Subsurface damage detection via noncontact laser based surface level strain sensing smart skin with carbon nanotubes

Abstract

Subsurface damage may remain undetected for long periods and accumulate until it leads to structural failure. Non-destructive measurements of surface strain can provide critical data for detecting early symptoms of hidden damage and helping to prevent serious consequences. This study investigates the capabilities of the developed method called strain-sensing smart skin (S4) to find internal damage. The S4 method uses single-wall carbon nanotubes in a thin polymer coating as optically interrogated fluorescent strain sensors to measure the strain map. A set of aluminum specimens with internal cavities representing damage were coated both with S4 films and with digital image correlation (DIC) speckle patterns. Maps of surface strain from the two methods were then compared (after the application of tensile stress to the specimens) to compute finite element method (FEM) strain maps. The strain maps obtained from S4 point-wise laser scanning agreed well with FEM predictions, while DIC strain maps were not very precise. S4 maps revealed strain map features with superior accuracy. It is concluded that S4 strain mapping can be used as a non-destructive evaluation method for detecting and visualizing subsurface damage in structural elements for structural health monitoring.