Automated, high-accuracy classification of textured microstructures using a convolutional neural network

Automated, high-accuracy classification of textured microstructures using a convolutional neural network

Crystallographic texture is an important descriptor of material properties but requires time-intensive electron backscatter diffraction (EBSD) for identifying grain orientations. While some metrics such as grain size or grain aspect ratio can distinguish textured microstructures from untextured micr...

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Bibliographic Details
Main Authors: Ishan D. Khurjekar, Bryan Conry, Michael S. Kesler, Michael R. Tonks, Amanda R. Krause, Joel B. Harley
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-01-01
Series:Frontiers in Materials
Subjects:
microstructure
texture
feature extraction
machine learning (ML)
convolutional neural network
Online Access:https://www.frontiersin.org/articles/10.3389/fmats.2023.1086000/full

Internet

https://www.frontiersin.org/articles/10.3389/fmats.2023.1086000/full

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