ASSESSING DURABILITY OF COMMERCIAL POWDER COATINGS: EFFECTS OF CORROSION TESTING PROCEDURES ON CORROSION INDICATORS AND COATING MICROSTRUCTURE

Abstract

This thesis presents the results of experimental work aiming at studying the effectiveness of different corrosion testing procedures to generate reliable corrosion indicators that can be used to assess the anticorrosion performance of commercial powder coating systems. Different corrosion indicators, such as the corroded area, the electrochemical impedance response, or the microstructural damage to the coating, have been acquired following natural exposure, cyclic corrosion test ISO-20340, and immersion tests with or without electrochemical polarisation, for a large set of specimens prepared to industrial standards. The overall results indicate that, regardless of the corrosion testing method, on artificially scribed specimens, the corrosion propagation proceeds in two stages; a first stage of relatively rapid propagation, with a clear variation in the values associated to a specific corrosion indicator, followed by a second stage when the propagation is significantly slower, and the values of corrosion indicators are relatively steady. However, despite the similarity in overall qualitative behaviour, little or no correlation was found between the results obtained from natural exposure testing and laboratory-based accelerated techniques. This implies that the relative ranking between different coating systems depends substantially on the testing method adopted. This work provides the basis and highlights the need for further research in accelerated corrosion testing development such as the kinetics of corrosion propagation induced during accelerated testing can be tuned to realistically approximate that observed during natural exposure testing. This Ph.D. thesis is the result of four years of research collaboration between The University of Manchester and AkzoNobel, funded by the European Union’s Horizon 2020 Research and Innovation Programme (ITN-MSCA) through the SUSTICOAT project.

Date of Award	31 Dec 2021
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Xiaorong Zhou (Supervisor) & Michele Curioni (Supervisor)