Performance of 1–3 piezoelectric composites with porous piezoelectric matrix

Della, Christian N.; Shu, Dongwei

doi:10.1063/1.4822109

Cited by 10 publications

(4 citation statements)

References 24 publications

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“…The effect of porosity in non-piezoelectric polymer matrix on the effective material properties in terms of the volume fraction, shape and orientation of pores has been reported in this work. The effect of porosity in the polymer matrix has been taken into consideration to show that the material properties depended on the porosity to a considerable extent [199].…”

Section: Effect Of Porositymentioning

confidence: 99%

Effective properties and nonlinearities in 1-3 piezocomposites: a comprehensive review

Pramanik

Arockiarajan

2019

Smart Mater. Struct.

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1-3 piezocomposites are excellent candidate materials for sensor, actuator and transducer applications owing to their remarkable dielectric properties, enhanced piezoelectric coupling constants and improved hydrostatic performance along with tunable acoustic impedance, high bandwidth and reliability. These materials find extensive use in aerospace, naval and biomedical sectors. 1-3 piezocomposites show a linear response when subjected to low electric fields and/or mechanical stresses. In such cases, linear models are sufficient for predicting their linear response. But, when high electro-mechanical loads are applied to these materials, they show nonlinearity owing to the presence of a passive and viscoelastic polymer matrix phase and inherent hysteretic damping in the piezoceramic fibers. This is when it becomes mandatory to understand both their linear and nonlinear behavior under different magnitudes of thermo-electro-mechanical static and dynamic loads. Linear response is modeled using linear piezoelectric constitutive equations. Nonlinearities in the form of hysteresis, depolarization, fatigue and creep occurs in 1-3 piezocomposites, which drastically affects their accuracy, precision and efficiency. In order to understand these nonlinearities, analytical and numerical methods have been proposed by several researchers in the past. An endeavor has been undertaken to review some of the attempts made earlier in these directions. Effective properties of these inhomogeneous media are evaluated through different hypotheses and assumptions. Most often, experimental routes are undertaken to predict material properties of 1-3 piezocomposites; nevertheless, the experimental evaluation of a few material properties is quite difficult and sometimes impossible, even with the best state-of-the-art experimental facilities. This motivates researchers to develop theoretical models to predict these material properties. Nonlinearities in 1-3 piezocomposites have been studied by researchers earlier with different theoretical modeling approaches and experimental techniques. This review paper is an endeavor to discuss the progress regarding the study of effective properties and nonlinearities in 1-3 piezocomposites in a coherent and holistic manner.

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Section: Effect Of Porositymentioning

confidence: 99%

Effective properties and nonlinearities in 1-3 piezocomposites: a comprehensive review

Pramanik

Arockiarajan

2019

Smart Mater. Struct.

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“…The reliability of 1-3 piezocomposites is affected by the presence of multiple interface cracks [34]. The effect of porosity and the polarization orientation dependent on the performance of 1-3 piezocomposites are studied using a micromechanics based model [35]. The behaviour of 1-3 piezocomposites under pure mechanical loading at elevated thermal environment and combined electromechanical loading at room temperature has been studied and it shows the dependency of fibre volume fraction when subjected to these loading conditions [36,37].…”

Section: Introductionmentioning

confidence: 99%

Numerical modelling and experimental characterization of temperature-dependent viscoelastic effect on the ferroelastic behaviour of 1–3 piezocomposites

Jayendiran

Arockiarajan

2015

Sensors and Actuators A: Physical

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“…A large body of work already exists aimed at the micromechanical modeling of the thermo-mechanical and multiphysics behavior of heterogeneous materials, ranging from the simplest rule-of-mixture assumptions and processing to the more complex geometric models that require computationally demanding analytical and numerical treatment. [4][5][6][7] In general, these approaches can be categorized into two broad categories, namely, the representative volume element (RVE) and repeating unit cell (RUC) based approaches, according to the different geometric representations of material microstructures that involve the concepts of statistical homogeneity and periodicity, 8,9 respectively. The extensively employed composite cylinder and sphere assemblage models, 10 the Mori-Tanaka scheme, 11 and the three-phase model 4 were the earliest and most classic geometric RVE-based models that provide explicit expressions for the effective moduli.…”

Section: Introductionmentioning

confidence: 99%

Deep learning in heterogeneous materials: Targeting the thermo-mechanical response of unidirectional composites

Chen

2020

Journal of Applied Physics

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In this communication, a multi-task deep learning-driven homogenization scheme is proposed for predicting the effective thermomechanical response of unidirectional composites consisting of a random array of inhomogeneity. Toward this end, 40 000 repeating unit cells (RUCs) comprising an arbitrary number of locally irregular inclusions are generated over a wide range of fiber volume fractions. The finite-volume direct averaging micromechanics is then employed to evaluate the homogenized thermo-mechanical moduli of each RUC. Subsequently, a twodimensional deep convolution neural network (CNN) is constructed as a surrogate model to extract the statistical correlations between the RUC geometrical information and the corresponding homogenized response. The RUC images together with their homogenized moduli are divided into two datasets in a ratio of 9:1 with the former part used for training the CNN model and the latter part used for verification. The results presented in this contribution demonstrate that the deep CNN predictions exhibit remarkable correlations with the theoretical values generated by the finite-volume micromechanics, with a maximum relative prediction error of less than 8%, providing good support for the data-based homogenization approach.

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Performance of 1–3 piezoelectric composites with porous piezoelectric matrix

Cited by 10 publications

References 24 publications

Effective properties and nonlinearities in 1-3 piezocomposites: a comprehensive review

Effective properties and nonlinearities in 1-3 piezocomposites: a comprehensive review

Numerical modelling and experimental characterization of temperature-dependent viscoelastic effect on the ferroelastic behaviour of 1–3 piezocomposites

Deep learning in heterogeneous materials: Targeting the thermo-mechanical response of unidirectional composites

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