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Machine learning-based determination of Mode II translaminar fracture toughness of composite laminates from simple V-notched shear tests

文献类型：期刊论文

作者Qiu, Cheng2,3; Gui, Yizhuo3; Ma, Jiwen2; Song, Hongwei3; Yang, Jinglei1,2; Song HW(宋宏伟)
刊名COMPOSITES PART A-APPLIED SCIENCE AND MANUFACTURING
出版日期2024-09-01
卷号184页码:14
关键词Composite laminates Fracture toughness Fracture mechanics Machine learning
ISSN号1359-835X
DOI10.1016/j.compositesa.2024.108233
通讯作者Qiu, Cheng(qiucheng@imech.ac.cn) ; Yang, Jinglei(maeyang@ust.hk)
英文摘要This paper presents a novel method for measuring the translaminar crack resistance curve of composite laminates under Mode II shear loading. A machine learning (ML)-based approach is utilized to extract the inapparent information of the crack resistance curve from the translaminar shear strength measurements obtained from simple V-notched shear tests. The entire campaign is built on the framework of the Finite Fracture Mechanics (FFM) combined with Finite Element Method (FEM). Special emphasis is made on the nonlinear mechanical behavior of composites under shear stress since the original FFM models are designed for quasi-brittle materials. With the well-trained recurrent neural network model, the Mode II R-curve of composite laminate can be obtained with un-notched and V-notched shear strength values as inputs. Experiments were conducted on carbon fiber-reinforced composites to validate the accuracy of the R-curve obtained by the proposed approach and that by the traditional compact shear test. The successful implementation of the method suggests a more convenient and low-cost way of obtaining this important damage-related parameter for composites.
WOS关键词CRACK RESISTANCE CURVE ; NANOINDENTATION ; MECHANICS ; SPECIMEN ; STRESS
资助项目Project of Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone[HZQB-KCZYB-2020083] ; Department of Science and Technology of Guangdong Province[2022A0505030023] ; Chinese Academy of Sciences[025GJHZ2022103FN]
WOS研究方向Engineering ; Materials Science
语种英语
WOS记录号WOS:001243343700001
资助机构Project of Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone ; Department of Science and Technology of Guangdong Province ; Chinese Academy of Sciences
源URL[http://dspace.imech.ac.cn/handle/311007/95678]  
专题力学研究所_流固耦合系统力学重点实验室(2012-)
通讯作者Qiu, Cheng; Yang, Jinglei
作者单位1.HKUST Shenzhen Hong Kong Collaborat Innovat Res In, Shenzhen, Peoples R China
2.Hong Kong Univ Sci & Technol, Dept Mech & Aerosp Engn, Hong Kong, Peoples R China
3.Chinese Acad Sci, Inst Mech, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Qiu, Cheng,Gui, Yizhuo,Ma, Jiwen,et al. Machine learning-based determination of Mode II translaminar fracture toughness of composite laminates from simple V-notched shear tests[J]. COMPOSITES PART A-APPLIED SCIENCE AND MANUFACTURING,2024,184:14.
APA Qiu, Cheng,Gui, Yizhuo,Ma, Jiwen,Song, Hongwei,Yang, Jinglei,&宋宏伟.(2024).Machine learning-based determination of Mode II translaminar fracture toughness of composite laminates from simple V-notched shear tests.COMPOSITES PART A-APPLIED SCIENCE AND MANUFACTURING,184,14.
MLA Qiu, Cheng,et al."Machine learning-based determination of Mode II translaminar fracture toughness of composite laminates from simple V-notched shear tests".COMPOSITES PART A-APPLIED SCIENCE AND MANUFACTURING 184(2024):14.

入库方式： OAI收割

来源：力学研究所

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