Abstract Summary
Safety-critical composite structures are prone to barely visible damages which do not manifest themselves in the global structural response. Acousto-ultrasonic techniques have been utilized to identify these damages, but the problem is extremely challenging on multiple fronts. Com-posite waveguides with high-density cores and/or discontinuities are difficult to model for their dispersion characteristics. Damage identification in such structures thus requires, on one hand, monitoring and mapping of the acoustic signals collected from structures to damage characteristics and, on the other, a physics-based understanding the structural acoustic charac-teristics of composites. Damage identification in composites, including characterization of damage types, for practical applications need to merge the information from data-driven learning as well as modelling in-formation to give a robust/reliable identification framework. This paper focuses on a Bayesian framework for inverse identification of damage in compo-site waveguides based on experimental and modelling data. The dominant propagating wave modes in damaged composite waveguides is obtained as eigenmodes of the damaged compo-site waveguide. The obtained dispersion characteristics of the modes are assimilated into the Bayesian identification framework which enables online identification of damage characteris-tics from acoustic signal features. The proposed identification framework will be the basis of a holistic model-informed and data-driven autonomous monitoring of safety-critical structures.
