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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
B Boardman, Fatigue resistance of steels. In: ASM Handbook. Vol. 1, no. Properties and selection: Irons, Steels, and High-Performance Alloys, pp. 673–688 (1990)
F. C. Campbell, Elements of metallurgy and engineering alloys ASM international (2008)
E. Santecchia, A. M. S. Hamouda, F. Musharavati, E. Zalnezhad, M. Cabibbo, M. El Mehtedi, S. Spigarelli, A review on fatigue life prediction methods for metals. In: Advances in Materials Science and Engineering. Vol. 2016 (2016)
F. Barsoum, J. Suleman, A. Korcak, E. Hill, Acoustic emission monitoring and fatigue life prediction in axially loaded notched steel specimens. J. Acoust. Emiss. 27, 40–63 (2009)
M. Mohammad, S. Abdullah, N. Jamaluddin, O. Innayatullah, Acoustic emission evaluation of fatigue life prediction for a carbon steel spec- imen using a statistical-based approach. Mater. Test. 55, 487–495 (2013)
A. Kahirdeh, C. Sauerbrunn, M. Modarres, Acoustic emission entropy as a measure of damage in materials (2015)
G. Drummond, J. Watson, P.P. Acarnley, Acoustic emission from wire ropes during proof load and fatigue testing. NDT E Int. 40, 94–101 (2007)
T.M. Roberts, M. Talebzadeh, Fatigue life prediction based on crack propagation and acoustic emission count rates. J. Construct. Steel Res. 59, 679–694 (2003)
T.M. Morton, R.M. Harrington, J. Bjelectich, Acoustic emissions of fatigue crack growth. Fract. Mech. 5, 691–692 (1973)
M.N. Bassim, S.S. Lawrence, C.D. Liu, Detection of the onset of fatigue crack growth in rail steels using acoustic emission. Fract. Mech. 47(2), 207–214 (1994)
A. Berkovits, D. Fang, Study of fatigue crack characteristics by acoustic emission. Fract. Mech. 51(2), 401–409, 411–416 (1995)
M. Saeedifar, M. Najafabadi, K. Mohammadi, M. Fotouhi, H. Toudeshky, Acoustic emission-based methodology to evaluate delamination crack growth under quasi-static and fatigue loading conditions. In: Journal of nondestructive evaluation, vol. 37 (2018)
D. G. Aggelis, E.Z. Kordatos, T.E. Matikas, Acoustic emission for fatigue damage characterization in metal plates. Mech. Res. Commun. 38, 106–110 (2011)
J. Kaiser, An investigation into the occurrence of noises in tensile tests, or a study of acoustic phenomena in tensile tests. MA Thesis (1950)
Z. Jia, An entropy approach for characterization and assessment of fatigue damage accumulation in Q235 steel based on acoustic emission testing. In: 2017 (2017)
S. F. Wayne, G. Qi, L. Zhang, Data-enabled quantification of aluminum microstructural damage under tensile loading. JOM. 68, 2096–2108 (2016)
R. Moddemeijer, On estimation of entropy and mutual information of continuous distributions. Signal Process. 16, 233–248 (1989)
R. Oliveira, A. Marques, Health monitoring of FRP using acoustic emission and artificial neural networks. Computers and Structures. 86, 367–373 (2007)
S. Huguet, N. Godin, R. Gaertner, L. Salmonand, D. Villard, Use of acoustic emission to identify damage modes in glass fibre reinforced polyester. Compos. Sci. Technol. 62, 1433–1444 (2002)
Bishop, Neural networks for pattern recognition. Oxford University Press (1995)
R. Rojas. Neural networks (Springer, Berlin, 1996)
M. Riedmiller, Rprop - description and implementation details. In: 2004 (2004)
F. Gunther, F. Fritsch, neuralnet: Training of Neural Networks. In: R Foundation for Statistical Computing, pp. 30–38 (2008)
J. Holt, D.J. Goddard, Acoustic emission during the elastic-plastic deformation of low alloy reactor pressure vessel steels I: Uniaxial tension. Mater. Sci. Eng. 44, 251–265 (2008)
J. Lankford, The growth of small fatigue cracks in 7075-T6 aluminum. Fatig. Fract. Eng. Mater. Struct. 5, 233–248 (1982)
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.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
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
- Acoustic emission
- Neural networks