Advanced Deep Learning‐Based 3D Microstructural Characterization of Multiphase Metal Matrix Composites

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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.

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: Advanced Deep Learning‐Based 3D Microstructural Characterization of Multiphase Metal Matrix Composites ; volume:22 ; number:4 ; year:2020 ; extent:6
Advanced engineering materials ; 22, Heft 4 (2020) (gesamt 6)

Creator: Evsevleev, Sergei
Paciornik, Sidnei
Bruno, Giovanni

DOI: 10.1002/adem.201901197

URN: urn:nbn:de:101:1-2022061807025583332067

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:38 AM CEST

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Evsevleev, Sergei
Paciornik, Sidnei
Bruno, Giovanni

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Advanced Deep Learning‐Based 3D Microstructural Characterization of Multiphase Metal Matrix Composites

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Other Objects (12)

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