Analysis of vibration signal responses on pre induced tunnel defects in friction stir welding using wavelet transform and empirical mode decomposition

Abstract Among many condition-monitoring systems in welding operation, Defect identification is an important method to ensure the precision in finishing operation. Friction stir welding is a solid state welding process used to join two metals without the use of electrode at lower temperatures. The aim of this present work is to identify and localize the tunnel defect in aluminum alloy and measure the distance of the defect zone in the time domain of the vibration signal during Friction stir welding. The vibration signals are captured from the experiments and the burst in the vibration signal is focused in the analysis. A signal-processing scheme is proposed to filter the noise and to measure the dimensional parameters of the defect area. The proposed technique consists of discrete wavelet transform (DWT), which is used to decompose the signal. The enveloping technique is applied on the decomposed zero padded signal. The continuous wavelet transform (CWT) has been implemented on detailed signal followed by a time marginal integration (TMI) of the CWT scalogram. Empirical mode decomposition (EMD) is used to replace the detailing coefficients from DWT with Intrinsic Mode Function (IMF). Statistical parameters such as mean, kurtosis, S.D and crest factor have been extracted from the final filtered signal for validating the defect welds from the control defect free welds. Results produced were found to be that kurtosis is 7.4402 for tunnel defect induced weld and 3.3862 for defect free welds. As the increase in kurtosis value predicts the defect zone impact in the signal. The measurement of the defect zone of the cut 1 (voids) and cut 2 (tunnel grooves) in correlation with the processed signal is found to produce a much redundant results with an error rate of 0.02.