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

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 journal › Conference article › peer-review

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 language	English (US)
Pages (from-to)	1303-1306
Number of pages	4
Journal	Proceedings of the IEEE Ultrasonics Symposium
Volume	2
State	Published - 1994
Event	Proceedings of the 1994 IEEE Ultrasonics Symposium. Part 1 (of 3) - Cannes, Fr Duration: Nov 1 1994 → Nov 4 1994

All Science Journal Classification (ASJC) codes

Acoustics and Ultrasonics

Cite this

@article{29bdc88513314295a4d2bdb699ad3df6,

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.",

author = "Mirna Urquidi-Macdonald and Tittmann, {Bernhard R.} and Koopman, {Michael G.}",

year = "1994",

language = "English (US)",

volume = "2",

pages = "1303--1306",

journal = "Proceedings of the IEEE Ultrasonics Symposium",

issn = "1051-0117",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "Proceedings of the 1994 IEEE Ultrasonics Symposium. Part 1 (of 3) ; Conference date: 01-11-1994 Through 04-11-1994",

}

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 journal › Conference article › peer-review

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

Y1 - 1994

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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UR - http://www.scopus.com/inward/citedby.url?scp=0028741955&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:0028741955

SN - 1051-0117

VL - 2

SP - 1303

EP - 1306

JO - Proceedings of the IEEE Ultrasonics Symposium

JF - Proceedings of the IEEE Ultrasonics Symposium

T2 - Proceedings of the 1994 IEEE Ultrasonics Symposium. Part 1 (of 3)

Y2 - 1 November 1994 through 4 November 1994

ER -

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

Abstract

All Science Journal Classification (ASJC) codes

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