Access to master’s theses is restricted to current ERAU students, faculty, and staff.
Date of Award
Thesis - Open Access
Master of Science in Aerospace Engineering
Dr. Eric v. K. Hill
Dr. Frank J. Radosta
Dr. James G. Ladesic
The research encompassed within this paper deals with the analysis and classification of fatigue cracks in aircraft structures. The particular structure that was examined was the vertical tail section of a Cessna T-303 Crusader aircraft. The analysis was performed using the nondestructive evaluation technique known as acoustic emission (AE), as well as the artificial intelligence of neural networks. Data were taken in a controlled laboratory environment as well as in a flying testbed aboard the aircraft.
The first part of the research involved the analysis of a typical aircraft structure in a controlled laboratory environment. This support structure was fabricated from 7075-T6 aluminum, which is common in aircraft structures. Two different methods were used to fatigue the support, an MTS tensile test machine and a shaker table. Extensive AE data were taken throughout the laboratory tests in order to provide a known reference for the identification of fatigue cracks.
The acoustic emission data derived from the laboratory tests were thoroughly examined and sorted into three distinct mechanisms: fatigue cracking, plastic deformation, and mechanical noise. The AE parameters associated with these mechanisms were in turn used to train a neural network. The neural network used was the Kohonen self-organizing map, as it is an excellent choice for the purpose of classification.
Once the neural network was trained, it was possible to proceed to the second stage of the research. A support structure, identical to the one used in the laboratory tests, was installed in the vertical tail of the T-303 aircraft. Acoustic emission data were gathered during all aspects of aircraft maneuvers, from the initial taxiing and takeoff to the final approach and landing, including rolls and Dutch rolls.
The AE parameters recorded from the in-flight tests were processed using the neural network trained in the first part of the research. Thus, the data were classified as being indicative of fatigue cracking, plastic deformation, or rubbing. These mechanisms were then analyzed with respect to the particular maneuver performed to further understand the stresses associated with different maneuvers. As a result of the ability to classify fatigue cracks, it is possible to develop a monitoring system for aircraft to determine the existence of fatigue cracks before they grow to the point where they become dangerous.
Scholarly Commons Citation
Rovik, Christopher Lee, "Classification of In-Flight Fatigue Cracks in Aircraft Structures using Acoustic Emission and Neural Networks" (1998). Theses - Daytona Beach. 231.