@article{EvsevleevPaciornikBruno2020,
  author    = {Evsevleev, Sergei and Paciornik, Sidnei and Bruno, Giovanni},
  title     = {Advanced deep learning-based 3D microstructural characterization of multiphase metal matrix composites},
  series = {Advanced engineering materials},
  volume    = {22},
  journal   = {Advanced engineering materials},
  number    = {4},
  publisher = {Wiley-VCH},
  address   = {Weinheim},
  issn      = {1438-1656},
  doi       = {10.1002/adem.201901197},
  pages     = {6},
  year      = {2020},
  abstract  = {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.},
  language  = {en}
}