Defect imaging in layered composite plates and honeycomb sandwich structures using sparse piezoelectric transducers network

Carbon fiber reinforced polymer (CFRP) plates and honeycomb composite sandwich structures (HCSS) are widely used in the aerospace industry as they exhibit excellent strength-to-weight ratio, stiffness, toughness, corrosion resistance, etc. Nevertheless, defects, such as face sheet delamination or core-sheet debonding, may appear due to the impact forces or thermo-mechanical aging and can degrade these properties. Structural health monitoring (SHM) based on the use of guided elastic waves (GW) is regarded as a promising solution to detect such defects, and consequently to reduce maintenance costs and to extend structure service time. GWs propagate over large distances while being sensitive to structural inhomogeneities. Here, a SHM system prototype is proposed. It relies on a sparse grid of piezoelectric transducers distributed over the structure, used for both actuating and sensing GWs. Defect imaging is performed by means of the correlation based algorithm, the so-called Excitelet. It computes correlation coefficients between the theoretical and experimental GW signals for each pixel on the image representing the region of interest of the structure. The theoretical signals for CFRP are computed using a model based on a 2D semi-analytical finite element formulation. Analytical prediction of theoretical signals for the HCSS being intractable, a homogenization model is applied to the honeycomb core to replace it by an equivalent orthotropic plate preserving the same modelling approach. Defect imaging results are presented for both structures, namely a CFRP plate and a HCSS. The resolution of the corresponding images can be related to the wavelength of the inspecting mode.