Development of Effective Corrosion Testing Approaches for Performance Evaluation of Organic Coatings

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The aim of the PhD is to develop and apply innovative and efficient corrosion testing methods that can generate rapidly corrosion and performance data to support machine learning activities. The PhD activity will focus on designing and implementing corrosion testing approaches exploiting electrochemistry, imaging and other methods, such as reliable, standardized, and representative data can be obtained for a variety of coating systems and correlated to long term performance information. The data obtained will then be used to train machine learning algorithms aiming at optimizing new coatings formulations. One of the key challenges to be addressed is the acceleration in the laboratory of specific processes that are responsible for failure in field applications. To approach the challenge, information from modelling activities and from high-resolution imaging, will be exploited. Once suitable methods for corrosion testing are developed, the data produced and the methodologies developed will also be exploited to enhance fundamental understanding of long-term failure processes.