3D打印梯度随机蜂窝夹芯结构耐撞性研究与多目标优化设计

doi:10.19562/j.chinasae.qcgc.2024.11.017

摘要/Abstract

摘要：

蜂窝材料具有轻质、高比吸能等优良的能量吸收性能，被广泛应用于各类吸能防护结构，本文基于Voronoi图形和3D打印技术，设计并制备了新型梯度随机蜂窝夹芯结构，建立其三点弯曲加载的有限元模型，并进行了试验验证，进而基于数值模型开展了其耐撞性研究以及多目标优化设计，研究结果表明，均匀随机蜂窝夹芯结构中，较低随机度的蜂窝夹芯结构具有更好的能量吸收特性，壁厚增大增加比吸能的同时因其细观变形模式由塑性铰主导而具有较大的承载波动系数，相对密度一致时，胞元尺寸不同的随机蜂窝夹芯结构的比吸能相差不大，胞元尺寸的减小使变形过程更加平稳而降低了承载波动系数；对于胞元尺寸梯度、壁厚梯度随机蜂窝夹芯结构，正梯度的引入增大了加载端强度，使得吸能指标提高；基于非支配排序速传算法（non-dominated sorting genetic algorthm-II，NSGA-II），对正梯度随机蜂窝夹芯结构进行多目标优化，得到的优化结果相比于未进行优化设计的均匀随机蜂窝夹芯结构，比吸能提高了33.9%。

关键词: 随机蜂窝, 夹芯结构, 3D打印, 能量吸收特性, 多目标优化

Abstract:

With excellent energy absorption properties of lightweight and high specific energy absorption， honeycomb material is widely used in various energy absorption protection structures. In this article， based on Voronoi diagrams and 3D printing technology， a novel gradient random honeycomb sandwich structure is designed and prepared. A finite element model of its three-point bending load is established and experimentally validated. Subsequently， based on the numerical model， crashworthiness research and multi-objective optimization design are conducted. The results show that for uniform random honeycomb sandwich structures， those with a lower degree of randomness have better energy absorption characteristics. Increasing the wall thickness increases the specific energy absorption but also leads to a larger load fluctuation coefficient due to the meso-structural deformation mode dominated by plastic hinges. When the relative density is consistent， the specific energy absorption of the random honeycomb sandwich structure with different cell size is not much different， and the decrease of cell size makes the deformation process more stable and reduces the bearing fluctuation coefficient. For cell size and cell wall thickness gradient random honeycomb sandwich structures， the introduction of a positive gradient of leads to a deformation mode dominated by both the support end and the loading end， which improves the energy absorption indicators. Based on the Non-dominated Sorting Genetic Algorthm-II （NSGA-II）， a multi-objective optimization of the positive gradient random honeycomb sandwich structures is performed. The obtained meso-structural parameters with optimal energy absorption characteristics show a 33.9% increase in specific energy absorption compared to the uniformly random honeycomb sandwich structure without optimization design.

Key words: random honeycomb, sandwich structure, 3D printing, energy absorption characteristics, multi-objective optimization

罗耿,柴成鹏,祝召飞,陈轶嵩. 3D打印梯度随机蜂窝夹芯结构耐撞性研究与多目标优化设计[J]. 汽车工程, 2024, 46(11): 2110-2121.

Geng Luo,Chengpeng Chai,Zhaofei Zhu,Yisong Chen. Research on the Crashworthiness of 3D Printing Gradient Random Honeycomb Sandwich Structure and Multi-objective Optimization Design[J]. Automotive Engineering, 2024, 46(11): 2110-2121.

图/表 31

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图5

表4

基体材料PLA基本力学性能参数"

密度 $ρ 0$	弹性模量 E	泊松比 $μ$	屈服应力 $σ y$
2 100 kg/m³	1.901 GPa	0.3	41 MPa

表4

图6

图7

图8

图9

图10

图11

图12

图13

图14

图15

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图17

图18

图19

图20

图21

图22

表5

图23

图24

表6

表7

参考文献 21

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编号	n	t/mm	γ	IPCF/N	SEA/（J·g^-1）
1	100	0.4	0.2	134.573	0.171
2	100	0.6	0.4	335.685	0.289
3	100	0.8	0.6	596.148	0.473
4	100	1.2	0.8	1 369.74	0.853
5	140	0.4	0.4	227.096	0.204
6	140	0.6	0.2	396.278	0.388
7	140	0.8	0.8	975.801	0.731
8	140	1.2	0.6	1 561.64	1.036
9	180	0.4	0.6	360.213	0.326
10	180	0.6	0.8	731.563	0.602
11	180	0.8	0.2	1 046.55	0.715
12	180	1.2	0.4	2 966.34	1.157
13	220	0.4	0.8	358.486	0.383
14	220	0.6	0.6	846.727	0.623
15	220	0.8	0.4	1 365.03	0.871
16	220	1.2	0.8	3 497.39	1.472

类型	SEA/（J·g^-1）	IPCF/N
仿真结果	1.06	1 443
优化结果	1.03	1 541
误差	-2.8%	6.8%

类型	SEA/（J·g^-1）	IPCF/N	CFE
K02N210T08-γ （0.8）	1.06	1 443	1.56
K02N210T08	0.70	1 112	1.55
指标增量	33.9%	22.9%	0.64%

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