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Updated: Sep 13, 2025

Micro/Nano-scale Strain Distribution Measurement from Sampling Moiré Fringes

Micro/Nano-scale Strain Distribution Measurement from Sampling Moiré Fringes

Published on: May 23, 2017

一种基于UNet++的方法,用于使用全波场的CFRP层材中的分层成像.

Yitian Yan¹, Kang Yang², Yaxun Gou¹

¹State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China.

Sensors (Basel, Switzerland)

|July 30, 2025

概括

在碳纤维增强聚合物 (CFRP) 中检测分层是非常重要的. 本研究介绍了一个UNet++深度学习模型,使用2D频域光谱在CFRP结构中进行准确,无工件的分层成像.

关键词:

碳纤维增强塑料增强碳纤维的塑料深度学习是一种深度学习.脱层是分层的方法.非破坏性评价 - - 非破坏性评价.超声波导向波是指导波的超声波导向波.

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相关实验视频

Last Updated: Sep 13, 2025

Micro/Nano-scale Strain Distribution Measurement from Sampling Moiré Fringes

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Published on: May 23, 2017

Flapping Soft Fin Deformation Modeling using Planar Laser-Induced Fluorescence Imaging

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Published on: April 28, 2022

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

Published on: May 15, 2017

科学领域:

材料科学材料科学材料科学
结构健康监测结构健康监测
非破坏性测试是指非破坏性测试.

背景情况:

及时检测分层对于碳纤维增强聚合物 (CFRP) 的完整性和寿命至关重要.
在CFRP中,完整的波场数据包含了丰富的信息,用于损坏映射,但由于引导波的复杂性,难以解释.
现有的方法与引导波的多式和分散性质作斗争,阻碍了准确的分层成像.

研究的目的:

开发一个端到端的深度学习方法,用于在CFRP结构中准确的分层成像.
为了利用来自全波场数据的二维频域光谱 (FDS) 来增强损坏检测.
在模拟,实验和公共数据集上验证拟议的方法.

主要方法:

实现一个UNet++深度学习架构用于图像细分.
使用从完整波场数据中提取的2D频域光谱 (FDS) 作为输入.
使用自建模拟数据集,扫描激光多普勒振动测试实验数据和公共数据集进行验证.

主要成果:

UNet++模型使用多频FDS准确预测分层位置,形状和大小.
该方法证明了对FDS频率偏移和噪声干扰的稳定性.
在模拟数据上训练的模型显示直接适用于现实世界的实验数据,产生无文物图像.

结论:

拟议的基于UNet++的方法为CFRP中的分层成像提供了有效的端到端解决方案.
该方法成功地解决了多式联络和分散导向波所带来的挑战.
该模型能够从模拟到实验数据进行概括,这突显了其用于结构性健康监测的实际潜力.