Deep Learning Approach for Predicting Stress Fields in Composites With Diverse Fiber Volume Fractions

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成果类型：

期刊论文

作者：

Qiuze Yao;Xiang Peng;Weiqiang Jia;Xin Liu;Jiquan Li;...

通讯作者：

Xiang Peng<&wdkj&>Weiqiang Jia

作者机构：

[Weiqiang Jia] Zhejiang Lab, Hangzhou, China

[Qiuze Yao; Xiang Peng; Jiquan Li; Shaofei Jiang] College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China

[Xin Liu] Hunan Province Key Laboratory of Safety Design and Reliability Technology for Engineering Vehicle, Changsha University of Science and Technology, Changsha, China

通讯机构：

[Xiang Peng] C

[Weiqiang Jia] Z

Zhejiang Lab, Hangzhou, China<&wdkj&>College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China

语种：

英文

关键词：

composite;deep learning;finite element method;local stress;representative volume element

期刊：

Polymer Composites

ISSN：

0272-8397

年：

2025

DOI：

10.1002/pc.30048

基金类别：

This work was supported by the National Natural Science Foundation of China (grant numbers 51875525, 52275235), the Zhejiang Provincial Natural Science Foundation of China (grant numbers LY21E050008, LHZ22F020001), and Fundamental Research Funds for the Provincial Universities of Zhejiang (RF-B2024004).

机构署名：

本校为其他机构

院系归属：

汽车与机械工程学院

摘要：

Predicting the full-field stress distribution has important significance in analyzing mechanical characteristics of composite materials. Numerical calculations of stress field distribution using finite element analysis (FEA) can call for significant computational effort for microscale geometries. To address this challenge, this paper demonstrates a deep learning (DL) framework for predicting local stress distributions in fiber-reinforced composites with diverse fiber volume fractions. An adaptive generation algorithm of representative volume element (RVE) microstructures is developed for const...

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Deep Learning Approach for Predicting Stress Fields in Composites With Diverse Fiber Volume Fractions

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