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基于人工鱼群算法的碳纳米管纱线传感器对编织复合材料的损伤监测

Damage Monitoring of Braided Composites Using CNT Yarn Sensor Based on Artificial Fish Swarm Algorithm.

作者信息

Wang Hongxia, Jia Yungang, Jia Minrui, Pei Xiaoyuan, Wan Zhenkai

机构信息

Engineering Teaching Practice Training Center, Tiangong University, Tianjin 300387, China.

National Experimental Teaching Demonstration Center for Engineering Training, Tianjin 300387, China.

出版信息

Sensors (Basel). 2023 Aug 10;23(16):7067. doi: 10.3390/s23167067.

DOI:10.3390/s23167067
PMID:37631604
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10458496/
Abstract

This study aims to enable intelligent structural health monitoring of internal damage in aerospace structural components, providing a crucial means of assuring safety and reliability in the aerospace field. To address the limitations and assumptions of traditional monitoring methods, carbon nanotube (CNT) yarn sensors are used as key elements. These sensors are woven with carbon fiber yarns using a three-dimensional six-way braiding process and cured with resin composites. To optimize the sensor configuration, an artificial fish swarm algorithm (AFSA) is introduced, simulating the foraging behavior of fish to determine the best position and number of CNT yarn sensors. Experimental simulations are conducted on 3D braided composites of varying sizes, including penetration hole damage, line damage, and folded wire-mounted damage, to analyze the changes in the resistance data of carbon nanosensors within the damaged material. The results demonstrate that the optimized configuration of CNT yarn sensors based on AFSA is suitable for damage monitoring in 3D woven composites. The experimental positioning errors range from 0.224 to 0.510 mm, with all error values being less than 1 mm, thus achieving minimum sensor coverage for a maximum area. This result not only effectively reduces the cost of the monitoring system, but also improves the accuracy and reliability of the monitoring process.

摘要

本研究旨在实现航空航天结构部件内部损伤的智能结构健康监测，提供确保航空航天领域安全性和可靠性的关键手段。为解决传统监测方法的局限性和假设，碳纳米管（CNT）纱线传感器被用作关键元件。这些传感器采用三维六向编织工艺与碳纤维纱线编织在一起，并用树脂复合材料固化。为优化传感器配置，引入了人工鱼群算法（AFSA），模拟鱼的觅食行为以确定CNT纱线传感器的最佳位置和数量。对不同尺寸的三维编织复合材料进行了实验模拟，包括穿透孔损伤、线状损伤和折叠式线装损伤，以分析受损材料内碳纳米传感器电阻数据的变化。结果表明，基于AFSA的CNT纱线传感器优化配置适用于三维编织复合材料的损伤监测。实验定位误差范围为0.2

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