Vibration-Based Delamination Detection in a Composite Plate

Ullah, Israr; Sinha, Jyoti; Pinkerton, Andrew J.

doi:10.1080/15376494.2011.643275

Cited by 10 publications

(7 citation statements)

References 25 publications

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“…A method for delamination detection in composites structure based on the vibration responses when excited at the lower modes is suggested by Ullah and Sinha. 17 Two review papers on the vibration analysis and damage detection of composite structures are presented by Senthil et al 18 and Thombare et al 19 Zhang etal. 20 examined three different inverse algorithms for solving the non-linear equations to predict the interface, location and size of delamination: direct solution using a graphical method, artificial neural network (ANN) and surrogate-based optimization.…”

Section: Introductionmentioning

confidence: 99%

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Moving support technique for delaminatoin detection in laminated composite beams using the first natural frequency

Torabi

Samadi

Heidari-Rarani

2017

Journal of Reinforced Plastics and Composites

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A new practical procedure is presented for delamination detection in beam-like composite structures. This technique identifies the delamination axial location and its length accurately based on the only first natural frequency. An additional simply support condition besides the boundary conditions is defined and it is moved along the beam length. When this support is located on delamination, especially its tips, the frequency reduction will be noticeable. Simulating this idea in ABAQUS finite element software shows that the location and size of delamination can be detected accurately. To verify the numerical results, some experiments are conducted and a new fixture is designed and manufactured for simulating the additional moving support along the length of the beam. Both finite element and experimental results show the ability of the proposed method in delamination detection for different boundary conditions. Also, this new simple and applicable technique can be used even for small delamination lengths by monitoring only the first natural frequency, unlike the available methods in the literature.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Moving support technique for delaminatoin detection in laminated composite beams using the first natural frequency

Torabi

Samadi

Heidari-Rarani

2017

Journal of Reinforced Plastics and Composites

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“…Observations are made on the effects of delamination size and its location around the cutout on the natural frequency of a thick square laminate; a substantial decrease in natural frequencies is observed [10]. The possibility of delamination detection using nonlinear interaction in the delaminated region is explored [11]. In functionally graded beams with a single delamination, the natural frequencies are affected when the beam has longer delamination [12].…”

Section: Introductionmentioning

confidence: 99%

Vibroacoustic performance of fiber metal laminates with delamination

Balakrishnan

Raja

Dwarakanathan

et al. 2016

Mechanics of Advanced Materials and Structures

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In the present work, the numerical assessment of vibroacoustic (VA) performance of fiber metal laminates (FML) with mid-plane center delamination is presented. A fluid structure interaction study has been done using finite element method (FEM). Experimental validation is performed on an aluminium (AL) panel for verifying the correctness of finite element (FE) idealization procedure to simulate the fluid-structure interaction. Delamination is introduced in the FE model of FML panel and VA analysis is subsequently carried out. Sound transmission loss (STL) is computed on the panel with center delamination and without delamination. The overall sound pressure level (OASPL) shows that the presence of delamination (40% in total area) in FML has not changed the total energy of the transmitted sound when compared to aluminium and composites. However, in the narrow frequency bands (150-200 Hz, 200-250 Hz), the sound transmission nature has been significantly affected due to local delamination modes participating in the fluid-structure interaction process.

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“…In addition, structural vibration is produced during operation of the component/structure and no input source of excitation is required. However, in most of the published literature, the parameters of structural vibration (e.g., natural frequency, modal damping) have been used for the global assessment (i.e., presence) of delamination [66][67][68][69]. The results of the methodology proposed in this work show the possibility to employ low-frequency structural vibration for the global (i.e., detection) and local assessment (i.e., localization, For the in-plane location of delamination (Figure 8a), it is observed that the inner delaminations (AM, BM, CM) are difficult to detect compared with the edge delaminations (AU, AL, BU, BL, CU, CL).…”

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confidence: 99%

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

Khan

Shin

Lim

et al. 2020

Sensors

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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.

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Vibration-Based Delamination Detection in a Composite Plate

Cited by 10 publications

References 25 publications

Moving support technique for delaminatoin detection in laminated composite beams using the first natural frequency

Moving support technique for delaminatoin detection in laminated composite beams using the first natural frequency

Vibroacoustic performance of fiber metal laminates with delamination

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

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