during plunge phase but is steady during welding. They have applied FFT, STFT, and wavelet transforms on signals but found difficult to correlate the frequency of the signal directly to various interactions that occur during the process.…”

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

“…It is characterized by a more precise representation of the behaviour of the phenomenon, but its high complexity must be considered in the analysis. Different works reported [7][8][9][10][11] show the application of nondestructive techniques (NDT) on the mechanical and manufacturing processes.…”

“…Soundararajan et al [7] determined the correlation between the losses of contact of the tool with the workpiece based on features of the AE signal, using the Fast Fourier Transform (FFT), the Short-Time Fourier Transform (STFT), and the Discrete Wavelet Transform (DWT). The authors investigated the possibility of applying AE techniques for the in-process monitoring of the FSW process.…”

“…Welding processes are physically complex in nature, nonlinear, and dynamic, presenting a large number of input parameters; there is no close mathematical model which can describe the behaviour of these processes [7]. On the other hand, in the last years, artificial intelligence techniques and especially artificial neural networks (ANNs) are being used as a tool to model different complex processes.…”