Although much research has been done on zirconium hydrides, one of the major challenges has been the lack of an efficient and unbiased method to analyze these hydrides in both 2D and 3D. This is where the use of deep learning (DL) algorithms comes in. DL methods have shown great potential in tackling various materials science problems, especially in recognizing and classifying microstructural features with a high degree of accuracy and reliability.

The aim of this research is to apply DL techniques to detect and extract hydride features from datasets, allowing for the development of functions that can quantify hydride characteristics such as their length, orientation, and connectivity. By achieving this, the project hopes to provide, for the first time, a reliable quantitative analysis of hydride microstructures and, ultimately, gain a deeper understanding of their precipitation behaviour and how it impacts the overall performance of the cladding material.