B. Sasi

Wavelet Transform–Based Denoising Method for Processing Eddy Current Signals

Wavelet transform (WT)–based denoising method is proposed for processing eddy current signals of thin-walled stainless steel fuel tubes with periodic wall thickness variations formed due to fluctuation in tube drawing process parameters. In this method, discrete wavelet transform with level-based threshold has been applied to selectively eliminate the noise due to periodic wall thickness variations towards meeting the quality assurance requirement of detection of holes larger than 0.3 mm diameter and linear defects deeper than 0.075 mm (20% wall thickness). The method has been applied to tubes having machined holes, longitudinal notches, and circumferential notches, and an overall improvement of 20 dB in signal-to-noise ratio has been observed. The method has been able to detect defects present anywhere in the wall thickness variation regions and also in tubes without any wall thickness variations.

Nondestructive evaluation of austenitic stainless steel welds

The paper presents advanced ultrasonic and eddy current NDE techniques developed in the authors laboratory for nondestructive evaluation of austenitic stainless steel welds. The paper discusses the performance and comparison of 2D discrete wavelet transform (DWT) and de-noising methods applied on eddy current images obtained from stainless steel weld pad with machined longitudinal notches and a systematic approach for eddy current defect characterisation in weld pads by neural network. The simulation and experimental results on the effect of elastic anisotropy on ultrasonic phased array inspection in austenitic stainless steel weld are also discussed. A guided wave based ultrasonic method developed for detection of defects in stainless steel welds and its validation with complimentary techniques such as radiography and in-situ metallography are also presented.

Prediction of fracture profile using digital image correlation

Digital Image Correlation (DIC) based full field strain mapping methodology is used for mapping strain on an aluminum sample subjected to tensile deformation. The local strains on the surface of the specimen are calculated at different strain intervals. Early localization of strain is observed at a total strain of 0.050ɛ; itself, whereas a visually apparent localization of strain is observed at a total strain of 0.088ɛ;. Orientation of the line of fracture (12.0°) is very close to the orientation of locus of strain maxima (11.6°) computed from the strain mapping at 0.063ɛ itself. These results show the efficacy of the DIC based method to predict the location as well as the profile of the fracture, at an early stage.

Indirectly Excited Eddy Current Sensor

Conventional directly excited eddy current sensor (DECS) cannot be used where direct electrical connectivity is a constraint such as in liquid sodium environments. Utilizing an inductive coupling, an indirectly excited eddy current sensor (IECS) is designed to circumvent this problem. The response of IECS for targets of different electrical conductivities and fill-factors, and positioning accuracy are compared with the performance of an equivalent DECS. The response of IECS to that of DECS is about 70%.

Hybrid Signal Processing Approach for Enhanced Detection of Defects in EC-NDE

In eddy current (EC) testing, presence of lift–off and material property variation makes the defect detection as a challenging task. In view of this, a signal processing approach comprising of independent component analysis (ICA) and wavelet packet analysis (WPA) is attempted. In this paper, performance of hybrid signal processing approach and the resultant improvement in signal-to-ratio (SNR) is discussed.