A Deep Learning Framework for Damage Assessment of Composite Sandwich Structures. (1st July 2021)

• Record Type: Journal Article
• Authors: Meruane, Viviana; Aichele, Diego; Ruiz, Rafael; López Droguett, Enrique
• Other Names: Sbarufatti Claudio Academic Editor.
• Is Part Of: Shock and vibration. Volume 2021(2021)
• Publication Date: 2021-07-01
• Subjects: Shock (Mechanics) -- Periodicals; Vibration -- Periodicals; 534.5
• DOI: 10.1155/2021/1483594
• Languages: English
• ISSNs: 1070-9622

Abstract

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.