Tyler N. Tallman

Damage detection and conductivity evolution in carbon nanofiber epoxy via electrical impedance tomography

Utilizing electrically conductive nanocomposites for integrated self-sensing and health monitoring is a promising area of structural health monitoring (SHM) research wherein local changes in conductivity coincide with damage. In this research we conduct proof of concept investigations using electrical impedance tomography (EIT) for damage detection by identifying conductivity changes and by imaging conductivity evolution in a carbon nanofiber (CNF) filled epoxy composite. CNF/epoxy is examined because fibrous composites can be manufactured with a CNF/epoxy matrix thereby enabling the entire matrix to become self-sensing. We also study the mechanisms of conductivity evolution in CNF/epoxy through electrical impedance spectroscopy (EIS) testing. The results of these tests indicate that thermal expansion is responsible for conductivity evolution in a CNF/epoxy composite.

Damage detection via electrical impedance tomography in glass fiber/epoxy laminates with carbon black filler

The conductivity of glass fiber reinforced polymers with nanocomposite matrices can be leveraged for structural health monitoring. Since nanocomposite matrices depend on well-connected networks of conductive nanofillers for electrical conductivity, matrix damage will sever the connection between fillers and result in a local conductivity loss. Monitoring composite conductivity changes can therefore give insight into the state of the matrix. Existing conductivity-based structural health monitoring methods are either insensitive to matrix damage or employ large electrode arrays. This research advances the state of the art by combining the superior imaging capabilities of electrical impedance tomography with conductive networks of nanofillers in the composite matrix. Electrical impedance tomography for damage detection in glass fiber/epoxy laminates with carbon black nanocomposite matrices is characterized by identifying a lower threshold of through-hole detection, demonstrating the capability of electrical impedance tomography to accurately resolve multiple through holes, and locating impact damage. It is found that through holes as small as 3.18 mm in diameter can be detected, and electrical impedance tomography can detect multiple through holes. However, sensitivity to new through holes is diminished in the presence of existing through holes unless a damaged baseline is used. Finally, it is shown that electrical impedance tomography is also able to accurately locate impact damage. These research findings demonstrate the considerable potential of conductivity-based health monitoring for glass fiber reinforced polymer laminates with conductive networks of nanoparticles in the matrix.

An arbitrary strains carbon nanotube composite piezoresistivity model for finite element integration

Piezoresistive carbon nanotube (CNT) composites can radically enhance structural identification and health monitoring through continuous self-sensing. However, prevailing piezoresistivity models examine only uniaxial strain and are too computationally burdensome to be implemented on a structural scale. This research circumvents these limitations by developing an analytical piezoresistivity model for CNT composites that is adaptable to the finite element formulation enabling the analysis of complicated structures subjected to arbitrary strain. The accuracy of the model is verified by comparison to uniaxial piezoresistivity experiments in existing literature.

Enhanced delamination detection in multifunctional composites through nanofiller tailoring

Delamination detection is challenging but vital to ensuring the safety of an increasing number of structures utilizing laminated composites. Laminated composites manufactured with nanocomposite matrices show incredible potential for enhanced material properties including electrical conductivity which can be leveraged for conductivity-based damage identification. Advances in nanotechnology also enable the development of materials engineered at micro-scales for specific macro-scale applications. This research capitalizes on that approach by exploring how micro-scale manipulation of nanofillers can bolster structural-scale damage identification. An equivalent resistor network model is developed to predict nanocomposite conductivity as a function of filler alignment. Next, electrical impedance tomography is employed to locate delamination damage. It is found that aligning nanofillers at an angle through the thickness of a plate markedly enhances sensitivity to delamination damage. This demonstrates that powerful insights can be gleaned from the micro-scale tailoring approach to material design to radically enhance structural-scale damage identification.

Damage and strain identification in multifunctional materials via electrical impedance tomography with constrained sine wave solutions

Tomographic methods such as electrical impedance tomography have tremendous potential for electrical conductivity-based structural health monitoring, damage identification, strain sensing, and environmental/corrosion sensing as evidenced by a growing body of literature employing electrical impedance tomography. However, electrical impedance tomography also has important limitations preventing its widespread acceptance such as requiring burdensome computational resources not available to on-board structural health monitoring and often difficult to obtain initial estimates of conductivity distributions. We herein overcome these limitations by developing a novel electrical impedance tomography reconstruction algorithm with substantially abated computational requirements and independent of initial estimates. This method is predicated on the difference between two sets of observed voltages being due to a difference in resistivity that is constrained to be a summation of two-dimensional sine waves. This method is first explored analytically and then demonstrated experimentally on three different materials. This approach is an important advancement to the state of the art because it overcomes critical limitations of electrical impedance tomography thereby substantially facilitating the viability of electrical impedance tomography for real structural health monitoring applications.

On the inverse determination of displacements, strains, and stresses in a carbon nanofiber/polyurethane nanocomposite from conductivity data obtained via electrical impedance tomography

Carbon nanofiller-modified composites possess extraordinary potential for structural health monitoring because they are piezoresistive and therefore self-sensing. To date, considerable work has been done to understand how strain affects nanocomposite conductivity and to utilize electrical impedance tomography for detecting strain or damage-induced conductivity changes. Merely detecting the occurrence of mechanical effects, however, does not realize the full potential of piezoresistive nanomaterials. Rather, knowing the mechanical state that results in the observed conductivity changes would be much more valuable from a structural health monitoring perspective. Herein, we make use of an analytical piezoresistivity model to inversely determine the displacement field of a strained carbon nanofiber/polyurethane nanocomposite from conductivity changes obtained via electrical impedance tomography. From the displacements, kinematic and constitutive relations are used to calculate strains and stresses, respectively. A commercial finite element simulation is then used to validate the accuracy of these predictions. These results concretely demonstrate that it is possible to inversely determine displacements, strains, and stresses from conductivity data thereby enabling unprecedented insight into the mechanical response of piezoresistive nanofiller-modified materials and structures.

The influence of nanofiller alignment on transverse percolation and conductivity

Nanocomposites have unprecedented potential for conductivity-based damage identification when used as matrices in structural composites. Recent research has investigated nanofiller alignment in structural composites, but because damage identification often requires in-plane measurements, percolation and conductivity transverse to the alignment direction become crucial considerations. We herein contribute indispensable guidance to the development of nanocomposites with aligned nanofiller networks and insights into percolation trends transverse to the alignment direction by studying the influence of alignment on transverse critical volume fraction, conductivity, and rate of transition from non-percolating to percolating in three-dimensional carbon nanotube composite systems.

Enhanced health monitoring of fibrous composites with aligned carbon nanotube networks and electrical impedance tomography

The high strength to weight ratio of fibrous composites such as glass-fiber reinforced polymers (GFRP) makes them prominent structural materials. However, their laminar nature is susceptible to delamination failure the onset of which traditional structural health monitoring (SHM) techniques cannot reliably and accurately detect. Carbon nano-tubes (CNT) have been recently used to tailor the electrical conductivity of polymer based materials that otherwise behave as insulators. The occurrence of damage in the polymer matrix produces localized changes in conductivity which can be tracked using electrical impedance tomography (EIT). This paper explores combining advances in composite manufacturing with EIT to develop a SHM technique that exploits anisotropic conductance monitoring for enhanced delamination and matrix crack detection.

An inverse methodology for calculating strains from conductivity changes in piezoresistive nanocomposites

Carbon nanofiller-modified composites have incredible potential for self-sensing, structural health monitoring (SHM), and damage identification because they are piezoresistive. This means that conductivity changes can be monitored to detect mechanical perturbations such as damage and strain. Recently, considerable research effort has been dedicated to understanding the effect of strain on nanocomposite conductivity. However, from a SHM perspective, it is more desirable to know the strain state giving rise to an observed conductivity change. Therefore, we herein develop an inverse relation for nanocomposite piezoresistivity. That is, given a conductivity change, we seek to determine the underlying strain state of the nanocomposite. This is done by formulating an inverse problem wherein we seek the strain state that minimizes the difference between the observed conductivity and the conductivity predicted by a piezoresistivity model. This approach is verified through simulations by making use of a piezoresistivity model published in the literature. Our results show that strains can indeed be reconstructed from known conductivity changes. When coupled with conductivity-based SHM such as electrical impedance tomography, these results have the potential to provide unprecedented insight into the mechanical response of nanofiller-modified materials and structures.

Predicting failure from conductivity changes in piezoresistive nanocomposites

Self-sensing nanocomposites hold immense potential for structural health monitoring (SHM) because their electrical conductivity is influenced by mechanical effects such as strain and damage. This property, known as piezoresistivity, has been leveraged by numerous researchers for damage detection. However, from a SHM perspective, it would be much more beneficial to know the stresses that precipitate failure so that mitigating actions can be taken. Herein, we propose a novel method of accomplishing this based on the concept of piezoresistive inversion. Using simulations, the conductivity of a deformed piezoresistive nanocomposite is first determined using electrical impedance tomography (EIT). Next, the piezoresistive inversion process is used to determine the displacement field that gives rise to the conductivity obtained via EIT. Strains are then determined from kinematic relations and stresses from constitutive relations. A suitable failure criterion is then used to predict the location and likelihood of failure. Using these simulations, we demonstrate that the proposed approach allows for the accurate localization and quantification of stress concentrations which may induce failure. Because of these damage prediction capabilities, this approach has the potential to enable unparalleled predictive SHM capabilities.