Neural network prediction of aluminum-silicon carbide tensile strength from acoustic emission rise angle data-Reference-Cited by-同舟云学术

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Neural network prediction of aluminum-silicon carbide tensile strength from acoustic emission rise angle data

Published:2018 Issue:3 Volume:46 Page:253-258
ISSN:1451-2092
Container-title:FME Transaction
language:en
Short-container-title:FME Transaction

Author:

Christopher Loo,Sasikumar T.,Santulli C.,Fragassa C.

Publisher

Centre for Evaluation in Education and Science (CEON/CEES)

Subject

Mechanical Engineering,Mechanics of Materials

Link

https://scindeks-clanci.ceon.rs/data/pdf/1451-2092/2018/1451-20921802253C.pdf

Reference25 articles.

1. Qamhia, I.L.-H.E. and El-Hajjar, R.F.: Identification of acoustic emissions from porosity and waviness defects in continuous fiber reinforced composites, ACEM, Vol. 2, No. 1, pp. 37-50, 2013.;

2. Woo, S.C., Choi, N.S. and Cho, N.: Characterization of the fracture process of notched glass fiber/aluminum hybrid laminates by acoustic emission, Computer Science and Technology, Vol. 68, pp. 1521-1530, 2008.;

3. Petrašinović, N., Petrašinović, D., Rašuo B., Milković D.: Aircraft Duraluminum Wing Spar Fatigue Testing, FME Transactions, Vol. 45, No. 4, pp 531-536, 2017.;

4. Kastratović, G., Vidanović, N., Grbović, A., Rašuo, B.: Approximate determination of stress intensity factor for multiple surface cracks, FME Transactions, Vol. 46, No. 1, pp 41-47, 2018.;

5. Grbović, A., Rasuo, B.: FEM based fatigue crack growth predictions for spar of light aircraft under variable amplitude loading, Engineering Failure Analysis, Volume 26, December 2012, Elsevier Ltd., pp. 50-64.;

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