Acoustic emission characterization of failure modes in composites with ANN

Chandrashekhar Bhat, M. R. Bhat, C. R.L. Murthy

Research output: Contribution to journalArticlepeer-review

46 Citations (Scopus)


Noise suppression in acoustic emission data was attempted by developing and using artificial neural networks (ANN) and with the long-term objective of in-flight monitoring. In-flight experiments conducted earlier and the noise characteristics outlined therein were taken as basis for their simulation in the laboratory. Simulated noise sources were classified through both supervised and a combination of un-supervised and supervised training of ANN. AE signals were generated by fatigue spectrum load tests on CFRP specimens and their failure modes were characterized. Finally, simulated noise and the actual signals were mixed and re-classified into their respective classes. The results obtained are encouraging and the methods and procedures adopted confirm the feasibility of the approach for field applications.

Original languageEnglish
Pages (from-to)213-220
Number of pages8
JournalComposite Structures
Issue number3
Publication statusPublished - 01-01-2003

All Science Journal Classification (ASJC) codes

  • Ceramics and Composites
  • Civil and Structural Engineering


Dive into the research topics of 'Acoustic emission characterization of failure modes in composites with ANN'. Together they form a unique fingerprint.

Cite this