Date of Award
Spring 5-2004
Document Type
Thesis - Open Access
Degree Name
Master of Aerospace Engineering
Department
Aerospace Engineering
Committee Chair
Eric v. K. Hill
Committee Member
Frank J. Radosta
Committee Member
Jean-Michel Dhainaut
Abstract
The objective of this research was to classify acoustic emission (AE) -data associated with fatigue cracks in aluminum fatigue specimens and to use early cycle life AE data to predict failure in such members. An AE data acquisition system coupled with a Kohonen self organizing map and a back propagation neural network were used to perform the analysis. AE waveforms were recorded during fatigue cycling of twenty-four notched 7075-T6 aluminum specimens using broad-band piezoelectric transducers. A Kohonen self organizing map was used to classify the AE flaw growth signals. The signals were classified into three categories based on their AE parameters: plastic deformation, plane strain fracture and mixed mode (plane strain and plane stress) fracture.
Acoustic emission amplitude data from the twenty-four low cycle fatigue tests were used to train and test a back propagation neural network for prediction of cycles to failure. The input data consisted of amplitude frequency histograms (30-100 dB) and the actual cycle lives. The output was the predicted cycles to failure or fatigue life. A network capable of predicting cycles to failure with a worst case error of- 9.30% was the final result.
Scholarly Commons Citation
Ibekwe, Emeka Chigozie, "Neural Network Fatigue Life Prediction in 7075-T6 Aluminum from Acoustic Emission Data" (2004). Master's Theses - Daytona Beach. 276.
https://commons.erau.edu/db-theses/276
