A Deep Learning Framework for Vibration-Based Assessment of Delamination in Smart Composite Laminates

Abstract

Delamination is one of the detrimental defects in laminated composite materials that often arose due to manufacturing defects or in-service loadings (e.g., low/high velocity impacts). Most of the contemporary research efforts are dedicated to high-frequency guided wave and mode shape-based methods for the assessment (i.e., detection, quantification, localization) of delamination. This paper presents a deep learning framework for structural vibration-based assessment of delamination in smart composite laminates. A number of small-sized (4.5% of total area) inner and edge delaminations are simulated using an electromechanically coupled model of the piezo-bonded laminated composite. Healthy and delaminated structures are stimulated with random loads and the corresponding transient responses are transformed into spectrograms using optimal values of window size, overlapping rate, window type, and fast Fourier transform (FFT) resolution. A convolutional neural network (CNN) is designed to automatically extract discriminative features from the vibration-based spectrograms and use those to distinguish the intact and delaminated cases of the smart composite laminate. The proposed architecture of the convolutional neural network showed a training accuracy of 99.9%, validation accuracy of 97.1%, and test accuracy of 94.5% on an unseen data set. The testing confusion chart of the pre-trained convolutional neural network revealed interesting results regarding the severity and detectability for the in-plane and through the thickness scenarios of delamination.

Deep Transfer Learning Approach for Localization of Damage Area in Composite Laminates Using Acoustic Emission Signal

“…The image classification technique represented by a convolutional neural network (CNN) is a deep learning architecture that has been widely studied in recent years [ 26 ]. Khan [ 27 ] used CNN models and vibration data to identify delamination damage in composites. Tao [ 28 ] used deep learning algorithms and ultrasound to characterize fatigue damage in composite laminates.…”

Acoustic emission data based deep learning approach for classification and detection of damage-sources in a composite panel

“…The advantage of CNN is its capacity to deal with grid-like inputs, such as images and it produces similar values of features from local regions of similar patterns. Delamination in composites was identified using CNN-based vibration data [28]. Some more applications of CNN in the SHM domain includes the identification of-cracks in concrete, road cracks, pavement distress, cracks in historical structures, amongst others [29][30][31][32][33][34][35][36].…”

2D Temperature Field Reconstruction Using Optical Frequency Domain Reflectometry and Machine-Learning Algorithms

“…Growing interest to smart composite materials production opens new areas for fiber sensor application [ 16 , 17 ]. Since an optical fiber acting as a distributed sensor may be embedded in a material with a complex topology, or may pass through several different types of materials, etc., the problem of reconstructing the external physical impact on the material can be significantly complicated.…”