Graded honeycombs with high impact resistance through machine learning-based optimization

doi:10.1016/j.tws.2023.110794

CORC > 力学研究所 > 中国科学院力学研究所 > 非线性力学国家重点实验室

	Graded honeycombs with high impact resistance through machine learning-based optimization
	Gao Y(高洋); Chen XJ(陈贤佳); Wei YJ(魏宇杰)
刊名	THIN-WALLED STRUCTURES
	2023-07
卷号	188 页码:110794
关键词	Graded honeycomb Impact resistance Machine learning Energy absorption Equal-load-partition
ISSN号	0263-8231
DOI	10.1016/j.tws.2023.110794
英文摘要	Gradient structures with enhanced performance are ubiquitously observed in nature and in engineering materials. In this paper, we studied the impact resistance of two types of broadly used honeycomb structures (HCSs), a hexagonal HCS and an auxetic HCS. We developed a neural network (NN) which could effectively help to find an optimal gradient design for energy absorption of HCSs in contrast with their uniform counterpart. The optimal density gradient for both hexagonal HCS and auxetic HCS was identified, which are 66% and 40% higher in energy absorption than their respective uniform control. Followed finite-element analysis revealed that density gradient of HCSs enables loading transfer among a greater deformation zone, consequentially more cells involving in energy absorption. The initially graded sample promotes a de-gradient process and leads to more homogeneous density; conversely, a uniform sample develops localized deformation when subject to impact loading. Such an equal-load-partition (ELP) strategy in graded HCSs is responsible for their supreme energy absorption. The developed machine learning (ML) method for impact resistance optimization and the revealed deformation mechanisms in graded HCSs would be meaningful for the design of new advanced graded materials.
分类号	一类
WOS研究方向	Engineering, Civil ; Engineering, Mechanical ; Mechanics
语种	英语
WOS记录号	WOS:001013005900001
资助机构	NSFC Basic Science Center, China [11988102] ; NSFC, China [12202447] ; China Postdoctoral Science Foundation, China [2021M703289]
其他责任者	Wei, YJ (corresponding author), Chinese Acad Sci, Inst Mech, LNM, Beijing 100190, Peoples R China.
内容类型	期刊论文
源URL	[http://dspace.imech.ac.cn/handle/311007/92405]
专题	力学研究所_非线性力学国家重点实验室
作者单位	1.{Wei Yujie} Univ Chinese Acad Sci Sch Engn Sci Beijing 100049 Peoples R China 2.{Gao Yang, Chen Xianjia, Wei Yujie} Chinese Acad Sci Inst Mech LNM Beijing 100190 Peoples R China
推荐引用方式 GB/T 7714	Gao Y,Chen XJ,Wei YJ. Graded honeycombs with high impact resistance through machine learning-based optimization[J]. THIN-WALLED STRUCTURES,2023,188:110794.
APA	高洋,陈贤佳,&魏宇杰.(2023).Graded honeycombs with high impact resistance through machine learning-based optimization.THIN-WALLED STRUCTURES,188,110794.
MLA	高洋,et al."Graded honeycombs with high impact resistance through machine learning-based optimization".THIN-WALLED STRUCTURES 188(2023):110794.