Analysis of Fatigue Damage Information Obtained from Acoustic Emission Data

This study examines acoustic emission data obtained during intermittent static tension loading of progressively fatigued 4340 steel and 7075-T651 aluminum specimens with the aim of inferring fatigue damage information from static tension testing. Acoustic emission data were collected using a novel loading procedure based on the Dunegan corollary. Results from 4340 steel testing showed a moderate correlation between total acoustic emission energy parameter and the number of cyclic loading cycles. Results from 7075-T651 aluminum testing showed a moderate correlation between the information entropy parameter and loading cycles. A supervised neural network was assessed to be 54.0 ± 19.1% accurate in predicting cyclic loading cycles for 4340 steel specimens and 52.0 ± 19.2% accurate for 7075-T651 aluminum specimens. Overall, results showed that limited but potentially useful fatigue damage information from 4340 steel or 7075-T651 aluminum is contained within acoustic emission signals collected during elastic tension loading.