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

博士学位的目的是开发和应用创新和高效的腐蚀测试方法，可以快速生成腐蚀和性能数据，以支持机器学习活动。博士课程将侧重于设计和实施利用电化学、成像和其他方法的腐蚀测试方法，例如可以获得各种涂层系统的可靠、标准化和代表性数据，并与长期性能信息相关。获得的数据将用于训练旨在优化新涂料配方的机器学习算法。需要解决的关键挑战之一是在实验室中加速导致现场应用失败的特定过程。为了应对这一挑战，将利用来自建模活动和高分辨率成像的信息。一旦开发出合适的腐蚀测试方法，所产生的数据和开发的方法也将用于加强对长期失效过程的基本理解。

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.