TY  - JOUR
A1  - Evsevleev, Sergei
A1  - Paciornik, Sidnei
A1  - Bruno, Giovanni
T1  - Advanced deep learning-based 3D microstructural characterization of multiphase metal matrix composites
JF  - Advanced engineering materials
N2  - 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.
KW  - computed tomography
KW  - convolutional neural networks
KW  - deep learning
KW  - metal
KW  - matrix composites
KW  - segmentations
Y1  - 2020
U6  - https://doi.org/10.1002/adem.201901197
SN  - 1438-1656
SN  - 1527-2648
VL  - 22
IS  - 4
PB  - Wiley-VCH
CY  - Weinheim
ER  -