|ijphm_17_005.pdf||3.98 MB||March 27, 2017 - 4:00pm|
It has been established that corrosion is one of the most important factors causing deterioration, loss of metal, and ultimately decrease of product performance and reliability in critical aerospace and industrial systems. Corrosion monitoring, accurate detection and quantification are recognized as key enabling technologies to reduce the impact of corrosion on the integrity of critical aircraft and industrial assets. Accurate and reliable detection of corrosion initiation and propagation with specified false alarm rates requires novel tools and methods, including verifiable modeling methods. This paper reports results from an experimental investigation of pitting corrosion detection and quantification on aluminum alloy panels using 3D surface metrology methods and image processing techniques. Panel surface characterization was evaluated with laser microscopy and stylus-based profilometry to obtain global and local surface images. Promising imaging and texture features were extracted and compared between the uncoated and coated panel sides, as well as on the uncoated sides under different corrosion exposure times. In the evaluation process, image processing, information processing, and data mining techniques were utilized. A new modeling framework for various corrosion stages is introduced emphasizing the representation of corrosion pitting and cracking processes. Detection and prediction of the evolution of corrosion stages relies on data, an estimation method called particle filtering, and the corrosion propagation model. Results from these experimental studies demonstrate the efficacy of the proposed methodology.