Date of Award

Spring 2010

Access Type

Thesis - Open Access

Degree Name

Master of Science in Aerospace Engineering

Department

Aerospace Engineering

Committee Chair

Eric v. K. Hill

First Committee Member

Yi Zhao

Second Committee Member

Fady F. Barsoum

Abstract

The purpose of this research was to replicate the fatigue cracking that occurs in aircraft placed under loads from cyclical compression and decompression. As a fatigue crack grows, it releases energy in the form of acoustic emissions. These emissions are transmitted through the structure in waves, which can be recorded using acoustic emission (AE) transducers. This research employed a pressure vessel constructed out of aluminum and placed under cyclical loads at 1 Hz in order to simulate the loads placed on an aircraft fuselage in flight. The AE signals were recorded by four resonant AE transducers. These were placed on the pressure vessel such that it was possible to determine the location of each AE signal. These signals were then classified using a Kohonen self organizing map (SOM) neural network. By using proper data filtering before the SOM was run and using the correct classification parameters, it was shown that this is a highly accurate method of classifying AE waveforms from fatigue crack growth. This initial classification was done using AE waveform quantification parameters. The method was then validated by using both source location and then examining the waveforms in order to ensure that the waveforms classified into each category were the expected waveform types associated with each of the AE sources. Thus, acoustic emission nondestructive testing (NDT), in combination with a SOM neural network, proved to be an excellent means of fatigue crack growth monitoring in a simulated aluminum aircraft structure.

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