Lamb wave-based quantitative identification of delamination in CF/EP composite structures using artificial neural algorithm
Lamb wave-based quantitative identification of delamination in CF/EP composite structures using artificial neural algorithm
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DOI：
10.1016/j.compstruct.2004.05.011
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发表时间：
2004-10-01
影响因子：
6.3
通讯作者：
Ye, L
中科院分区：
文献类型：
--
作者：
Su, ZQ;Ye, L
复合材料结构中的脱层对降低结构的强度和刚度起着重要作用，从而降低了系统的完整性和可靠性。建立了一种基于兰姆波的碳纤维/环氧树脂复合材料分层定量识别技术。采用动态有限元分析方法研究了Lamb波在一系列含分层复合材料层合板中的传播。利用小波变换和人工神经网络算法，开发了智能信号处理与模式识别（ISPPR）软件包，提取了模拟Lamb波信号的时频域频谱特征，并将其数字化为数字损伤指纹（DDF），构建了损伤参数数据库（DPD）。然后离线使用DPD来训练在误差反向传播（BP）算法的监督下的多层前馈人工神经网络（ANN）。在一个基于压电作动器/传感器网络的主动在线结构健康监测（AO-SHM）系统的辅助下，通过对CF/EP（T650/F584）准各向同性复合材料层合板实际分层损伤的在线识别，验证了该方法的有效性。(C)2004爱思唯尔有限公司保留所有权利。
Delamination in composite structures plays a major role in lowering structural strength and stiffness, consequently downgrading system integrity and reliability. A Lamb wave-based quantitative identification technique for delamination in CF/EP composite structures was established. Propagation of Lamb waves in a series of composite laminates, individually bearing a delamination, was evaluated using dynamic FEM analyses. Taking advantage of wavelet transform and artificial neural algorithms, an Intelligent Signal Processing and Pattern Recognition (ISPPR) package was developed, by which the spectrographic characteristics of simulated Lamb wave signals in the time-frequency domain were extracted and digitised as Digital Damage Fingerprints (DDF), to construct a Damage Parameters Database (DPD). The DPD was then used offline to train a multi-layer feedforward artificial neural network (ANN) under supervision of an error-backpropagation (BP) algorithm. Assisted by an active online structural health monitoring (AO-SHM) system with an active piezoelectric actuator/sensor network, the proposed methodology was validated online by identifying actual delaminations in CF/EP (T650/F584) quasi-isotropic composite laminates. (C) 2004 Elsevier Ltd. All rights reserved.