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A stochastic modelling framework for predicting flexural properties of ultra-thin randomly oriented strands (1).pdf (5.34 MB)

A stochastic modelling framework for predicting flexural properties of ultra-thin randomly oriented strands

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posted on 2024-05-22, 16:19 authored by Xiaodong Xu, Andre JesusAndre Jesus, Yi Wan, Jun Takahashi

A stochastic modelling framework is developed to predict the flexural properties of high-strength sheet moulding compounds made of randomly oriented ultra-thin carbon fibre-reinforced thermoplastic prepreg tapes. The model enables reliable designs using ultra-thin randomly oriented strands with less testing, leading to potential applications in automotive primary structures. The stochastic model is based on a Monte Carlo simulation. The flexural modulus is predicted using classical laminate theory, while the flexural strength is predicted by following a Weibull distribution. Fibre discontinuities are considered through stress concentrations introduced by tape overlaps. The results are validated against new 3-point bending and previously reported 4-point bending experimental results. A scaling effect on strength and scatter is predicted, and its implications for structural applications are also discussed. 

Funding

Royal Society (IEC\R3\213017)

History

School

  • Architecture, Building and Civil Engineering

Published in

Advanced Composite Materials

Publisher

Informa UK

Version

  • VoR (Version of Record)

Rights holder

© The Author(s)

Publisher statement

This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial- NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribu-tion, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

Acceptance date

2024-05-01

Publication date

2024-05-09

Copyright date

2024

ISSN

0924-3046

eISSN

1568-5519

Language

  • en

Depositor

Dr Andre Jesus. Deposit date: 11 May 2024

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