Analysis of Fatigue Damage Information Obtained from Acoustic Emission Data

Abstract

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.

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Acknowledgments

Thank you to Dr. Wayne and Dr. Qi for their support and advisement during my research. Thank you to Jason and the University of Memphis faculty for their significant aid during this process.

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Correspondence to Rowan Lumb.

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Lumb, R., Wayne, S. & Qi, G. Analysis of Fatigue Damage Information Obtained from Acoustic Emission Data. Data-Enabled Discov. Appl. 4, 1 (2020). https://doi.org/10.1007/s41688-020-0036-7

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Keywords

  • Acoustic emission
  • Neural networks
  • Fatigue