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在复合板中使用基于卷积神经网络的羔羊波技术检测分层深度.

Asaad Migot¹, Ahmed Saaudi², Victor Giurgiutiu³

¹Department of Petroleum and Gas Engineering, College of Engineering, University of Thi-Qar, Nasiriyah 64001, Iraq.

Sensors (Basel, Switzerland)

|May 25, 2024

概括

本研究介绍了一种使用卷积神经网络 (CNN) 和Lamb波来检测复合板中的分层深度的自动化系统. 该系统准确地识别损伤,增强结构健康监测能力.

关键词:

在美国,CNN是CNN.在谷歌的网络上,谷歌LeNet.羊羔的波浪在浪叫着.在 SLDVV 里面.复合材料复合材料是一种复合材料.脱层是分层的方法.波场图像波场图像波场图像波数频谱中的波数.

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科学领域:

材料科学材料科学材料科学
机械工程机械工程
计算机科学计算机科学

背景情况:

分层是复合板的关键故障模式,存在重大安全风险.
结构健康监测 (SHM) 技术对于检测分层至关重要,但自动化仍然是一个挑战.
卷积神经网络 (CNN) 在自动化非破坏性测试 (NDT) 数据分析方面表现有前途.

研究的目的:

开发一种自动化系统,以区分原始结构和受损的复合结构.
使用CNN和Lamb波浪技术,根据深度对分层进行分类.
为了研究分层深度对引导波传播的影响.

主要方法:

一个拟议的CNN模型与Lamb波技术集成.
使用压电晶片活性传感器 (PWAS) 和扫描激光多普勒振动计 (SLDV) 的数值模拟和实验验证.
利用了三个数据集:数值波场图像,实验波场图像和实验波数频谱图像.

主要成果:

数字和实验研究都证实了分层深度直接影响被困的波浪能量和分布.
拟议的CNN模型在所有三个数据集中准确地分类了四个类别 (原始和三个分层深度).
对谷歌LeNet CNN的验证显示出了很好的一致性,证实了该模型的预测能力.

结论:

开发的自动化系统有效地检测和分类复合板的分层深度.
波场和波数频谱图像是分层检测中CNN的合适输入数据.
这项研究推进了复合结构健康监测的自动化NDT.