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.

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.

Highly flexible nanocomposites have tremendous potential as smart, self-sensing materials because their conductivity is inherently linked to their mechanical state. Herein, carbon nanofiber (CNF)/polyurethane (PU)nanocomposites are studiedfor tactile imaging and distributed strain sensing via electrical impedance tomography (EIT) by investigatingthe influence of filler volume fraction on microscale morphology, piezoresistive response while bonded to mechanically loaded substrates, and sensitivity to distributed strain.Load testing of the bonded sensor reveals that viscoelasticity and filler volume fraction profoundly affect the piezoresistive response. EIT is able to accurately capture and discern between multiple points of contact in each volume fraction with lower volume fractions being more sensitivethereby demonstrating the potential of utilizing tomographic methods for tactile imaging and distributed strain sensing in PU-based nanocomposites.

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.

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