Migot Asaad, Saaudi Ahmed, Giurgiutiu Victor
Department of Petroleum and Gas Engineering, College of Engineering, University of Thi-Qar, Nasiriyah 64001, Iraq.
Department of Communication and Electronics Engineering, College of Engineering, University of AL-Muthanna, Samawah 66001, Iraq.
Sensors (Basel). 2024 May 14;24(10):3118. doi: 10.3390/s24103118.
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.
分层是复合材料板中最严重且危险的损伤之一。近年来，许多论文展示了结构健康监测（SHM）技术用于研究各种形状和厚度深度的结构分层的能力。然而，关于利用卷积神经网络（CNN）方法自动构建无损检测（NDT）技术数据库以识别分层尺寸和深度的研究却很少。本文提出了一种能够区分原始结构和受损结构，并对三种不同深度的分层进行分类的自动化系统。该系统包括一个提出的CNN模型和兰姆波技术。在这项工作中，制备了一个单向复合材料板，其中插入了三个不同深度的分层样本，用于数值和实验研究。在数值部分，研究了导波传播以及与三个分层样本的相互作用，以观察分层深度如何影响分层区域上的散射波和捕获波。该数值研究通过一种高效的超声导波技术进行了实验验证。该技术涉及压电晶片有源传感器（PWAS）和扫描激光多普勒测振仪（SLDV）。数值和实验研究均表明，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，包括数值波场图像数据集、实验波场图像数据集和实验波数谱图像数据集。这三个数据集分别与提出的CNN模型独立使用，以开发一个能够自动对四类（原始类和三种不同的分层类）进行分类的系统。所有三个数据集的结果都表明，所提出的CNN模型具有高精度预测分层深度的能力。使用GoogLeNet CNN对三个不同数据集的所提出的CNN模型结果进行了验证。两种方法的结果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作为CNN输入数据用于检测分层深度的能力。
