Deep learning-driven automated microstructural quantification in tungsten heavy alloys using U-net segmentation and image processing

International Journal of Refractory Metals and Hard Materials 2026

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Authors: Rajneesh Patel, Porika Abidsingh Rajput, G. Prabhu, Pawan K. Sharma, First G.M. Karthik
DOI: 10.1016/j.ijrmhm.2026.107719
Cited by: 2
References: 27

Fields of Study & Subjects

Advanced materials and composites Titanium Alloys Microstructure and Properties Machine Learning in Materials Science

Publication details

Journal / source
International Journal of Refractory Metals and Hard Materials
Publisher
Elsevier BV
Volume
138
Pages
107719–107719
Publication date
7 February 2026
Language
EN