Predicting the degree of sensitisation of AA5456 alloys through convolution neural networks using microstructural images

Abstract

The degree of sensitisation is a reliable measure of the susceptibility to intergranular corrosion for Al-Mg and Al-Mg-Mn marine-grade alloys (Al 5XXX series). Measuring the degree of sensitisation is an exhaustive and expensive operation, as it requires destructive sectioning and outsourcing the measurement to a laboratory to determine the necessary corrosion risk level. This paper proposes an alternative method to predict degree of sensitisation using a convolutional neural network trained on microstructural images of AA5456 alloy specimens etched with phosphoric acid to reveal β-phase precipitates along grain boundaries. Two convolutional neural network architectures are proposed to predict the degree of sensitisation of AA5456 specimens from 490 microstructural images at 200× and 500× magnification. An overall accuracy of 83% for a continuous prediction convolutional neural network model and 87% for a classification prediction convolutional neural network model. The high classification accuracy reflects the success of the augmentation process, which increased the dataset to 9000 images with an equal distribution of degree of sensitisation targets.