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Abstract

The hybrid composite materials are a new type of composite material. Due to their complex microscopic structures, it is very challenging to predict the equivalent thermal conductivities of hybrid composites. In this paper, an innovative hybrid wavelet-based learning method assisted multiscale analysis is developed to predict the effective thermal conductivities of hybrid composite materials with heterogeneous conductivity by the asymptotic homogenization method, wavelet transform method, and machine learning method. This innovative approach mainly includes two parts: off-line multi-scale modeling and on-line machine learning. Firstly, the material database about thermal transfer performance of hybrid composites is established by the asymptotic homogenization method and off-line multi-scale modeling, and then the off-line material database is preprocessed by the wavelet transform method. Secondly, the artificial neural network and support vector regression method are employed to establish the on-line machine learning model for predicting the equivalent heat conduction properties of hybrid composites. Finally, the effectiveness of the proposed hybrid wavelet-based learning method is verified by numerical experiments on the periodic and random hybrid composites. The numerical results show that the hybrid wavelet-based artificial neural network method owns the optimal capability of parameter prediction and anti-noise. Furthermore, it should be emphasized that the hybrid wavelet-based learning method can not only extract the important features of off-line material database for random hybrid composites with high-dimensional large-scale data features, but also significantly reduce the quantity of input data for ensuring the successful on-line supervised learning and improve the training efficiency and anti-noise performance of the machine learning model. The established hybrid wavelet-based learning method in this paper can not only be used to evaluate the equivalent thermal conductivities of hybrid composite materials, but also further extend to the predicting of the equivalent physical and mechanical properties of composite materials.

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Authors and contacts

Corresponding author:Dong Hao,donghao@mail.nwpu.edu.cn

Funds:Project supported by the National Natural Science Foundation of China (Grant Nos. 12001414, 61971328), the China Postdoctoral Science Foundation (Grant No. 2018M643573), the Young Scientists Fund of the Natural Science Foundation of Shaanxi Province, China (Grant No. 2019JQ-048), and the Open Foundation of Hubei Key Laboratory of Theory and Application of Advanced Materials Mechanics (Wuhan University of Technology), China (Grant No. WUT-TAM202104)

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Cited By

DownLoad: Full-Size Img PowerPoint

样本编号	数据特征					数据标签
样本1	$kc_1$	$kc_2$	$km$	$r_1$	$r_2$	$\widehat k$
$\cdots$	$\cdots$	$\cdots$	$\cdots$	$\cdots$	$\cdots$	$\cdots$
样本n	$kc_1$	$kc_2$	$km$	$r_1$	$r_2$	$\widehat k$

DownLoad: CSV

样本编号	数据特征				数据标签
样本1	$k^1_{x_1 x_2}$	$k^2_{x_1 x_2}$	$\cdots$	$k^m_{x_1 x_2}$	$\widehat k$
$\cdots$	$\cdots$	$\cdots$	$\cdots$	$\cdots$	$\cdots$
样本n	$k^1_{x_1 x_2}$	$k^2_{x_1 x_2}$	$\cdots$	$k^m_{x_1 x_2}$	$\widehat k$

DownLoad: CSV

样本编号	数据特征	数据标签
样本1	$CA_3$	$\widehat k$
$\cdots$	$\cdots$	$\cdots$
样本n	$CA_3$	$\widehat k$

DownLoad: CSV

方法	模型参数	Cv训练误差	Cv验证误差	训练误差	测试误差	训练时间/s	测试时间/s
ANN	1250-1024-512-512-256-1	1.708%	5.760%	1.360%	3.973%	100.345	0.061
ANN-I	1250-1024-512-512-256-1	1.516%	5.948%	1.671%	4.496%	100.517	0.089
ANN-II	1250-1024-512-512-256-1	1.349%	5.404%	1.683%	4.427%	101.160	0.071
ANN-III	1250-1024-512-512-256-1	1.550%	5.862%	1.285%	4.810%	100.270	0.059
SVR	C= 1和$\varepsilon=10^{-2}$	2.658%	9.257%	2.664%	8.156%	0.780	0.171
SVR-I	C= 1和$\varepsilon=10^{-2}$	2.664%	9.268%	2.660%	8.160%	0.710	0.187
SVR-II	C= 1和$\varepsilon=10^{-2}$	2.659%	9.265%	2.673%	8.150%	0.696	0.159
SVR-III	C= 1和$\varepsilon=10^{-2}$	2.677%	9.124%	2.675%	8.099%	0.694	0.166

DownLoad: CSV

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Vol. 70, Issue 3 - 2021-02-05

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