P. Kalyanasundaram

A TUNABLE OPTICAL FILTER

We present the details of a new tunable optical filter, suitable for selecting different bands of wavelength in the UV, visible and IR regions. The filter comprises a ferrofluid-based emulsion cell (emulsion sandwiched between two transparent sheets), a miniature solenoid and a variable direct current source for changing the magnetic field inside the solenoid. By varying the magnetic field, one can tune the filter and select the desired wavelength. We discuss the working principle of the new tunable optical filter with a few examples.

An artificial neural network for eddy current testing of austenitic stainless steel welds

An artificial neural network is presented for on-line eddy current testing of austenitic stainless steel welds. Time-domain parameters that are functions of digitized in-phase and quadrature components of probe impedance are used as input to the neural network and the network output, in depth units, is evaluated and displayed continuously. The neural network is trained to recognize disturbing variables such as variations in weld microstructure, lift-off and edge-effect as well as notches of different depth. The neural network is able to detect and characterize longitudinal and transverse surface-breaking notches, despite the presence of disturbing variables.

Acoustic emission source location on a cylindrical surface

Acoustic emission (AE) is a stress wave, generated in a structural material when it is stimulated by perturbations. One of the important features of this technique is to detect the location of the AE source in the structure from the differences in the times of arrival of stress waves at several sensors placed on it. A generalized mathematical formalism for the evaluation of the path travelled by an acoustic wave on the surface of a structure, on the concept of geodesics, is presented. Based on this concept, a mathematical method is developed for the calculation of the coordinates of an AE source on a cylindrical surface by three AE sensors. This concept has not been attempted analytically so far, to our knowledge.

Improving the evaluation sensitivity of an ultrasonic pulse echo technique using a neural network classifier

In this paper, the use of an artificial neural network (ANN) for classifying weak ultrasonic signals has been attempted. The limitations of using a single conventional parameter for signal detection and classification (namely peak amplitude alone) are highlighted. Use of a multi-parameter approach is suggested. The ANN used is a multi-layered, feedforward, error-backpropagation network. Results are compared with those of conventional approaches.

Structural integrity assessment of the containment structure of a pressurised heavy water nuclear reactor using impact echo technique

The impact echo technique is based on the use of transient stress waves for non-destructive detection of flaws in concrete structures. Impact-echo testing has been carried out for assessment of the structural integrity of the ring beam of a pressurised heavy water nuclear reactor. In order to develop the test procedure for carrying out impact echo testing, mock up calibration blocks were made. The detectability of the impact echo system has also been established in terms of the depth and the lateral dimension of the detectable flaw for the ring beam under consideration. Based on the optimised test parameters identified with the help of the studies carried out on the mock up blocks, impact echo testing was carried out on the ring beam of the reactor containment structure, for assessing its structural integrity.

Acoustic emission technique for leak detection in an end shield of a pressurised heavy water reactor

This paper discusses a successful application of the Acoustic Emission Technique (AET) for the detection and location of leak paths present on an inaccessible side of an end shield of a Pressurised Heavy Water Reactor (PHWR). The methodology was based on the fact that air- and water-leak AE signals have different characteristic features. Baseline data was generated from a sound end shield of a PHWR for characterising the background noise. A mock-up end shield system with saw-cut leak paths was used to verify the validity of the methodology. It was found that air-leak signals under pressurisation (as low as 3 psi) could be detected by frequency domain analysis. Signals due to air leaks from various locations of defective end shield were acquired and analysed. It was possible to detect and locate leak paths. The presence of detected leak paths was further confirmed by an alternative test.

AN INTELLIGENT IMAGING SCHEME FOR AUTOMATED EDDY CURRENT TESTING

An intelligent imaging scheme involving artificial neural networks and image processing methods is presented for automated eddy current testing of austenitic stainless steel plates. A quick coarse-scan imaging is performed to identify all the defective regions, which are later subjected to detailed fine-scan imaging to produce accurate threedimensional pictures of the defects. Experimental studies confirm that the imaging scheme is insensitive to disturbing variables and is able to significantly reduce the imaging time as well as the data storage requirements. Details of the scheme and its applications are given.

Acoustic emission studies on welded and thermally treated AISI 304 stainless steel during tensile deformation

The present investigations are planned to study the influence of prior martensites formed due to cold treatment as 77K in AISI 304 SS welded specimens, on strain-induced martensites occurred during tensile deformation using AE technique. AE parameters like count rate and root mean square (r.m.s.) voltage have been used to characterize AE activities generated during tensile deformation process in as-welded and welded-treated samples. Frequency spectrum analysis of AE signals captured from the samples has been done to understand the dynamic behavior of the martensite phase formation. Tensile properties of these samples have also been reported. Volume fraction of the magnetic phase (martensite and delta ferrite) formed in these samples are measured before and after straining. X-ray diffraction (XRD) technique has been used to support the presence of delta ferrite (formed during welding) and martensite in the weld region.

Reliability of detection of small defects in noisy weldments by advanced signal processing and pattern recognition techniques

Abstract Reliable detection of a defect (3 mm long × 1 mm deep) in the top mid-section of maraging steel weldment poses problems during conventional pulse-echo ultrasonic testing. Acoustic noise due to coarse and textured microstructure in the top mid-section adversely influences the signal-to-noise ratio and thus the detection. The reliable detection of a small defect in rocket motor components is essential to achieve the designed payload capacity of the space vehicle. Advanced signal analysis techniques such as autocorrelation, demodulation, etc., were applied to the detection of a simulated fatigue crack on the top mid-section of the weldment. Confidence levels of only 60 to 85% could be achieved. When a combination of cluster analysis of cross-power data and Gaussian distribution function were applied, a confidence level of 95% for defect detection could be achieved.

A New Approach for Restoration of Eddy Current Images

Eddy current images of defects are blurred due to convolution of point spread function of eddy current probe with defects. Disturbing variables such as lift-off, surface roughness, and material property variations influence the eddy current images. In order to restore the length, width, depth, and orientation of surface-breaking defects in the presence of disturbing variables, a new and comprehensive approach has been developed. This approach uses artificial neural network and image processing methods. Studies on austenitic steel plates confirm that through this approach it is possible to restore the spatial information of surface-breaking defects of uniform or slowly varying depth and also to form their accurate three-dimensional pictures. This approach is fast as well as amenable for automation.