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Deep learning-driven automated microstructural quantification in tungsten heavy alloys using U-net segmentation and image processing
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DOI：10.1016/j.ijrmhm.2026.107719.png)
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摘要
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• Developed deep learning-driven automated algorithm for accurate microstructural parameter quantification of WHA. • Implemented U-Net Segmentation to enhance precision and reduce manual intervention. • Accurate quantification of the microstructural parameters of WHA such as contiguity, dihedral angle, W grain size and distribution, W volume fraction, and W precipitate size and distribution in statistical form.
Keyword:
deep learning
U-Net segmentation
microstructural quantification
tungsten heavy alloys
image processing
期刊
IF:
4.6
论文数:
5.3K
被引数:
1.4W
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Thermo-mechanical processing, microstructure and tensile properties of a tungsten heavy alloy.png)
MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING
MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSINGIF7
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