Date of Award
Thesis - Open Access
Master of Science in Aerospace Engineering
Dr. Eric v. K. Hill
Dr. Frank J. Radosta
Mr. Seth-Andrew T. Dion
Through the use of an acoustic emission (AE) data acquisition system, a Kohonen self-organizing map, and a back-propagation neural network, AE data from 7075-T6 aluminum specimens were used to classify failure mechanisms and predict the number of fatigue cycles to failure. AE waveforms were captured from 40 notched tensile specimens during the low-cycle fatiguing process. A Kohonen self-organizing map and initial data filters were used to classify the data into two distinct failure mechanisms, plane strain and plane stress fracture, plus a third less prevalent mechanism. These results were employed to construct a back-propagation neural network to predict the number of cycles to failure from the first 250 cycles of AE data. Due to a scarcity of AE data, optimal prediction results were not obtained on all 40 specimens. However, a smaller set of 18 specimens, 9 for training and 9 for testing, produced a worst case prediction error of-13.9%.
Scholarly Commons Citation
Spivey, Nicholas S., "Prediction of Fatigue Life in 7075-T6 Aluminum from Neural Network Analysis of Acoustic Emission Data" (2007). Master's Theses - Daytona Beach. 187.