Abstract
A pattern recognition approach was applied to the analysis of ultrasonic echo signals from two classes of aluminum-to-aluminum adhesive bonds. The two classes differed in the surface preparation of the adherends prior to bonding, resulting in different interfacial properties of the joints. These properties have a crucial effect on the long-term adhesive properties of the specimens. Application of advanced signal processing and pattern recognition techniques enabled the classification of the joints according to the surface preparation of the adherends, based on features extracted from the ultrasonic signals. The statistics yielded an upper bound for the probability of mis-classification of the specimens. The sensitivity of certain features, extracted from the ultrasonic signal, to the interfacial characteristics of the specimens is explained by means of the natural frequencies of a joint's components and surface condition of the adherends. This leads to a method for selecting the optimal probe frequency for carrying out the ultrasonic inspection.
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