Predictions of the mechanical properties of unidirectional fibre composites by supervised machine learning

M V Pathan; S A Ponnusami; J Pathan; R Pitisongsawat; B Erice; N Petrinic; V L Tagarielli

doi:10.1038/s41598-019-50144-w

Predictions of the mechanical properties of unidirectional fibre composites by supervised machine learning

Sci Rep. 2019 Sep 27;9(1):13964. doi: 10.1038/s41598-019-50144-w.

Authors

M V Pathan¹, S A Ponnusami^{1

2}, J Pathan³, R Pitisongsawat⁴, B Erice¹, N Petrinic¹, V L Tagarielli^{5

6}

Affiliations

¹ Engineering Science, University of Oxford, Oxford, OX1 3PJ, UK.
² Mechanical Engineering and Aeronautics, City, University of London, London, EC1V 0HB, UK.
³ J P Consultancy Specialists Ltd, Bristol, BS9 3LA, UK.
⁴ Aeronautics, Imperial College London, London, SW7 2AZ, UK.
⁵ Engineering Science, University of Oxford, Oxford, OX1 3PJ, UK. v.tagarielli@imperial.ac.uk.
⁶ Aeronautics, Imperial College London, London, SW7 2AZ, UK. v.tagarielli@imperial.ac.uk.

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 physically-based 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. The model obtained is able to accurately predict the homogenized properties of arbitrary microstructures.