Damage imaging in skin-stringer composite aircraft panel by ultrasonic-guided waves using deep learning with convolutional neural network. (May 2022)

• Record Type: Journal Article
• Authors: Cui, Ranting; Azuara, Guillermo; Lanza di Scalea, Francesco; Barrera, Eduardo
• Is Part Of: Structural health monitoring. Volume 21:Number 3(2022)
• Page Start: 1123
• Page End: 1138
• Publication Date: 2022-05
• Subjects: Structural health monitoring -- ultrasonic-guided waves -- skin-to-stringer assembly -- deep learning -- convolutional neural network; Structural health monitoring -- Periodicals; Structural stability -- Periodicals; Strength of materials -- Periodicals; Nondestructive testing -- Periodicals; Constructions -- Stabilité -- Périodiques; Résistance des matériaux -- Périodiques; Contrôle non destructif -- Périodiques; Electronic journals; 624.17
• DOI: 10.1177/14759217211023934
• Languages: English
• ISSNs: 1475-9217

Abstract

The detection and localization of structural damage in a stiffened skin-to-stringer composite panel typical of modern aircraft construction can be addressed by ultrasonic-guided wave transducer arrays. However, the geometrical and material complexities of this part make it quite difficult to utilize physics-based concepts of wave scattering. A data-driven deep learning (DL) approach based on the convolutional neural network (CNN) is used instead for this application. The DL technique automatically selects the most sensitive wave features based on the learned training data. In addition, the generalization abilities of the network allow for detection of damage that can be different from the training scenarios. This article describes a specific 1D-CNN algorithm that has been designed for this application, and it demonstrates its ability to image damage in key regions of the stiffened composite test panel, particularly the skin region, the stringer's flange region, and the stringer's cap region. Covering the stringer's regions from guided wave transducers located solely on the skin is a particularly attractive feature of the proposed SHM approach for this kind of complex structure.