Vibration-Based In-Situ Detection and Quantification of Delamination in Composite Plates

Mei, Hanfei; Migot, Asaad; Haider, Mohammad Faisal; Joseph, Roshan; Bhuiyan, Md. Yeasin; Giurgiutiu, Victor

doi:10.3390/s19071734

Cited by 31 publications

(18 citation statements)

References 53 publications

(69 reference statements)

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“…Also, it was found that the transverse position of the delamination has a greater effect on the damping parameter (tanδ). Mei et al [32] also found that the different sizes and transverse position of delamination within a carbon fiber-reinforced polymer (CFRP) composite significantly modified the local defect resonance frequency based on a vibration in-situ detection method. They found strong local vibrations only occurred in the delamination regions.…”

Section: Discussionmentioning

confidence: 99%

Dynamic Mechanical Analysis on Delaminated Flax Fiber Reinforced Composites

et al. 2019

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It is well-known that the presence of the delamination in a plant fiber-reinforced composite is difficult to detect. However, the delamination introduces a local flexibility, which changes the dynamic characteristics of the composite structure. This paper presents a new methodology for composite laminate delamination detection, which is based on dynamic mechanical analysis. A noticeable delamination-induced storage modulus reduction and loss factor enhancement have been observed when the delaminated laminate was subjected to a forced oscillation compared to the intact composite laminate. For delamination area of 12.8% of the whole area of the composite laminate, loss factor of approximately 12% increase was observed. For near-to-surface delamination position, loss factor of approximately an 18% increment was observed. The results indicate that the delamination can be reliably detected with this method, and delamination position shows greater influence on the loss factor than that of the delamination size. Further investigations on different frequencies and amplitudes configurations show that the variation of loss factor is more apparently with low frequency as well as the low amplitude.

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Section: Discussionmentioning

confidence: 99%

Dynamic Mechanical Analysis on Delaminated Flax Fiber Reinforced Composites

et al. 2019

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“…The detection and characterization of various types of composite damage are quite difficult. Delamination is a common and dangerous failure mode for composite structures, because it occurs and grows in the absence of any visible surface damage, making it difficult to detect through visual inspection [2]. In addition, barely visible impact damage (BVID) due to low-velocity impact, in the form of fiber breakage, matrix cracking, and interlaminar delamination, is also invisible to the naked eye and is easily induced from various sources such as bird strikes, tools dropped on parts, or runway debris [3,4].…”

Section: Introductionmentioning

confidence: 99%

Multimode Guided Wave Detection for Various Composite Damage Types

et al. 2020

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This paper presents a new methodology for detecting various types of composite damage, such as delamination and impact damage, through the application of multimode guided waves. The basic idea is that various wave modes have different interactions with various types of composite damage. Using this method, selective excitations of pure-mode guided waves were achieved using adjustable angle beam transducers (ABTs). The tuning angles of various wave modes were calculated using Snell’s law applied to the theoretical dispersion curves of composite plates. Pitch–catch experiments were conducted on a 2-mm quasi-isotropic carbon fiber-reinforced polymer (CFRP) composite plate to validate the excitations of pure fundamental symmetric mode (S0) and shear horizontal mode (SH0). The generated pure S0 mode and SH0 mode were used to detect and separate the simulated delamination and actual impact damage. It was observed that S0 mode was only sensitive to the impact damage, while SH0 mode was sensitive to both simulated delamination and impact damage. The use of pure S0 and SH0 modes allowed for damage separation. In addition, the proposed method was applied to a 3-mm-thick quasi-isotropic CFRP composite plate using multimode guided wave detection to distinguish between delamination and impact damage. The experimental results demonstrated that the proposed method has a good capability to detect and separate various damage types in composite structures.

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“…Yang et al [24] proposed that the variations of modal frequency under mass loading could be used to quantify the effects of delamination in laminated composites. Mei et al [25] studied high-frequency local vibration for detecting and quantifying the size, shape, and depth of delamination in composite plates. Sikdar et al [26] investigated the effects of debonding and variable ambient temperature on the propagation of Lamb wave in composite materials.…”

Section: Introductionmentioning

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 In-Situ Detection and Quantification of Delamination in Composite Plates

Cited by 31 publications

References 53 publications

Dynamic Mechanical Analysis on Delaminated Flax Fiber Reinforced Composites

Dynamic Mechanical Analysis on Delaminated Flax Fiber Reinforced Composites

Multimode Guided Wave Detection for Various Composite Damage Types

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

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