Papers by Author: Chin Kian Liew

Abstract: This study proposes a new nondestructive evaluation methodology named laser lock-in thermography (LLT) for fatigue crack detection. LLT utilizes a high power continuous wave (CW) laser as a heat generation source for lock-in thermography instead of commonly used flash and halogen lamps. The advantages of the proposed LLT method are that (1) the laser heat source can be positioned at an extended distance from a target structure thank to the directionality and low energy loss of the laser source, (2) thermal image degradation due to surrounding temperature disturbances can be minimized because of high temperature gradient generated by the laser source and (3) a large target surface can be inspected using a scanning laser heat source. The developed LLT system is composed of a modulated high power CW laser, galvanometer and infrared camera. Then, a holder exponent-based data processing algorithm is proposed for intuitive damage evaluation. The developed LLT is employed to detect a micro fatigue crack in a metal plate. The test result confirms that 5 μm (or smaller) fatigue crack in a dog-bone shape aluminum plate with a dimension of 400 x 140 x 3 mm³ can be detected.

Abstract: An approach to identify damages in materials particularly aerospace composites has been developed based on application of wavelet analysis on guided wave transient signals. The wave response was convoluted against a suitable wavelet to present information in the time-frequency domain for baseline comparison of signals. By evaluating the time-frequency conditional moment, sensitive indices were computed to identify the presence of damage. Normalisation of these indices was found to be an important procedure to reduce measurement discrepancies from baseline comparison. Extending this further was the correlation of these parameters to pulse propagation time from actuator to sensor positions to generate a tomographic representation of the damage in the scanned material. These damage evaluation processes were investigated in an ultrasonic health monitoring system inspecting carbon fibre reinforced composite panels with simulated delamination. The imaging results displayed positive damage characterisation capabilities for implementation within the methodology of smart or intelligent materials.

Abstract: In this research, an advanced signal processing technique using wavelet analysis has been developed for a guided wave structural health monitoring system. The approach was applied for the detection of delamination in carbon fibre reinforced composites. A monolithic piezoceramic actuator was attached to a laminate plate for wave generation while laser vibrometry was used to facilitate the measurements of the wave response in a sensor network. This database of wave response was then processed using the continuous wavelet transform to obtain the positional frequency content. Transforms between damaged and undamaged states were compared to ascertain the presence of defects by evaluating the total energy of the time-frequency density function. Results show high damage detection indices depending on the location of the sensor and normalisation factor applied while there are positive indications that this methodology can be extended for damage characterisation.