Abstract
The vibrational behavior of composite structures has been demonstrated as a useful feature for identifying debonding damage. The precision of the damage localization can be greatly improved by the addition of more measuring points. Therefore, full-field vibration measurements, such as those obtained using high-speed digital image correlation (DIC) techniques, are particularly useful. In this study, deep learning techniques, which have demonstrated excellent performance in image classification and segmentation, are incorporated into a novel approach for assessing damage in composite structures. This article presents a damage-assessment algorithm for composite sandwich structures that uses full-field vibration mode shapes and deep learning. First, the vibration mode shapes are identified using high-speed 3D DIC measurements. Then, Gaussian process regression is implemented to estimate the mode shape curvatures, and a baseline-free gapped smoothing method is applied to compute the damage images. The damage indices, which are represented as grayscale images, are processed using a convolutional-neural-network-based algorithm to automatically identify damaged regions. The proposed methodology is validated using numerical and experimental data from a composite sandwich panel with different damage configurations.
Highlights
Academic Editor: Claudio Sbarufatti e vibrational behavior of composite structures has been demonstrated as a useful feature for identifying debonding damage. e precision of the damage localization can be greatly improved by the addition of more measuring points. erefore, full-field vibration measurements, such as those obtained using high-speed digital image correlation (DIC) techniques, are useful
The accuracy of these methods is sensitive to the family and order of the wavelets selected [9]. e gapped smoothing (GS) method was initially proposed by Ratcliffe and Bagaria [6], who assumed that the undamaged mode shapes can be estimated using a smoothed version of the damaged mode shapes. en, the damage indices are computed from the difference between the shapes of the undamaged and damaged curvature modes. is method has proven to be useful in different damage detection and localization applications, such as damage identification in
Meruane et al [4] combined the GS method with curvature mode shapes estimated through Gaussian process (GP) regression. ey demonstrated that GP regression allows to obtain noise-free mode shape curvatures from mode shape displacements with noise, improving the damage identification results compared to those using the conventional GS method
Summary
In the GS method, the undamaged mode shape curvatures are calculated using a smoothed version of the damaged mode shape curvatures (Laplacian). e undamaged mode shape curvatures are approximated using first-order base functions, as follows:. E undamaged mode shape curvatures are approximated using first-order base functions, as follows:. Where gi,jis a vector of base functions and θi,jdenotes its coefficients: gTi,j 1, xi, yj, θTi,j a0, a1, a2. Let us consider the neighboring points of (xi, yj); (9) can be expressed in the matrix form as follows: λrxi, yj GTr xi, yjθi,j,. E measure of damage at point (xi, yj)is estimated by the difference in the curvatures of the undamaged and damaged modes, represented by damage index dr: drxi, yj ∇2φrxi, yj − Crxi, yj. This expression is expanded to consider the first m modes:.
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