Bond Line Thickness Estimation in Composite Structures Using Multiple Inspection Techniques

Abstract

Imaging and other nondestructive evaluation techniques are commonly used for material characterization and defect recognition in safety critical aerospace applications, with data fusion providing the framework for uncertainty quantification in these contexts. Most commonly, forward physics-based modeling predicts the response conditioned on material properties and defect assumptions, and probabilistic methods are used to infer the hidden state of the subject of the inspection from a combination of prior information, likelihoods, and inspection data. In this paper Bayesian methods are used to estimate bond thickness in lap joints comprised of aluminum adherends using a combination of infrared thermography and ultrasound. The concept of the conflation of probability distributions is applied to combine the posterior distributions derived from thermography and ultrasound and the quality of the fused estimates are compared against the individual estimates against synthetic data that was created to mimic the inspection of a lap joint comprised of aluminum adherends.