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

A representative volume element (RVE) is a reasonably small unit of
microstructure that can be simulated to obtain the same effective properties as
the entire microstructure sample. Finite element (FE) simulation of RVEs, as
opposed to much larger samples, saves computational expense, especially in
multiscale modeling. Therefore, it is desirable to have a framework that
determines RVE size prior to FE simulations. Existing methods select the RVE
size based on when the FE-simulated properties of samples of increasing size
converge with insignificant statistical variations, with the drawback that many
samples must be simulated. We propose a simulation-free alternative that
determines RVE size based only on a micrograph. The approach utilizes a machine
learning model trained to implicitly characterize the stochastic nature of the
input micrograph. The underlying rationale is to view RVE size as the smallest
moving window size for which the stochastic nature of the microstructure within
the window is stationary as the window moves across a large micrograph. For
this purpose, we adapt a recently developed Fisher score-based framework for
microstructure nonstationarity monitoring. Because the resulting RVE size is
based solely on the micrograph and does not involve any FE simulation of
specific properties, it constitutes an RVE for any property of interest that
solely depends on the microstructure characteristics. Through numerical
experiments of simple and complex microstructures, we validate our approach and
show that our selected RVE sizes are consistent with when the chosen
FE-simulated properties converge.

通过微观图像分析，我们提出了一种确定代表性体积单元（RVE）大小的无需仿真的方法，该方法利用机器学习模型隐式表征输入微观图像的随机性质，并通过验证实验证明了我们选择的 RVE 大小与有限元仿真结果的一致性。