Artificial neural networks to interpret acoustic emission signals to detect early delamination during carbonization of pre-fabricated components of carbon-carbon composite material

Mirna Urquidi-Macdonald, Bernhard R. Tittmann, Michael G. Koopman

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The applicability of Artificial Neural Network Systems (ANN) to identify the features in the acoustic emission (AE) signals that can be used to predict delamination defects is investigated. Characteristic features in the acoustic emission signals are identified through extensive review of the available data and development of suitable ANN. Results of six carbonization runs including those for components with and without delamination are presented. In general, the results of the preliminary investigation are very encouraging and demonstrate the benefit of combined AE and ANN techniques.

Original languageEnglish (US)
Pages (from-to)1303-1306
Number of pages4
JournalProceedings of the IEEE Ultrasonics Symposium
Volume2
StatePublished - 1994

Fingerprint

carbon-carbon composites
Carbon carbon composites
carbonization
Carbonization
acoustic emission
Acoustic emissions
Delamination
Neural networks
composite materials
Composite materials
Defects
defects

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

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title = "Artificial neural networks to interpret acoustic emission signals to detect early delamination during carbonization of pre-fabricated components of carbon-carbon composite material",
abstract = "The applicability of Artificial Neural Network Systems (ANN) to identify the features in the acoustic emission (AE) signals that can be used to predict delamination defects is investigated. Characteristic features in the acoustic emission signals are identified through extensive review of the available data and development of suitable ANN. Results of six carbonization runs including those for components with and without delamination are presented. In general, the results of the preliminary investigation are very encouraging and demonstrate the benefit of combined AE and ANN techniques.",
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year = "1994",
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journal = "Proceedings of the IEEE Ultrasonics Symposium",
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publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Artificial neural networks to interpret acoustic emission signals to detect early delamination during carbonization of pre-fabricated components of carbon-carbon composite material. / Urquidi-Macdonald, Mirna; Tittmann, Bernhard R.; Koopman, Michael G.

In: Proceedings of the IEEE Ultrasonics Symposium, Vol. 2, 1994, p. 1303-1306.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Artificial neural networks to interpret acoustic emission signals to detect early delamination during carbonization of pre-fabricated components of carbon-carbon composite material

AU - Urquidi-Macdonald, Mirna

AU - Tittmann, Bernhard R.

AU - Koopman, Michael G.

PY - 1994

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N2 - The applicability of Artificial Neural Network Systems (ANN) to identify the features in the acoustic emission (AE) signals that can be used to predict delamination defects is investigated. Characteristic features in the acoustic emission signals are identified through extensive review of the available data and development of suitable ANN. Results of six carbonization runs including those for components with and without delamination are presented. In general, the results of the preliminary investigation are very encouraging and demonstrate the benefit of combined AE and ANN techniques.

AB - The applicability of Artificial Neural Network Systems (ANN) to identify the features in the acoustic emission (AE) signals that can be used to predict delamination defects is investigated. Characteristic features in the acoustic emission signals are identified through extensive review of the available data and development of suitable ANN. Results of six carbonization runs including those for components with and without delamination are presented. In general, the results of the preliminary investigation are very encouraging and demonstrate the benefit of combined AE and ANN techniques.

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EP - 1306

JO - Proceedings of the IEEE Ultrasonics Symposium

JF - Proceedings of the IEEE Ultrasonics Symposium

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