Ultrasonic classification of thin layers

Methods for non-destructive inspection of layered materials are becoming more and more popular as a way of assuring product integrity and quality. In this paper, we present a model-based technique using ultrasonic measurements for classification of thin bonding layers within three-layered materials. This could be, for example, an adhesive bond between two thin plates, where the integrity of the bonding layer needs to be evaluated. The method is based on a model of the wave propagation of pulse-echo ultrasound that first reduces the measured data to a few parameters for each measured point. The model parameters are then fed into a statistical classifier that assigns the bonding layer to one of a set of predefined classes. In this paper, two glass plates are bonded together with construction silicone, and the classifiers are trained to determine if the bonding layer is intact or if it contains regions of air or water. Two different classification methods are evaluated: nominal logistic regression and discriminant analysis. The former is slightly more computationally demanding but, as the results show, it performs better when the model parameters cannot be assumed to belong to a multivariate Gaussian distribution. The performance of the classifiers is evaluated using both simulations and real measurements.