Automated Defect Recognition on X-ray Radiographs of Solid Propellant Using Deep Learning Based on Convolutional Neural Networks-Reference-Cited by-同舟云学术

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Automated Defect Recognition on X-ray Radiographs of Solid Propellant Using Deep Learning Based on Convolutional Neural Networks

Published:2021-01-28 Issue:1 Volume:40 Page:
ISSN:0195-9298
Container-title:Journal of Nondestructive Evaluation
language:en
Short-container-title:J Nondestruct Eval

Author:

Gamdha Dhruv^ORCID,Unnikrishnakurup Sreedhar,Rose K. J. Jyothir,Surekha M.,Purushothaman Padma,Ghose Bikash,Balasubramaniam Krishnan

Funder

Defence Research and Development Organisation

Publisher

Springer Science and Business Media LLC

Subject

Mechanical Engineering,Mechanics of Materials

Link

http://link.springer.com/content/pdf/10.1007/s10921-021-00750-4.pdf

Reference38 articles.

1. Brooks, W.: Solid propellant grain design and internal ballistics, vol 8076. National Aeronautics and Space Administration (1972)

2. Davenas, A.: Solid Rocket Propulsion Technology. Newnes, Oxford (2012)

3. Halmshaw, R.: Industrial Radiology: Theory and Practice, vol. 21. Chapman Hall, London (2012)

4. Ghose, B., Kankane, D.: Estimation of location of defects in propellant grain by X-ray radiography. NDT E Int. 41(2), 125–128 (2008)

5. Deprins, E.: Digital radiography in ndt applications. CINDE J. 25(4), 14–16 (2004)

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