Detection of zirconium hydrides in transmission electron micrographs using deep neural networks. (January 2023)

• Record Type: Journal Article
• Authors: Ni, Yezhou; Topham, Robert; Skippon, Travis; Zhang, Jun-Tian; Hanlon, Sean; Long, Fei; Anghel, Catalina; Torres, Edmanuel; Daymond, Mark R.; Béland, Laurent K.
• Is Part Of: Engineering applications of artificial intelligence. Volume 117:Part B(2023)
• Publication Date: 2023-01
• Subjects: Computer vision -- Zirconium -- Hydrides -- Zr-2.5Nb -- Transmission electron microscopy -- Deep learning; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105573
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Zirconium alloys are commonly employed in nuclear power applications. Under typical operating conditions, hydrogen ingress can lead to the formation of brittle Zr hydrides in the alloy. To study this behavior, transmission electron microscopy (TEM) is routinely used to image hydrides in Zr-alloys. However, the analysis of these TEM micrographs is a complex time-consuming task. Here, we employed a mask region-based convolutional neural network (Mask R-CNN) to automate an essential part of the analysis process: the identification and annotation of hydrides. In addition, although training a neural network usually requires large training datasets (in the order of thousands of images), the proposed framework was developed using a limited training dataset with the recourse of transfer learning. This work shows that the Mask R-CNN is capable of correctly and quickly labeling thermo-mechanically cycled hydrides in TEM images of pressure tube material.