Prediction of Fatigue Life in 7075-T6 Aluminum from Neural Network Analysis of Acoustic Emission Data

<p>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%.</p>

Name thesis:degree_name

Master of Science in Aerospace Engineering

Level thesis:degree_level

Thesis - Open Access

Discipline thesis:degree_discipline

Aerospace Engineering

Year

2007

Author dc:creator

• Spivey, Nicholas S.

Contributors dc:contributor

• Eric v. K. Hill
• Frank J. Radosta
• Seth-Andrew T. Dion

• fatigue
• aluminum
• neural network
• acoustic emission
• Aerospace Engineering
• Structures and Materials

Repository record dc:identifier

https://commons.erau.edu/db-theses/187

OAI identifier oai:identifier

oai:commons.erau.edu:db-theses-1270