Predictions of the mechanical properties of unidirectional fibre composites by supervised machine learning
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Published version
Author(s)
Type
Journal Article
Abstract
We present an application of data analytics and supervised machine learning to allow accurate
predictions of the macroscopic stiffness and yield strength of a unidirectional composite loaded in the
transverse plane. Predictions are obtained from the analysis of an image of the material microstructure,
as well as knowledge of the constitutive models for fibres and matrix, without performing physicallybased calculations. The computational framework is based on evaluating the 2-point correlation function
of the images of 1800 microstructures, followed by dimensionality reduction via principal component
analysis. Finite element (FE) simulations are performed on 1800 corresponding statistical volume
elements (SVEs) representing cylindrical fibres in a continuous matrix, loaded in the transverse plane.
A supervised machine learning (ML) exercise is performed, employing a gradient-boosted tree
regression model with 10-fold cross-validation strategy. We show how the model obtained is able to
accurately predict the homogenized properties of arbitrary microstructures without performing FE
calculations of their response.
predictions of the macroscopic stiffness and yield strength of a unidirectional composite loaded in the
transverse plane. Predictions are obtained from the analysis of an image of the material microstructure,
as well as knowledge of the constitutive models for fibres and matrix, without performing physicallybased calculations. The computational framework is based on evaluating the 2-point correlation function
of the images of 1800 microstructures, followed by dimensionality reduction via principal component
analysis. Finite element (FE) simulations are performed on 1800 corresponding statistical volume
elements (SVEs) representing cylindrical fibres in a continuous matrix, loaded in the transverse plane.
A supervised machine learning (ML) exercise is performed, employing a gradient-boosted tree
regression model with 10-fold cross-validation strategy. We show how the model obtained is able to
accurately predict the homogenized properties of arbitrary microstructures without performing FE
calculations of their response.
Date Issued
2019-09-27
Date Acceptance
2019-09-03
Citation
Scientific Reports, 2019, 9 (1)
ISSN
2045-2322
Publisher
Nature Publishing Group
Journal / Book Title
Scientific Reports
Volume
9
Issue
1
Copyright Statement
© The Author(s) 2019. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Sponsor
Innovate UK
Grant Number
110123
Subjects
0601 Biochemistry and Cell Biology
0299 Other Physical Sciences
Publication Status
Published
Article Number
13964 (2019)
Date Publish Online
2019-09-27