Experimental Characterization of Ultrasonic Phenomena by a Neural-Like Learning System

This paper describes a novel approach for analyzing ultrasonic signals to permit an experimental determination of the relations between elastic wave phenomena and the properties of a source of sound in a material. It is demonstrated that an adaptive learning system comprising an associative memory can be used to map source and waveform data and vice versa with the auto- and cross-correlation portions of the associative memory. Experiments are described which utilize such an adaptive system, running on a laboratory minicomputer, to process the data from a transient ultrasonic pulse in a plate specimen. In the learning procedure, the system learns from experimental pattern vectors, which are formed from the ultrasonic waveforms and, in this paper, encoded information about the source. The source characteristics are recovered by the recall procedure from detected ultrasonic signals and vice versa. Furthermore, from the discrepancy between the presented and the learned signals, the changes in the wave phenomenon, corresponding, for example, to changes in the boundary conditions of a specimen, can be determined

Novel Approaches for the Ultrasonic NDE of Thick and other Composites

This paper summarizes several recent developments which are facilitating new approaches for both active and passive quantitative ultrasonic measurements in composite materials. These include the development of point sources and point receivers, a theory for analyzing the propagation of transient elastic waves through a bounded, dispersive and attenuative medium, and the development and implementation of appropriate signal processing algorithms. An alternative to these deterministic approaches is a processing scheme based on a simulated intelligent system which processes the signals like a neural network. Examples of applications of these ideas to the NDE of composite materials are shown

Experimental modeling of physical laws

06.20.DK Measurement and error theory – , 02.50.-r Probability theory, stochastic processes, and statistics – , 89.70.+c Information science,