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Advanced Deep Learning‐Based 3D Microstructural Characterization of Multiphase Metal Matrix Composites
Author(s) -
Evsevleev Sergei,
Paciornik Sidnei,
Bruno Giovanni
Publication year - 2020
Publication title -
advanced engineering materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.938
H-Index - 114
eISSN - 1527-2648
pISSN - 1438-1656
DOI - 10.1002/adem.201901197
Subject(s) - materials science , characterization (materials science) , convolutional neural network , synchrotron , workflow , segmentation , deep learning , composite material , matrix (chemical analysis) , particle (ecology) , phase (matter) , artificial intelligence , nanotechnology , computer science , optics , oceanography , physics , database , geology , chemistry , organic chemistry
The quantitative analysis of microstructural features is a key to understanding the micromechanical behavior of metal matrix composites (MMCs), which is a premise for their use in practice. Herein, a 3D microstructural characterization of a five‐phase MMC is performed by synchrotron X‐ray computed tomography (SXCT). A workflow for advanced deep learning‐based segmentation of all individual phases in SXCT data is shown using a fully convolutional neural network with U‐net architecture. High segmentation accuracy is achieved with a small amount of training data. This enables extracting unprecedently precise microstructural parameters (e.g., volume fractions and particle shapes) to be input, e.g., in micromechanical models.