Tailoring composite materials for nonlinear viscoelastic properties using artificial neural networks
Tailoring composite materials for nonlinear viscoelastic properties using artificial neural networks
复制标题
使用人工神经网络定制复合材料的非线性粘弹性特性
DOI：
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复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
N. Gupta
中科院分区：
文献类型：
--
作者：
Xianbo Xu;Mariam Elgamal;M. Doddamani;N. Gupta
聚合物基复合材料在较宽的温度和加载频率范围内表现出非线性粘弹性行为，这需要精心设计的实验表征活动。现在有方法可以加速表征过程并从储能模量 (E') 恢复弹性模量。然而，这些方法仅限于线性粘弹性区域，需要扩展到非线性粘弹性问题以实现材料设计。目前的工作旨在构建一个基于通用机器学习的架构，以利用 E' 结果加速非线性粘弹性材料的表征和材料设计过程。为了扩展到线性粘弹性区域之外，首先建立粘弹性的一般关系，因此考虑非线性粘弹性的E'主关系可以转换为时域松弛函数。该转换首先通过使用克里金模型和遗传算法优化人工神经网络 (ANN) 公式来构建主关系。然后将主关系转换为松弛函数，可用于预测给定应变历史的应力响应并进一步提取弹性模量。该变换在高密度聚乙烯基质复合泡沫上进行了测试，并通过将预测的材料性能与拉伸测试获得的性能进行比较来确定其准确性。良好的一致性表明该变换可以预测复合材料任何成分在宽范围温度和应变率下的弹性模量，并且可用于材料设计问题。
Polymer matrix composites exhibit nonlinear viscoelastic behavior over a wide range of temperatures and loading frequencies, which requires an elaborate experimental characterization campaign. Methods are now available to accelerate the characterization process and recover elastic modulus from storage modulus (E′). However, these methods are limited to the linear viscoelastic region and need to be expanded to nonlinear viscoelastic problems to enable materials design. The present work aims to build a general machine learning based architecture to accelerate the characterization and materials design process for nonlinear viscoelastic materials using the E′ results. To expand outside the linear viscoelastic region, general relations of viscoelasticity are first developed so the master relation of E′ considering nonlinear viscoelasticity can be transformed to time domain relaxation function. The transform starts with building the master relation by optimizing the artificial neural network (ANN) formulation using Kriging model and genetic algorithm. Then the master relation is transformed to a relaxation function, which can be used to predict the stress response with a given strain history and to further extract the elastic modulus. The transform is tested on high density polyethylene matrix syntactic foams and the accuracy is found by comparing the predicted materials properties with those obtained from tensile tests. The good agreements indicate the transform can predict the elastic modulus under a wide range of temperatures and strain rates for any composition of the composite and can be used for material design problems.
影响因子：
8.4
作者：
He, Sammy;Carolan, Declan;Taylor, Ambrose C.
通讯作者：
Taylor, Ambrose C.
双线性结构中的静态和动态非互易性
DOI：
10.1121/2.0000861
发表时间：
2018
期刊：
Proceedings of Meetings on Acoustics
影响因子：
--
作者：
Wallen, Samuel P.;Haberman, Michael R.;Lu, Zhaocheng;Norris, Andrew;Wiest, Tyler;Seepersad, Carolyn C.
通讯作者：
Seepersad, Carolyn C.