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

• 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.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Materialteknik -- Kompositmaterial och -teknik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Materials Engineering -- Composite Science and Engineering (hsv//eng)

Nyckelord

A. Carbon fibre

A. Polymer-matrix composites

D. Non-destructive testing

D. X-ray computed tomography

Deep learning

Publikations- och innehållstyp

art (ämneskategori)

ref (ämneskategori)