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

doi:10.1016/j.compositesa.2024.108233

CORC > 力学研究所 > 中国科学院力学研究所 > 流固耦合系统力学重点实验室(2012-)

	Machine learning-based determination of Mode II translaminar fracture toughness of composite laminates from simple V-notched shear tests
	Qiu, Cheng 2,3; Gui, Yizhuo 3; Ma, Jiwen 2; Song, Hongwei 3; Yang, Jinglei 1,2
刊名	COMPOSITES PART A-APPLIED SCIENCE AND MANUFACTURING
	2024-09-01
卷号	184 页码:14
关键词	Composite laminates Fracture toughness Fracture mechanics Machine learning
ISSN号	1359-835X
DOI	10.1016/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.
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关键词	CRACK RESISTANCE CURVE ; NANOINDENTATION ; MECHANICS ; SPECIMEN ; STRESS
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
内容类型	期刊论文
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.(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.