Conductivity-Based Nanocomposite Structural Health Monitoring via Electrical Impedance Tomography.

structural health monitoring;nanocomposite;electrical impedance tomography;fiber-reinforced composite;piezoresistivity;damage detection;Mechanical Engineering;Engineering;Mechanical Engineering

Tallman, Tyler N.Epureanu, Bogdan ;

University of Michigan

关键词: structural health monitoring;    nanocomposite;    electrical impedance tomography;    fiber-reinforced composite;    piezoresistivity;    damage detection;    Mechanical Engineering;    Engineering;    Mechanical Engineering;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/111613/ttallman_1.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Nanocomposites have incredible potential when integrated as matrices in fiber-reinforced composites for transformative conductivity-based structural health monitoring (SHM). Key to this potential is the dependence of nanocomposite conductivity on well-connected nanofiller networks. Damage that severs the network or strain that affects the connectivity will manifest as a conductivity change. These damage or strain-induced conductivity changes can then be detected and spatially located by electrical impedance tomography (EIT). The nanofiller network therefore acts as an integrated sensor network giving unprecedented insight into the mechanical state of the structure.Despite the potential of combining nanocomposite matrices with EIT, important limitations exist. EIT, for example, requires large electrode arrays that are too unwieldy to be practically implemented on in-service structures. EIT also tends to be insensitive to small, highly localized conductivity losses as is expected from common modes fiber-reinforced composite damage such as matrix cracking and delamination. Furthermore, there are gaps in the fundamental understanding of nanocomposite conductivity.This thesis advances the state of the art by addressing the aforementioned limitations of EIT for conductivity-based SHM. This is done by insightfully leveraging the unique properties of nanocomposite conductivity to circumvent EIT;;s limitations. First, nanocomposite conductive properties are studied. This results in fundamental contributions to the understanding of nanocomposite piezoresistivity, the influence of nanofiller alignment on transverse percolation and conductivity, and conductivity evolution due to electrical loading. Next, the potential of EIT for conductivity-based health monitoring is studied and demonstrated for damage detection in carbon nanofiber (CNF)/epoxy and glass fiber/epoxy laminates manufactured with carbon black (CB) filler and for strain detection in CNF/polyurethane (PU). Lastly, the previously developed insights into nanocomposite conductive properties and damage detection via EIT are combined to greatly enhance EIT for SHM. This is done by first exploring how the sensitivity of EIT to delamination can be enhanced through nanofiller alignment and tailoring. A method of coupling the EIT image reconstruction process with known conductivity changes such as those induced by straining piezoresistive nanocomposites is developed and presented. This approach will tremendously bolster the image quality of EIT or, synonymously, significantly abate the number of electrodes required by EIT.