Automated detection and quantification of the onset of undercoating corrosion using pulsed thermography

Abstract

Corrosion of metals can occur under coatings making early visual detection almost impossible which enables corrosion to progress undetected. Nondestructive evaluation can be used to inspect parts during their life. Thermography uses the difference in thermal properties between defective and sound areas to locate defects by applying a short pulse of heat and monitoring the surface temperature of the sample with an infrared camera. However, currently, inspection is reliant on highly trained operators to analyze images for defects and in the case of corrosion, judge its growth. The aim of this research was to develop and test a novel algorithm to identify and quantify the onset of corrosion under coatings and monitor the growth. Using this algorithm, the corrosion was autonomously detected in a variety of mild steel samples exposed to three different corrosive environments. The method successfully detected, quantified, and monitored the growth of corrosion under coatings accurately and robustly using thermal images gathered by a microbolometer infrared camera. The extent of corrosion was reported with an accuracy of 3% absolute error when compared to the true extent.