Efficient processing of μCT images using deep learning tools for generating digital material twins of woven fabrics

Muhammad A. Ali, Qiangshun Guan, Rehan Umer*, Wesley J. Cantwell, Tiejun Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)

Abstract

The greatest challenge in creating digital material twins from μCT images is the lack of a robust and versatile tool for segmenting the μCT images and post-processing the segmented volumes into a FE mesh. Here, we have used deep convolutional neural networks (DCNN) for segmenting μCT images of a multi-layer plain-woven fabric. First, a set of raw 2D image slices extracted from the gray-scale volume of a single-layer fabric was used to train a DCNN using manually annotated images. The trained DCNN was then tested using some “unseen” manually segmented images, resulting in more than 96% global accuracy. Moreover, the trained DCNN was also used to segment unseen images from a multilayer stack of the fabric with good accuracy. A novel procedure based on the “watershed segmentation” technique was also successfully developed to separate individual yarns from connected yarn cross-sections during post-processing of segmented volumes. The work presented here provides a robust and efficient framework of segmenting CT scan images of woven fabrics for generating their digital material twins and FE mesh.

Original languageEnglish
Article number109091
JournalComposites Science and Technology
Volume217
Early online date10 Oct 2021
DOIs
Publication statusPublished - 5 Jan 2022

Keywords

  • CT Analysis
  • Deep learning
  • Fabrics/textiles
  • Microstructures
  • Process modeling

ASJC Scopus subject areas

  • Ceramics and Composites
  • General Engineering

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