Wrinkle defects were found widely exist in the field of industrial products,
i.e. wind turbine blades and filament-wound composite pressure vessels. The
magnitude of wrinkle wavelength varies from several millimeters to over one
hundred millimeters. Locating the wrinkle defects and measuring their responses
are very important to the assessment of the structures that containing wrinkle
defects. A meso-mechanical modeling is presented based on the homogenization
method to obtain the effective stiffness of a graded wrinkle. The finite
element simulation predicts the trans-scale response of out-of-plane
displacement of wrinkled laminates, where the maximum displacement ranges from
nanoscale to millimeter scale. Such trans-scale effect requires different
measurement approaches to observe the displacement responses. Here we employed
Shearography (Speckle Pattern Shearing Interferometry) and fringe projection
profilometry (FPP) method respectively according to the different magnitude of
displacement. In FPP method, a displacement extraction algorithm was presented
to obtain the out-of-plane displacement. The measurement sensitivity and
accuracy of Shearography and FPP are compared, which provides a quantitative
reference for industrial non-destructive test.

皱纹缺陷广泛存在于工业产品领域中，例如风力涡轮叶片和缠绕复合材料压力容器。基于均质化方法提出了一种介观力学建模，以获取分级皱纹的有效刚度。有限元模拟预测了皱纹层合板的跨尺度反应，其中最大位移范围从纳米到毫米级。根据位移的不同量级，使用剪切法光（Speckle Pattern Shearing Interferometry，简称 Shearography）和条纹投影轮廓法（Fringe Projection Profilometry，简称 FPP）方法进行测量，比较了剪切法光和 FPP 的测量灵敏度和准确性，为工业无损测试提供了定量参考。

纤维增强复合层压板皱纹缺陷的跨尺度位移响应的实验研究

Experimental investigation of trans-scale displacement responses of  wrinkle defects in fiber reinforced composite laminates

In Fringe Projection Profilometry (FPP), achieving robust and accurate 3D
reconstruction with a limited number of fringe patterns remains a challenge in
structured light 3D imaging. Conventional methods require a set of fringe
images, but using only one or two patterns complicates phase recovery and
unwrapping. In this study, we introduce SFNet, a symmetric fusion network that
transforms two fringe images into an absolute phase. To enhance output
reliability, Our framework predicts refined phases by incorporating information
from fringe images of a different frequency than those used as input. This
allows us to achieve high accuracy with just two images. Comparative
experiments and ablation studies validate the effectiveness of our proposed
method. The dataset and code are publicly accessible on our project page
this https URL

通过使用 SFNet 模型，我们提出了一种基于两个 fringe 图像的相位恢复方法，实现了高精度的 3D 重建，解决了使用有限数量的 fringe 图案的挑战。

使用深度学习进行增强的条纹到相位框架

Enhanced fringe-to-phase framework using deep learning

The quick and accurate retrieval of an object height from a single fringe
pattern in Fringe Projection Profilometry has been a topic of ongoing research.
While a single shot fringe to depth CNN based method can restore height map
directly from a single pattern, its accuracy is currently inferior to the
traditional phase shifting technique. To improve this method's accuracy, we
propose using a U shaped High resolution Network (UHRNet). The network uses
UNet encoding and decoding structure as backbone, with Multi-Level convolution
Block and High resolution Fusion Block applied to extract local features and
global features. We also designed a compound loss function by combining
Structural Similarity Index Measure Loss (SSIMLoss) function and chunked L2
loss function to improve 3D reconstruction details.We conducted several
experiments to demonstrate the validity and robustness of our proposed method.
A few experiments have been conducted to demonstrate the validity and
robustness of the proposed method, The average RMSE of 3D reconstruction by our
method is only 0.443(mm). which is 41.13% of the UNet method and 33.31% of Wang
et al hNet method. Our experimental results show that our proposed method can
increase the accuracy of 3D reconstruction from a single fringe pattern.

本文提出了一种基于 UHRNet 的单形态法高度提取方法，通过设计复合损失函数并进行多项实验验证，证明该方法能够有效提高从单个光栅图案中进行三维重建的准确性。

UHRNet: 基于深度学习的单条纹模式精确三维重建方法

UHRNet: A Deep Learning-Based Method for Accurate 3D Reconstruction from  a Single Fringe-Pattern

In many optical metrology techniques, fringe pattern analysis is the central
algorithm for recovering the underlying phase distribution from the recorded
fringe patterns. Despite extensive research efforts for decades, how to extract
the desired phase information, with the highest possible accuracy, from the
minimum number of fringe patterns remains one of the most challenging open
problems. Inspired by recent successes of deep learning techniques for computer
vision and other applications, here, we demonstrate for the first time, to our
knowledge, that the deep neural networks can be trained to perform fringe
analysis, which substantially enhances the accuracy of phase demodulation from
a single fringe pattern. The effectiveness of the proposed method is
experimentally verified using carrier fringe patterns under the scenario of
fringe projection profilometry. Experimental results demonstrate its superior
performance in terms of high accuracy and edge-preserving over two
representative single-frame techniques: Fourier transform profilometry and
Windowed Fourier profilometry.

本文介绍如何使用深度神经网络对载波光栅进行分析，并在实验中证明这种方法的准确度和优越性。