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Surface strain map reconstruction using dense network of thin film sensors

The authors have developed a capacitive-based thin film sensor for monitoring strain on meso-surfaces. Arranged in a network configuration, the sensing system is analogous to a biological skin, where local strain can be monitored over a global area. The measurement principle is based on a measurable change in capacitance provoked by strain. In the case of bi-directional in-plane strain, the sensor output contains the additive measurement of both principal strain components. Here, an algorithm for retrieving the directional strain from measurements is presented. The algorithm leverages the dense network application of the thin film sensor to reconstruct the surface strain map. A bi-directional shape function is assumed, and it is differentiated to obtain expressions for planar strain. A least square estimator (LSE) is used to reconstruct the planar strain map from the sensors’ measurements, after the system’s boundary conditions have been enforced in the model. The coefficients obtained by the LSE can be used to reconstruct the estimated strain map or the deflection shape directly. Results from numerical simulations and experimental investigations show good performance of the algorithm, in particular for monitoring surface strain on cantilever plates and wind turbine blades.

Author(s):

Simon Laflamme    
Iowa State University
United States

Hussam Saleem    
Iowa State University
United States

Austin Downey    
Iowa State University
United States