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Assessing crimp of fibres in random networks with 3D imaging

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posted on 2023-04-20, 14:50 authored by Yasasween Hewavidana, Mehmet N Balci, Andy GleadallAndy Gleadall, Behnam Pourdeyhimi, Vadim SilberschmidtVadim Silberschmidt, Emrah DemirciEmrah Demirci
The analysis of fibrous structures using micro-computer tomography (µCT) is becoming more important as it provides an opportunity to characterise the mechanical properties and performance of materials. This study is the first attempt to provide computations of fibre crimp for various random fibrous networks (RFNs) based on µCT data. A parametric algorithm was developed to compute fibre crimp in fibres in a virtual domain. It was successfully tested for six different X-ray µCT models of nonwoven fabrics. Computations showed that nonwoven fabrics with crimped fibres exhibited higher crimp levels than those with non-crimped fibres, as expected. However, with the increased fabric density of the non-crimped nonwovens, fibres tended to be more crimped. Additionally, the projected fibre crimp was computed for all three major 2D planes, and the obtained results were statistically analysed. Initially, the algorithm was tested for a small-size, nonwoven model containing only four fibres. The fraction of nearly straight fibres was computed for both crimped and non-crimped fabrics. The mean value of the fibre crimp demonstrated that fibre segments between intersections were almost straight. However, it was observed that there were no perfectly straight fibres in the analysed RFNs. This study is applicable to approach employing a finite-element analysis (FEA) and computational fluid dynamics (CFD) to model/analyse RFNs.

Funding

The Nonwovens Institute of North Carolina State University, Raleigh, NC, grant number 19-234, 2020

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

Polymers

Volume

15

Issue

4

Publisher

MDPI

Version

  • VoR (Version of Record)

Rights holder

© The authors

Publisher statement

This article is an Open Access article published by MDPI and distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Acceptance date

2023-02-13

Publication date

2023-02-20

Copyright date

2023

eISSN

2073-4360

Language

  • en

Depositor

Yasasween Hewavidana. Deposit date: 19 April 2023

Article number

1050

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