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Inverse Calculation of Displacements in CNF/PU from EIT-Imaged Conductivity Changes

TYLER N. TALLMAN

Abstract


Composite materials modified with carbon nanofillers have been thoroughly studied for structural health monitoring (SHM) and damage detection applications because they are piezoresistive and therefore self-sensing. That is, mechanical effects such as strain and damage collocate with conductivity changes within the material. The visualization of strain or damage-induced conductivity changes can then be leveraged for damage identification. To this end, electrical impedance tomography (EIT), has also received considerable attention for SHM because it can non-invasively image spatiallydistributed conductivity changes. Despite the potential of piezoresistivity and EIT for SHM, this approach has an important limitation. EIT can only deduce conductivity changes. Conductivity, however, is not a structurally relevant parameter. From a SHM perspective, it would be much more useful to know the underlying mechanical state of the structure that gives rise to the observed conductivity changes. To achieve this, a novel piezoresistive inversion process is herein presented. This process endeavors to inversely determine the underlying displacement field of a piezoresistive material that results in an observed conductivity change as determined via EIT. The accuracy of this process is experimentally tested on a carbon nanofiber (CNF)/polyurethane (PU) nanocomposite. These preliminary results demonstrate that it is indeed possible to inversely determine the mechanical state of a body from conductivity data.


DOI
10.12783/shm2017/14203

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