Acoustic emission signal detection by ceramic/polymer composite piezoelectrets embedded in glass‐epoxy laminates

Wenger, M. P.; Blanas, P.; Shuford, R. J.; Das-Gupta, D.K.

doi:10.1002/pen.10696

Cited by 36 publications

(18 citation statements)

References 25 publications

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“…[1][2][3] Piezoelectric composites consisting of a particulate piezoelectric ceramic phase randomly dispersed within a polymer matrix, generally referred to as 0-3 composites, 4 are possible candidates for integrated sensors in structural composites. 5,6 While displaying adequate mechanical properties, such composites exhibit intrinsically weak piezoelectric properties, such as a low piezoelectric charge coefficient ͑d 33 ͒ and only a moderate piezoelectric voltage coefficient ͑g 33 ͒. [7][8][9][10][11] These weak piezoelectric properties are the result of limited connectivity of the ceramic phase, which, in combination with a large discrepancy in dielectric properties for both phases, leads to an unfavorable electric field distribution in the composite.…”

Section: Introductionmentioning

confidence: 99%

Improving the d33 and g33 properties of 0-3 piezoelectric composites by dielectrophoresis

Ende

Bory

Groen

et al. 2010

Journal of Applied Physics

139

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Composites of piezoelectric particles in a polymer matrix with enhanced properties in the poling direction were prepared by dielectrophoretic alignment of the particles. The effect of processing parameters such as the amplitude and frequency of the applied electric field and the viscosity of the matrix on the dielectric and piezoelectric properties of the cured composite were demonstrated for a composite with a PZT volume fraction of 0.2. The amount of structuring could be correlated to the dielectric and piezoelectric properties of the composite through the P 2 order parameter for the average particle chain orientation, which was derived from image analysis of the microstructure. The piezoelectric properties of the aligned composites can be described with a new model for composites containing particles arranged into chains. The model predictions are in good agreement with the experimental results.

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Section: Introductionmentioning

confidence: 99%

Improving the d33 and g33 properties of 0-3 piezoelectric composites by dielectrophoresis

Ende

Bory

Groen

et al. 2010

Journal of Applied Physics

139

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show abstract

“…High-K polymer composites are known to be very useful materials for variety of electronic applications, such as transducers, piezo-sensors, hydrophones, etc. [1][2][3] and several reviews concerning various ferroelectric ceramic/polymer composites and their electrical properties have been published [4][5][6][7]. Recently, the high-K composite materials have raised a lot of interest as potential candidates for integration into high-frequency electronic devices.…”

Section: Introductionmentioning

confidence: 99%

Polymer Composites with High Dielectric Constant

2002

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“…These forms of damage not only affect the way in which the structure responds to applied loads, but may also lead to catastrophic system failure under certain environmental conditions. In general, damage detection systems with built-in diagnosis can be of the passive-sensing type or the active-sensing type with built-in diagnostics [1,2].…”

Section: Fxmentioning

confidence: 99%

Classifying Induced Damage in Composite Plates Using One-Class Support Vector Machines

2010

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For many engineering and aerospace applications, detection and quantification of multiscale damage in fiberreinforced composite structures is increasing in importance. Consequently, the development of an efficient and costeffective diagnosis scheme that can accurately sense, characterize, and evaluate the existence of any form of damage will offer significant potential for improving the performance, reliability, and extending the operational life of these complex systems. We present an approach to characterize and classify different damage states in composite laminates by measuring the change in the signature of the resultant wave that propagates through the anisotropic media under forced excitation. The wave propagation is measured using surface-mounted piezoelectric transducers. Sensor signals collected from test specimens with various forms of induced damage are analyzed using a patternrecognition algorithm known as the one-class support vector machines. The one-class support-vector-machine algorithm performs automatic anomaly detection and classification of damage signatures using various features from the sensor readings. The results obtained suggest that the one-class support-vector-machine algorithm, along with appropriate preprocessing techniques, can often achieve better accuracy than the popular k-nearest-neighbor method in detecting and classifying anomalies caused by structural defects, even when the perturbations caused in the sensor signals due to different damage states are minimal.Nomenclature fx = decision function Kx; x j = inner product kernel R ij = matrix with correct classification rate w = weight vector wn = random noise x i = input pattern y i = label i = Lagrangian multipliers = slack variable = bias = time delay = kernel width

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Acoustic emission signal detection by ceramic/polymer composite piezoelectrets embedded in glass‐epoxy laminates

Cited by 36 publications

References 25 publications

Improving the d33 and g33 properties of 0-3 piezoelectric composites by dielectrophoresis

Improving the d33 and g33 properties of 0-3 piezoelectric composites by dielectrophoresis

Polymer Composites with High Dielectric Constant

Classifying Induced Damage in Composite Plates Using One-Class Support Vector Machines

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