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Date of Award

6-2006

Document Type

Thesis - Open Access

Degree Name

Master of Science in Aerospace Engineering

Department

Aerospace Engineering

Committee Chair

Dr. Eric v. K. Hill

Committee Member

Dr. Eric R. Perrell

Committee Member

Dr. Seenithamby Sivasundaram

Abstract

Rapid editing of acoustic emission (AE) data is required in order to make real-time acoustic emission flaw growth systems a viable testing method for materials and setups that contain noisy signals. It was hypothesized that extracting major frequency components from the acoustic emission signal would therefore provide a representative acoustic signature of the major waveforms occurring due to defect growth This research has verified that the aforementioned filtering technique does, in fact, extract a representative signal from the composite and metal specimens utilized herein These findings were verified both through visual analysis of the data as well as the low error occurrence in backpropagation neural network predictions and good classification in self-organizing map type neural networks applied to the testing data.

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