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Abstract：
Delamination represents one of the most significant and dangerous damages in composite plates. Recently, many papers have presented the capability of structural health monitoring (SHM) techniques for the investigation of structural delamination with various shapes and thickness depths. However, few studies have been conducted regarding the utilization of convolutional neural network (CNN) methods for automating the non-destructive testing (NDT) techniques database to identify the delamination size and depth. In this paper, an automated system qualified for distinguishing between pristine and damaged structures and classifying three classes of delamination with various depths is presented. This system includes a proposed CNN model and the Lamb wave technique. In this work, a unidirectional composite plate with three samples of delamination inserted at different depths was prepared for numerical and experimental investigations. In the numerical part, the guided wave propagation and interaction with three samples of delamination were studied to observe how the delamination depth can affect the scattered and trapped waves over the delamination region. This numerical study was validated experimentally using an efficient ultrasonic guided waves technique. This technique involved piezoelectric wafer active sensors (PWASs) and a scanning laser Doppler vibrometer (SLDV). Both numerical and experimental studies demonstrate that the delamination depth has a direct effect on the trapped waves\' energy and distribution. Three different datasets were collected from the numerical and experimental studies, involving the numerical wavefield image dataset, experimental wavefield image dataset, and experimental wavenumber spectrum image dataset. These three datasets were used independently with the proposed CNN model to develop a system that can automatically classify four classes (pristine class and three different delamination classes). The results of all three datasets show the capability of the proposed CNN model for predicting the delamination depth with high accuracy. The proposed CNN model results of the three different datasets were validated using the GoogLeNet CNN. The results of both methods show an excellent agreement. The results proved the capability of the wavefield image and wavenumber spectrum datasets to be used as input data to the CNN for the detection of delamination depth.
摘要：
分层是复合板中最重要和最危险的损坏之一。最近,许多论文提出了结构健康监测（SHM）技术的能力，用于研究具有各种形状和厚度深度的结构分层。然而,关于利用卷积神经网络（CNN）方法来自动化无损检测（NDT）技术数据库以识别分层大小和深度的研究很少。在本文中,提出了一种自动化系统，该系统能够区分原始结构和受损结构，并对具有不同深度的三类分层进行分类。该系统包括提出的CNN模型和兰姆波技术。在这项工作中,准备了一个单向复合板，该复合板在不同深度插入了三个分层样品，用于数值和实验研究。在数值部分，研究了导波的传播和与三个分层样品的相互作用，以观察分层深度如何影响分层区域的散射和捕获波。使用有效的超声导波技术对该数值研究进行了实验验证。该技术涉及压电晶片有源传感器（PWAS）和扫描激光多普勒振动计（SLDV）。数值和实验研究都表明，分层深度对捕获波的能量和分布有直接影响。从数值和实验研究中收集了三个不同的数据集，涉及数值波场图像数据集，实验波场图像数据集，和实验波数光谱图像数据集。这三个数据集与提出的CNN模型一起独立使用，以开发一个系统，该系统可以自动分类四个类别（原始类别和三个不同的分层类别）。所有三个数据集的结果显示了所提出的CNN模型以高精度预测分层深度的能力。使用GoogLeNetCNN验证了三个不同数据集的CNN模型结果。两种方法的结果都显示出极好的一致性。结果证明了波场图像和波数谱数据集用作CNN的输入数据以检测分层深度的能力。
