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Date of Award
Thesis - Open Access
Master of Aerospace Engineering
Dr. Eric v. K. Hill
Dr. Frank J. Radosta
Dr. Jean-Michel Dhainaut
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). Theses - Daytona Beach. 276.