A Deep Learning Framework for Damage Assessment of Composite Sandwich Structures

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.

Topics & Concepts