TY - JOUR A1 - Evsevleev, Sergei A1 - Paciornik, S. A1 - Bruno, Giovanni T1 - Advanced Deep Learning-Based 3D Microstructural Characterization of Multiphase Metal Matrix Composites 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 matrix composites KW - Segmentations PY - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-504261 SN - 1438-1656 VL - 22 IS - 4 SP - 1901197 PB - WILEY-VCH Verlag GmbH & Co. KGaA CY - Weinheim AN - OPUS4-50426 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -