A review of deep learning in the study of materials degradation. (December 2018)

• Record Type: Journal Article
• Authors: Nash, Will; Drummond, Tom; Birbilis, Nick
• Is Part Of: Npj Materials degradation. Volume 2(2018)
• Page Start: 1
• Page End: 12
• Publication Date: 2018-12
• Subjects: Materials -- Deterioration -- Periodicals; Materials -- Testing -- Periodicals; 620.110287
• DOI: 10.1038/s41529-018-0058-x
• Languages: English
• ISSNs: 2397-2106

Abstract

Abstract Deep learning is revolutionising the way that many industries operate, providing a powerful method to interpret large quantities of data automatically and relatively quickly. Deterioration is often multi-factorial and difficult to model deterministically due to limits in measurability, or unknown variables. Deploying deep learning tools to the field of materials degradation should be a natural fit. In this paper, we review the current research into deep learning for detection, modelling and planning for material deterioration. Driving such research are factors such as budget reductions, increasing safety and increasing detection reliability. Based on the available literature, researchers are making headway, but several challenges remain, not least of which is the development of large training data sets and the computational intensity of many of these deep learning models.