Graphene-functionalized polymer composites for self-sensing of ultrasonic waves: An initiative towards “sensor-free” structural health monitoring

Li, Yehai; Liao, Yaozhong; Su, Zhongqing

doi:10.1016/j.compscitech.2018.09.021

Cited by 36 publications

(21 citation statements)

References 75 publications

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“…A surface-mounted PZT sensor captures guided waves through the piezoelectric response to surface dynamic strain transferred through the bonding layer, 33 while the dispersive GN perceives wave-induced strain within the structure through the piezoresistivity based on the tunnelling effect. 34 Nevertheless, the WT results demonstrate that the time features of different frequencies share similar delay patterns between the first-triggered and later-response signals, which validates the feasibility of achieving AE source localization using GN.…”

Section: Resultsmentioning

confidence: 57%

Acousto-ultrasonics-based health monitoring for nano-engineered composites using a dispersive graphene-networked sensing system

Wang

et al. 2020

Structural Health Monitoring

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Sensing is a fundamental yet crucial part of a functional structural health monitoring system. Substantial research has been invested in developing new sensing techniques to enhance sensing efficiency and accuracy. Practical applications of structural health monitoring approaches to real engineering structures require strict criteria for the sensing system (e.g. weight, position, intrusion and endurance), which challenge existing sensing techniques. The boom in nanotechnology has offered promising solutions for the development of new sensing approaches. However, a bottleneck still exists when considering the density of sensors and surface-mounted modality of installation. In this study, graphene nanoparticles are dispersed into a glass fibre/epoxy composite to form a dispersive network sensing system. The piezoresistivity of the graphene-formed network changes locally as a result of the change of inter-nanoparticle distances which triggers the ‘tunnelling effect’ and drives the sensor to respond to propagating elastic waves. Due to the dense graphene network formed within the composite, only a small area is required, functioning as a single sensing element to capture ultrasonic waves. To validate such capability, passive acoustic emission tests and active guided ultrasonic wave tests are performed individually. The graphene-networked sensing system can precisely capture wave signals which contain effective features to identify impact spot or damage location. Integrating passive graphene-formed network and active lead zirconate titanate wafers can form a dense network, capable of fulfilling general structural health monitoring tasks.

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Section: Resultsmentioning

confidence: 57%

Acousto-ultrasonics-based health monitoring for nano-engineered composites using a dispersive graphene-networked sensing system

Wang

et al. 2020

Structural Health Monitoring

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“…PVP is chosen as the matrix in this study to develop the nanocomposite hybrid for the spray-on nanocomposite sensor, as PVP is a kind of water-soluble polymer that can easily form and further stabilize the dispersion of nanocomposites in the solvent without adding extra surfactant because of its amphiphilic groups (hydrophobic methylene group and hydrophilic amide group) [35]. In the authors’ previous study [36,37], a variety of nanofillers such as carbon black (CB), carbon nanotubes (CNTs), and graphene were investigated and proven as promising candidates for developing nanocomposite sensors that can be used for acquiring GUW signals. By virtue of the higher demand for signal quality in active SHM, two-dimensional (2-D) graphene nanoparticles are selected in this study as the nanofiller.…”

Section: Spray-on Sensor Fabrication and Characterizationmentioning

confidence: 99%

“…Therefore, the distance (zi) from a particular mesh node to the elliptical locus calculated by Equation (2) can be used to quantify the probability of the presence of damage at this node. The field value at each mesh node, which is dependent on the distance (zi), can be defined as [6,37]:Ffalse(zfalse)=true∫−∞zf(zi)dzi, where ffalse(zifalse)=false(1/σi2πfalse)expfalse[−zi2/2σi2false] depicts the probability density of damage occurrence at mesh node false(xm,xnfalse), false(m=1,2,…,L; n=1,2,…,Kfalse) perceived by the sensing path Ai−Si, σi the standard variance (σi=2…”

Section: Applications To Damage Characterizationmentioning

confidence: 99%

A Spray-on, Nanocomposite-Based Sensor Network for in-Situ Active Structural Health Monitoring

Cao

Zhou

Liao

et al. 2019

Sensors

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A new breed of nanocomposite-based spray-on sensor is developed for in-situ active structural health monitoring (SHM). The novel nanocomposite sensor is rigorously designed with graphene as the nanofiller and polyvinylpyrrolidone (PVP) as the matrix, fabricated using a simple spray deposition process. Electrical analysis, as well as morphological characterization of the spray-on sensor, was conducted to investigate percolation characteristic, in which the optimal threshold (~0.91%) of the graphene/PVP sensor was determined. Owing to the uniform and stable conductive network formed by well-dispersed graphene nanosheets in the PVP matrix, the tailor-made spray-on sensor exhibited excellent piezoresistive performance. By virtue of the tunneling effect of the conductive network, the sensor was proven to be capable of perceiving signals of guided ultrasonic waves (GUWs) with ultrahigh frequency up to 500 kHz. Lightweight and flexible, the spray-on nanocomposite sensor demonstrated superior sensitivity, high fidelity, and high signal-to-noise ratio under dynamic strain with ultralow magnitude (of the order of micro-strain) that is comparable with commercial lead zirconate titanate (PZT) wafers. The sensors were further networked to perform damage characterization, and the results indicate significant application potential of the spray-on nanocomposite-based sensor for in-situ active GUW-based SHM.

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“…This fact presents a practical burden for its implementation on large-scale structures due to the power supply and weight-cost issues. Modern passive sensors, for example, fiber-optic 7 or nanocomposite ones, 8,9 provide competitive sensitivity to propagating GWs, require fewer cable connections, and could be integrated into the structure during the manufacturing process. Therefore, adequate in situ SHM imaging systems can be developed via the sharing of active and passive elements if a reliable level of imaging quality has been achieved.…”

Section: Introductionmentioning

confidence: 99%

“…With a network of piezoelectric wafer active sensors (PWAS), the simulation of reverse GW excitation can be addressed through various PWAS-waveguide interaction models accounting for the basic physical principles related to the inverse piezoelectric effect (pin-force model, FEM-based technique, integral equation-based solution of the contact problem, etc.). However, when dealing with passive sensors, [7][8][9] the physical basis of reemission is not clear. Intuitively, it is reasonable to assume that when TR process from passive (and/or active) sensors is simulated employing some simplified or even arbitrary models for the GW excitation from the sensor locations, these emitted waves would focus on the position of the initial active source/scatterer.…”

Section: Introductionmentioning

confidence: 99%

Guided wave time-reversal imaging of macroscopic localized inhomogeneities in anisotropic composites

Eremin

Глушков

Glushkova

et al. 2019

Structural Health Monitoring

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Estimation of damage position and extent in the inspected structure is among the emerging problems of active structural health monitoring (SHM) with elastic guided waves. Unlike conventional non-destructive testing (NDT) techniques, which assume continuous surface scanning, SHM systems operate only with data from a limited number of sensors. Nevertheless, in the case of isotropic (metal) plate-like structures, computational time-reversal techniques have proved to be effective for estimating in situ the location and size of wave sources and/or local scatterers within the SHM concept. With composite plates, the reconstruction procedure faces additional difficulties associated with the complexity of wave phenomena caused by their lamination and anisotropy. In this article, we present an extension of the time reversal technique for the case of composite laminate plate-like structures. This technique relies on the simulation of the reversed guided waves generated by reciprocal surface loads applied at a sparse set of measurement points of a real sensor network. The proposed implementation is based on the far-field asymptotics for guided waves generated in arbitrarily anisotropic laminate waveguides by a prescribed source, which have been derived from the path integral representations in terms of Green’s matrix for the structures considered. The performance of this approach has been experimentally tested on cross-ply carbon fiber-reinforced plastic plates showing reliable and adequate results for both active (piezoactuators) and passive (artificial defects) source characterization.

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Graphene-functionalized polymer composites for self-sensing of ultrasonic waves: An initiative towards “sensor-free” structural health monitoring

Cited by 36 publications

References 75 publications

Acousto-ultrasonics-based health monitoring for nano-engineered composites using a dispersive graphene-networked sensing system

Acousto-ultrasonics-based health monitoring for nano-engineered composites using a dispersive graphene-networked sensing system

A Spray-on, Nanocomposite-Based Sensor Network for in-Situ Active Structural Health Monitoring

Guided wave time-reversal imaging of macroscopic localized inhomogeneities in anisotropic composites

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