Imperial College London
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DrVitoTagarielli

Faculty of Engineering, Department of Aeronautics

Reader in Mechanics of Solids
 
 
 
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Contact

 

+44 (0)20 7594 5167v.tagarielli

 
 
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Location

 

218City and Guilds BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Pathan:2019:10.1038/s41598-019-50144-w,
author = {Pathan, M and ponnusami, S and pathan, J and pitisongsawat, R and Erice, B and Petrinic, N and Tagarielli, V},
doi = {10.1038/s41598-019-50144-w},
journal = {Scientific Reports},
title = {Predictions of the mechanical properties of unidirectional fibre composites by supervised machine learning},
url = {http://dx.doi.org/10.1038/s41598-019-50144-w},
volume = {9},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - We present an application of data analytics and supervised machine learning to allow accuratepredictions of the macroscopic stiffness and yield strength of a unidirectional composite loaded in thetransverse 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 functionof the images of 1800 microstructures, followed by dimensionality reduction via principal componentanalysis. Finite element (FE) simulations are performed on 1800 corresponding statistical volumeelements (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 treeregression model with 10-fold cross-validation strategy. We show how the model obtained is able toaccurately predict the homogenized properties of arbitrary microstructures without performing FEcalculations of their response.
AU  - Pathan,M
AU  - ponnusami,S
AU  - pathan,J
AU  - pitisongsawat,R
AU  - Erice,B
AU  - Petrinic,N
AU  - Tagarielli,V
DO  - 10.1038/s41598-019-50144-w
PY  - 2019///
SN  - 2045-2322
TI  - Predictions of the mechanical properties of unidirectional fibre composites by supervised machine learning
T2  - Scientific Reports
UR  - http://dx.doi.org/10.1038/s41598-019-50144-w
UR  - http://hdl.handle.net/10044/1/73189
VL  - 9
ER  -
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