Deep Learning for Metal Corrosion Control: Can Convolutional Neural Networks Measure Inhibitor Efficiency?

Abstract

The inhibition of corrosion is an important aspect not only from the theoretical viewpoint of physical and material sciences but also from the practical aspect of the frequent exposure and use of metals in our lives. The traditional investigation of this process is done through electrochemical measurements with local and selective inspection of some optical microscopy slides. This paper proposes a more objective and automatic way of examining the effectiveness of the employed inhibitors through convolutional neural networks. In spite of the limitation of the number of samples to few hundreds, as they can be provided from the electrochemical laboratory, the deep learner manages to offer valuable information regarding the entire surface of a metal plate and to distinguish between the states under observation.