A stretchable and large-scale guided wave sensor network for aircraft smart skin of structural health monitoring

互连 印刷电路板 过程(计算) 结构健康监测 信号(编程语言) 无线传感器网络 传感器 工程类 电子工程 计算机科学 电气工程 电信 计算机网络 操作系统 程序设计语言
作者
Yu Wang,Lei Qiu,Yijie Luo,Rui Ding
出处
期刊:Structural Health Monitoring-an International Journal [SAGE Publishing]
卷期号:20 (3): 861-876 被引量:100
标识
DOI:10.1177/1475921719850641
摘要

Aircraft smart skin technology requires the integration of large-scale, lightweight, and integrative sensor networks with aircraft structural skin, but it is difficult to directly manufacture such sensor networks in a large-scale, low-cost, reliable, and simple way. To study this issue, a design and manufacturing method of a stretchable and large-scale guided wave sensor network is proposed in this article. The key principle of the method is to design and manufacture the guided wave sensor network in a limited scale and expand it in a large-scale for aircraft smart skin of structural health monitoring. To ensure the excellent stretchability of the network, the design method of a serpentine-based fractal island-interconnect structure combined with the flexible printed circuit manufacturing process is proposed. The whole manufacturing process of the guided wave sensor network is realized by the mature flexible printed circuit process and the piezoelectric transducers (referred to as PZTs) are integrated with the network by reflow soldering which is compatible with the flexible printed circuit process, which greatly reduce manufacturing costs, simplify manufacturing process, and improve the yield and stability of the network. The guided wave sensor network can be uniaxially stretched to 500% of its original size and work well when fully expanded to cover 2500% of its original area. The test results of guided wave response show that the PZTs on the network can excite and receive guided wave signal normally. In addition, the network is placed on the surface of a carbon fiber composite laminate with stiffeners and the validation results show that the network can be applied to both active and passive guided wave–based structural health monitoring of composite structures, including damage imaging and impact imaging.
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