Damage detection methodology for composite UAV wings using modal analysis and probabilistic concepts

Abstract

Monitoring of structural integrity is critical in many fields today, and particularly so in the civil, mechanical and aerospace engineering industries. In the aerospace industry, appreciably sized and almost exclusively composite UAVs share the airspace with other aircraft. Such composite structures also pose numerous uncertainties to structural health monitoring and analysis techniques. This necessitates research into a methodology for practical and effective structural health monitoring techniques. This work presents a methodology for structural health monitoring and particularly delamination detection in composite wing structures. The approach uses experimental modal analysis with due consideration for the probabilistic effects of random variations in material and geometrical properties, for the purpose of a general and non wing-specific damage detection technique. A large number of composite material coupons were tested to determine statistical distributions of 2D orthotropic material properties, using an optical image correlation system to reduce the expense of testing. Uncertainties in the wing geometry arising from manufacturing variances were taken into consideration. The material properties of the foam spar and resin beadings were considered isotropic and deterministic. A finite element model of the wing was subsequently improved using a scanning laser vibrometer to conduct detailed experimental modal analyses of five wings, and a multi-model updating approach based on frequency and mode shape information was used to update selected sensitive material properties. Significant improvement was accomplished. Using the probabilistic material property database, a confidence region was established for wing mode shapes through a Monte Carlo procedure. It was shown that delamination effects are capable of perturbing the dynamic mode shapes beyond the confidence regions implied by the material uncertainties. This provides a basis for further development of a structural health monitoring methodology for composite structures, taking due account of the many uncertainties in the structure.