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Identifying Defects in Aerospace Composite Sandwich Panels Using High-Definition Distributed Optical Fibre Sensors

机译:使用高清分布式光纤传感器识别航空航天复合夹层面板中的缺陷

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摘要

Automated methods for detecting defects within composite materials are highly desirable in the drive to increase throughput, optimise repair program effectiveness and reduce component replacement. Tap-testing has traditionally been used for detecting defects but does not provide quantitative measurements, requiring secondary techniques such as ultrasound to certify components. This paper reports on an evaluation of the use of a distributed temperature measurement system—high-definition fibre optic sensing (HD-FOS)—to identify and characterise crushed core and disbond defects in carbon fibre reinforced polymer (CFRP)-skin, aluminium-core, sandwich panels. The objective is to identify these defects in a sandwich panel by measuring the heat transfer through the panel thickness. A heater mat is used to rapidly increase the temperature of the panel with the HD-FOS sensor positioned on the top surface, measuring temperature. HD-FOS measurements are made using the Luna optical distributed sensor interrogator (ODISI) 9100 system comprising a sensor fabricated using standard single mode fibre (SMF)-20 of external diameter 250 μm, including the cladding. Results show that areas in which defects are present modulate thermal conductivity, resulting in a lower surface temperature. The resultant data are analysed to identify the length, width and type of defect. The non-invasive technique is amenable to application in challenging operational settings, offering high-resolution visualisation and defect classification.
机译:在驱动器中,非常需要用于检测复合材料内缺陷的自动化方法,以提高产量,优化修复程序效果并减少组件更换。 Tap-Testing传统上用于检测缺陷,但不提供定量测量,需要次要技术,例如超声波认证组件。本文报告了使用分布式温度测量系统 - 高清光纤感测(HD-FOS)的使用评估 - 识别和表征碳纤维增强聚合物(CFRP)-Skin,铝 - 铝 - 铝 - 核心,三明治面板。目的是通过测量通过面板厚度的传热来识别夹层板中的这些缺陷。加热器垫用于快速增加面板的温度,HD-FOS传感器定位在顶面上,测量温度。使用Luna光学分布式传感器询问器(ODISI)9100系统进行了HD-FOS测量,包括使用外径250μm的标准单模光纤(SMF)-20制造的传感器,包括包层。结果表明,存在缺陷的区域调节导热率,导致表面温度较低。分析所得到的数据以识别缺陷的长度,宽度和类型。非侵入性技术可用于在具有挑战性的操作环境中应用,提供高分辨率可视化和缺陷分类。

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