Automated hydride segmentation and quantification in zirconium alloys using a convolutional neural network-assisted machine learning model
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OpenAlex topics: Nuclear Materials and Properties · Nuclear reactor physics and engineering · Fusion materials and technologies
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References (29)
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1. Corrosion of zirconium alloys used for nuclear fuel cladding10.1146/annurev-matsci-070214-020951 ↗
2. Zirconium alloys in nuclear technology10.1007/BF02843474 ↗
3. A review on hydride precipitation in zirconium alloys10.1016/j.jnucmat.2015.07.014 ↗
4. Hydrogen in zirconium alloys: a review10.1016/j.jnucmat.2019.02.042 ↗
5. Stress-reorientation of hydrides and hydride embrittlement of Zr–2.5 wt% Nb pressure tube alloy10.1016/j.jnucmat.2003.10.009 ↗
6. Isothermal stress reorientation of hydrides in Zr‑2.5Nb pressure tube alloy10.1016/j.jnucmat.2021.153440 ↗
7. Hydrides and delayed hydrogen cracking in zirconium and its alloys10.1179/imr.1983.28.1.92 ↗
8. A threshold selection method from gray-level histograms10.1109/TSMC.1979.4310076 ↗
9. A review on CLAHE based enhancement techniques
10. Adaptive thresholding: a comparative study
11. Image denoising by statistical area thresholding10.1007/s10851-005-4889-z ↗
12. GIMP - GNU Image manipulation program. [Online]. Available: https://www.gimp.org. [Accessed: Jun. 3, 2023].
13. Generative adversarial networks assisted machine learning based automated quantification of grain size from scanning electron microscope back scatter images10.1016/j.matchar.2023.113396 ↗
14. Detection of zirconium hydrides in transmission electron micrographs using deep neural networks10.1016/j.engappai.2022.105573 ↗
15. B.L. DeCost, B. Lei, T. Francis, and E.A. Holm, “High throughput quantitative metallography for complex microstructures using deep learning: a case study in ultrahigh carbon steel,” arXiv:1805.08693, May 2018, 10.48550/arXiv.1805.08693.10.1017/S1431927618015635 ↗
16. A review: deep learning for medical image segmentation using multi-modality fusion
17. Review of convolutional neural network
18. Deep convolution neural networks in computer vision: a review10.5573/IEIESPC.2015.4.1.035 ↗
19. Methods and datasets on semantic segmentation: a review10.1016/j.neucom.2018.03.037 ↗
20. A survey on image data augmentation for deep learning10.1186/s40537-019-0197-0 ↗
21. P. Iakubovskii, “Segmentation models PyTorch,” GitHub repository, 2019. [Online]. Available: https://github.com/qubvel-org/segmentation_models.pytorch. [Accessed: 01 Aug, 2025].
22. U-Net: convolutional networks for biomedical image segmentation
23. 3D U-net: learning dense volumetric segmentation from sparse annotation
24. Effect of radial hydrides on fracture behavior of Zr-2.5Nb pressure tube material10.1016/j.jnucmat.2023.154800 ↗
25. Effect of radial hydride fraction on fracture toughness of CWSR Zr-2.5%Nb pressure tube material between ambient and 300 °C temperatures10.1016/j.jnucmat.2018.06.003 ↗
26. Novel use of three-point bend test for determining the threshold stress for hydride reorientation in Zr-2.5%Nb pressure tube material10.1016/j.jnucmat.2025.155920 ↗
27. Effect of thermal cycling on fracture toughness of hydrided Zr–2.5Nb PT material10.1007/s12666-022-02752-w ↗
28. Effect of deuterium content on fracture toughness of Zr-2.5Nb pressure tube material in the temperature range of ambient to 300 °C10.1016/j.jnucmat.2017.08.040 ↗
29. Watersheds in digital spaces: an efficient algorithm based on immersion simulations10.1109/34.87344 ↗