LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Rosli, M. H.; Edwards, R. S. (Rachel S.); Dutton, B. (Ben); Johnson, C. G. (Colin G.); Cattani, P. (2010)
Publisher: American Institute of Physics
Languages: English
Types: Unknown
Subjects: QC, TA
Identifiers:doi:10.1063/1.3362258
Electromagnetic acoustic transducers (EMATs) have been used to generate and detect Rayleigh waves in order to identify surface cracking in aluminium bars and rails. B-scans produced during scans of samples were used to determine the presence of surface defects. Additionally, the differences between signal enhancements due to wave interference at the crack produced by normal (900) and angled cracks in the B-scans were used to classify samples in order to decide an appropriate depth calibration curve for depth estimation. Classification was done using an image processing algorithm that selected the best features for classification, and used these to identify similar patterns in unclassified B-scans.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • D. F. Cannon, K.-O. Edel , S. L. Grassie, K. Sawley, Fatigue Fract Engng Mater Struct 26, 865-887 (2003).
    • R. A. Cottis, Stress Corrosion Cracking,Corrosion and Protection Centre, UMIST, Manchester, 2000.
    • G. Alers, “A History of EMATs”, Review of Quantitative Nondestructive Evaluation, D. O. Thompson and D. E. Chimenti,AIP Conference Proceedings vol. 27, American Insitute of Physics, 2008, 801-808.
    • W.M. Irving, Continuous Casting of Steel, The Institute of Materials, London, 1993, 95-96 S. B. Palmer, S. Dixon, Insight 45, 211-217 (2003).
    • M. Hirao, Hirotsugu Ogi, EMATs for science and industry. Non-contact ultrasonic measurements, Kluwer Academic Publisher, Boston/Dordrecht/London, 2003 R. S. Edwards, S.Dixon, X. Jian, NDT&E International 3, 468-475 (2006).
    • T. Mitchell, Machine Learning, McGraw-Hill, 1997 K. Krawiec, “Visual Learning by Evolutionary Feature Synthesis”, Proceedings of the Twentieth International Conference on Machine Learning, Tom Fawcett and Nina Mishra, AAAI Press, 2003, 376-383 S. Shirakawa, S. Nakayama, T. Nagao, “Genetic Image Network for Image Classification”, Application of Evolutionary Computing: EvoWorkshops 2009, M.
    • Giacobini, A. Brabazon, S. Cagnoni, et al., Springer Berlin/ Heidelberg 5484, 2009, 395- 404 C. G. Johnson, P. Cattani, “Typed Cartesian Genetic Programming for Image Classification”, Proceeding of the 2009 UK Workshop on Computational Intelligence, 2009 G. W. C. Kaye, T. H. Laby, Tables of physical and chemical contstans 16th Ed, Longman, (1995) R.S. Edwards, X. Jian, Y. Fan, S. Dixon, Applied Physics Letters 87, 194104 (2005).
    • X. Jian, S. Dixon, N. Guo, R. S. Edwards, M. Potter, Ultrasonics 44, e1131-e1134 (2006) R.S. Edwards, X. Jian, S. Dixon, J. Phys. D: Appl. Phys. 37, 2291-2297 (2004).
    • I. H. Witten, E. Frank, Data Mining, Morgan Kaufmann, 2nd Edition, 2005
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article