Phase segmentation of uncured prepreg X-Ray CT micrographs

Pedro Galvez Hernandez, Karolina Gaska, James Kratz*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

14 Citations (Scopus)
52 Downloads (Pure)

Abstract

This paper explores methods to investigate voids and dry fibre areas in pre-impregnated aligned carbon fibre reinforced epoxy lay-ups. A deep learning segmentation approach was compared to conventional thresholding techniques to characterise the interlaminar voids (entrapped air) and dry areas (unsaturated fibre bed) phases obtained by micro-CT scanning of samples from uncured laminates. The performance of both approaches was quantitatively assessed in three regions of interest having different levels of porosity, ranging from a low 1% to a high 25%. Deep learning consistently outperformed thresholding in the segmentation of both interlaminar voidage and dry areas. Furthermore, deep learning improved the ability to detect small voids and was able to accurately segment voids in volumes with less than 2% voidage, whereas thresholding techniques fail in this task. Finally, the application of deep learning to the segmentation of dry areas in micro-CT scans provided sharper results than thresholding, without needing filtering.
Original languageEnglish
Article number106527
Number of pages14
JournalComposites Part A: Applied Science and Manufacturing
Volume149
Early online date17 Jun 2021
DOIs
Publication statusPublished - 1 Oct 2021

Bibliographical note

Funding Information:
The authors would like to acknowledge the Engineering and Physical Sciences Research Council (EPSRC) for their support of this research through Investigation of Fine-Scale Flows in Composites Processing [ EP/S016996/1 ]. A PhD studentship for P. Galvez-Hernandez was supported through the Rolls-Royce Composites University Technology Centre at the University of Bristol.

Funding Information:
The authors would like to acknowledge the Engineering and Physical Sciences Research Council (EPSRC) for their support of this research through Investigation of Fine-Scale Flows in Composites Processing [EP/S016996/1]. A PhD studentship for P. Galvez-Hernandez was supported through the Rolls-Royce Composites University Technology Centre at the University of Bristol.

Publisher Copyright:
© 2021 The Authors

Keywords

  • A. Prepreg
  • B. Porosity
  • D. CT analysis
  • Machine learning

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