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Pablo Villanueva Perez

Senior lecturer

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Deep-learning image enhancement and fibre segmentation from time-resolved computed tomography of fibre-reinforced composites

Author

  • Rui Guo
  • Johannes Stubbe
  • Yuhe Zhang
  • Christian Matthias Schlepütz
  • Camilo Rojas Gomez
  • Mahoor Mehdikhani
  • Christian Breite
  • Yentl Swolfs
  • Pablo Villanueva-Perez

Summary, in English

Monitoring the microstructure and damage development of fibre-reinforced composites during loading is crucial to understanding their mechanical properties. Time-resolved X-ray computed tomography enables such an in-situ, non-destructive study. However, the photon flux and fibre-matrix contrast limit its achievable spatial and temporal resolution. In this paper, we push the limits of temporal and spatial resolution for the microstructural analysis of unidirectional continuous carbon fibre-reinforced epoxy composites by establishing a new pipeline based on CycleGAN for unsupervised super-resolution and denoising and U-Net-id for individual fibre segmentation. After illustrating the benefits of a 3D CycleGAN over a 2D one, we show that data enhanced by this pipeline can yield similar segmentation quality to that of a slow-acquisition, high-quality scan that took up to 200 times longer to acquire. This pipeline, therefore, enables more robust data extraction from fast time-resolved X-ray tomography, removing a critical stumbling block for this technique.

Department/s

  • Synchrotron Radiation Research
  • NanoLund: Centre for Nanoscience
  • LTH Profile Area: Nanoscience and Semiconductor Technology
  • LU Profile Area: Light and Materials
  • LTH Profile Area: Photon Science and Technology

Publishing year

2023-11-10

Language

English

Publication/Series

Composites Science and Technology

Volume

244

Document type

Journal article

Publisher

Elsevier

Topic

  • Composite Science and Engineering

Keywords

  • A. Carbon fibre
  • A. Polymer-matrix composites
  • D. Non-destructive testing
  • D. X-ray computed tomography
  • Deep learning

Status

Published

ISBN/ISSN/Other

  • ISSN: 0266-3538