Abstract
Thermographic inspection provides opportunity to tailor non-destructive evaluation to specific applications. The paper discusses the opportunities this presents through consideration of adhesive bonds between composites, such as those joining structural members and outer skins, where access is restricted to a single side. To date, literature focusses on the development of either an experimental procedure or data processing approach. This research aims to demonstrate the importance of tailoring both of these aspects to an application to obtain improved defect detection and robust quantification. Firstly, the heating stimulus is optimised to maximise the thermal contrast created between defect and non-defect regions using a development panel. Traditional flash heating is compared to longer square pulse heating, using a developed shutter system, compromising between experimental duration and heat input. A pulse duration of 4 seconds using two 130 W halogen bulbs was found double the detection depth from 1 mm to 2 mm, revealing all defects in the development panel. Temporal processing was maintained for all data using thermal signal reconstruction. Spatial defect detection routines were then implemented to provide robust defect/feature detection. Spatial defect detection encompassed a combination of image enhancement and edge detection algorithms. A two-stage kernel filter/binary enhancement method followed by the use of Canny edge detection was found most robust, providing a sizing error of 1.8 % on the development panel data. This process was then implemented on adhesive bonds with simulated bond line defects. The simulated defects are based on target detection threshold of 10 mm diameter void found at 1- 2 mm depth. All simulated void defects were detected in the representative bonded joint down to the minimum diameter tested of 5 mm. By considering the tailoring of multiple aspects of the inspection routine independently, an overall optimised approach for the application of interest has been defined.
Hardware Implementation of Sobel Edge Detection Algorithm
