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Predictions of macroscopic mechanical properties and microscopic cracks of unidirectional fibre-reinforced polymer composites using deep neural network (DNN)

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Predictions of macroscopic mechanical properties and microscopic cracks of unidirectional fibre-reinforced polymer composites using deep neural network (DNN). / Ding, Xiaoxuan; Hou, Xiaonan; Xia, Min et al.

In: Composite Structures, Vol. 302, 116248, 15.12.2022.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Ding, X, Hou, X, Xia, M, Ismail, Y & Ye, J 2022, 'Predictions of macroscopic mechanical properties and microscopic cracks of unidirectional fibre-reinforced polymer composites using deep neural network (DNN)', Composite Structures, vol. 302, 116248. https://doi.org/10.1016/j.compstruct.2022.116248

APA

Ding, X., Hou, X., Xia, M., Ismail, Y., & Ye, J. (2022). Predictions of macroscopic mechanical properties and microscopic cracks of unidirectional fibre-reinforced polymer composites using deep neural network (DNN). Composite Structures, 302, [116248]. https://doi.org/10.1016/j.compstruct.2022.116248

Vancouver

Ding X, Hou X, Xia M, Ismail Y, Ye J. Predictions of macroscopic mechanical properties and microscopic cracks of unidirectional fibre-reinforced polymer composites using deep neural network (DNN). Composite Structures. 2022 Dec 15;302:116248. Epub 2022 Sep 24. doi: 10.1016/j.compstruct.2022.116248

Author

Ding, Xiaoxuan ; Hou, Xiaonan ; Xia, Min et al. / Predictions of macroscopic mechanical properties and microscopic cracks of unidirectional fibre-reinforced polymer composites using deep neural network (DNN). In: Composite Structures. 2022 ; Vol. 302.

Bibtex

@article{9a6b9f3eeaee4d7a9deb9351a2753cfb,
title = "Predictions of macroscopic mechanical properties and microscopic cracks of unidirectional fibre-reinforced polymer composites using deep neural network (DNN)",
abstract = "Fibre-reinforced polymer (FRP) composites have been widely used in different engineering sectors due to their excellent physical and mechanical properties. Therefore, fast, convenient and accurate prediction tools for both macroscopic mechanical properties and failure of the composites are highly demanded by industry and interested by academia. In this study, two back-propagation deep neural network (DNN) models are developed. The first model is a regression model for predicting macroscopic transverse mechanical properties of FRP laminae, which is based on a data set generated by Discrete Element Method (DEM) simulations of 2000 Representative Volume Element (RVE) with 200 different sets of fibre volume fractions and fibre radii. The second model, which is a classification model based on the results of 1600 DEM simulations of RVEs with a fixed 45 % fibre volume fraction and 3.3μm fibre radius, is developed for predicting microscopic crack patterns of the FRP laminae. The results show that the two developed DNN models are able to predict both the macroscopic transverse mechanical properties and the microscopic cracks of the RVE accurately.",
author = "Xiaoxuan Ding and Xiaonan Hou and Min Xia and Yaser Ismail and Jianqiao Ye",
year = "2022",
month = dec,
day = "15",
doi = "10.1016/j.compstruct.2022.116248",
language = "English",
volume = "302",
journal = "Composite Structures",
issn = "0263-8223",
publisher = "Elsevier Ltd",

}

RIS

TY - JOUR

T1 - Predictions of macroscopic mechanical properties and microscopic cracks of unidirectional fibre-reinforced polymer composites using deep neural network (DNN)

AU - Ding, Xiaoxuan

AU - Hou, Xiaonan

AU - Xia, Min

AU - Ismail, Yaser

AU - Ye, Jianqiao

PY - 2022/12/15

Y1 - 2022/12/15

N2 - Fibre-reinforced polymer (FRP) composites have been widely used in different engineering sectors due to their excellent physical and mechanical properties. Therefore, fast, convenient and accurate prediction tools for both macroscopic mechanical properties and failure of the composites are highly demanded by industry and interested by academia. In this study, two back-propagation deep neural network (DNN) models are developed. The first model is a regression model for predicting macroscopic transverse mechanical properties of FRP laminae, which is based on a data set generated by Discrete Element Method (DEM) simulations of 2000 Representative Volume Element (RVE) with 200 different sets of fibre volume fractions and fibre radii. The second model, which is a classification model based on the results of 1600 DEM simulations of RVEs with a fixed 45 % fibre volume fraction and 3.3μm fibre radius, is developed for predicting microscopic crack patterns of the FRP laminae. The results show that the two developed DNN models are able to predict both the macroscopic transverse mechanical properties and the microscopic cracks of the RVE accurately.

AB - Fibre-reinforced polymer (FRP) composites have been widely used in different engineering sectors due to their excellent physical and mechanical properties. Therefore, fast, convenient and accurate prediction tools for both macroscopic mechanical properties and failure of the composites are highly demanded by industry and interested by academia. In this study, two back-propagation deep neural network (DNN) models are developed. The first model is a regression model for predicting macroscopic transverse mechanical properties of FRP laminae, which is based on a data set generated by Discrete Element Method (DEM) simulations of 2000 Representative Volume Element (RVE) with 200 different sets of fibre volume fractions and fibre radii. The second model, which is a classification model based on the results of 1600 DEM simulations of RVEs with a fixed 45 % fibre volume fraction and 3.3μm fibre radius, is developed for predicting microscopic crack patterns of the FRP laminae. The results show that the two developed DNN models are able to predict both the macroscopic transverse mechanical properties and the microscopic cracks of the RVE accurately.

U2 - 10.1016/j.compstruct.2022.116248

DO - 10.1016/j.compstruct.2022.116248

M3 - Journal article

VL - 302

JO - Composite Structures

JF - Composite Structures

SN - 0263-8223

M1 - 116248

ER -