纺织学报 ›› 2021, Vol. 42 ›› Issue (09): 76-82.doi: 10.13475/j.fzxb.20210101907

• 纺织工程 • 上一篇    下一篇

三维编织复合材料中碳纳米管纱线嵌入位置和数量的优化配置

万振凯1(), 贾敏瑞2, 包玮琛2   

  1. 1.天津工业大学 工程教学中心, 天津 300387
    2.天津工业大学 纺织科学与工程学院, 天津 300387
  • 收稿日期:2021-01-11 修回日期:2021-03-17 出版日期:2021-09-15 发布日期:2021-09-27
  • 作者简介:万振凯(1964—),男,教授,博士。主要研究方向为三维编织复合材料检测方法。E-mail: wanzhenkai@tiangong.edu.cn

Optimal configuration of embedded position and number of carbon nanotube yarns in 3-D braided composites

WAN Zhenkai1(), JIA Minrui2, BAO Weichen2   

  1. 1. Engineering Teaching Center, Tiangong University, Tianjin 300387, China
    2. School of Textile Science and Engineering, Tiangong University, Tianjin 300387, China
  • Received:2021-01-11 Revised:2021-03-17 Published:2021-09-15 Online:2021-09-27

摘要:

为解决三维编织复合材料嵌入碳纳米管(CNT)纱线传感器优化配置目标多、目标函数不连续问题,实现航天结构制件内部损伤的全面监测,采用非支配邻域免疫算法对多目标优化问题进行了研究。以四步法三维六向编织工艺为依据,分析了CNT纱线传感器的最佳嵌入位置和数量;通过非支配邻近免疫算法实现了CNT纱线传感器优化配置问题的求解,推导出不同尺寸的三维编织复合材料制件嵌入传感器的最优数量和位置。对损伤制件的应力实验及数据分析证明,CNT纱线传感器优化配置结果适用于三维编织复合材料的损伤监测,损伤定位误差小于0.6 mm。该研究为复合材料损伤源定位模型的建立提供参考。

关键词: 碳纳米管纱线, 非支配邻域免疫算法, 传感器优化配置, 损伤定位源, 三维编织复合材料

Abstract:

In order to facilitate comprehensive monitoring of the internal damage of aerospace structures, the optimal configuration of 3-D intelligent braided composites embedded with carbon nanotube(CNT)yarns as sensors with multiple targets and discontinuous objective function was studied by using the non-dominant neighborhood immune multi-target optimization algorithm. In this paper, the optimal insertion location and quantity of CNT yarn sensors were analyzed based on the three-dimension-four-step-six-direction braiding process. The optimal allocation problem of CNT yarn sensor was solved by using the non-dominant neighborhood immune multi-objective optimization algorithm, and the optimal number and location of different specimen embedded sensors in the 3-D intelligent braided composite were derived. The stress experiment and data analysis on the damaged specimens prove that the principle of optimal configuration of CNT yarn sensor can be applied for damage monitoring of 3-D braided composite materials with the positioning error less than 0.6 mm. This study lays a foundation for the establishment of intelligent composite damage source identification model.

Key words: carbon nanotube yarn, nondominated neighborhood imnaune algorithm, optimal allocation of sensors, damage location source, 3-D braided composite

中图分类号: 

  • TS101.2

图1

三维编织机示意图"

图2

四步法携纱器运动步骤"

图3

纬纱嵌入预制件示意图"

图4

嵌入CNT纱线的三维编织复合材料预制件结构图"

表1

不同尺寸试件嵌入CNT纱线传感器的优化配置结果"

试件尺寸 传感网络总覆盖率/% 轴向传感器数量 纬向传感器数量 轴向传感器位置 纬向传感器位置
6 cm×1 cm 69.5 2 4 {1,7} {1,5,8,11}
6 cm×1 cm 62.9 2 5 {1,7} {1,3,7,10,12}
6 cm×1 cm 70.1 3 4 {1,4,7} {1,3,7,10,12}
6 cm×2 cm 77.8 4 4 {2,8,14,19} {1,5,8,11}
6 cm×2 cm 79.5 5 4 {1,5,10,15,19} {1,5,8,11}
8 cm×1 cm 74.6 2 6 {1,7} {1,4,7,10,13,16}
8 cm×1 cm 70.4 2 5 {1,7} {1,5,8,13,16}
8 cm×1 cm 71.2 3 5 {1,4,7} {1,5,8,13,16}
10 cm×10 cm 90.4 20 7 {1,3,5,7,9,11,13,15,17,19} {1,4,7,10,13,16,19}
20 cm×20 cm 91.8 40 13 {1,6,11,16,21,26,31,36,41,46,
51,56,61,66,71,76,81,86,91,96,
100,105,110,115,120,125,130,
135,140,145,150,155,160,165,
170,175,180,185,190,195,200}
{1,4,7,11,14,17,21,
24,27,31,34,37,40}

图5

不同尺寸复合材料试件嵌入CNT纱线数量"

图6

嵌入CNT纱线的三维编织复合材料损伤试件"

表2

检测与实际损伤坐标位置对照表"

损伤编号 损伤计算坐标 损伤实际坐标 坐标最大偏差/mm
A (-3.0,2.4) (-2.3,2.5) 0.5
B (-2.3,18.4) (-2.1,18.2) 0.08
C (-3.2,1.5) (-3.4,1.1) 0.2
D (-4.6,13.1) (-4.9,12.8) 0.58
E (-2.6,-3.8) (-2.5,-3.5) 0.1
F (-4.1,-7.8) (-4.3,-7.5) 0.13
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