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Title: Acoustic Emission Source Location in Unidirectional Carbon-Fibre-Reinforced Plastic Plates Using Virtually Trained Artificial Neural Networks

Abstract

Acoustic emission source location in a unidirectional carbon-fibre-reinforced plastic plate was attempted employing Artificial Neural Network (ANN) technology. The acoustic emission events were produced by a lead break, and the response wave received by piezoelectric sensors, type VS150-M resonant at 150 kHz. The waves were detected by a Vallen AMSY4 eight-channel instrumentation. The time of arrival, determined through the conventional threshold crossing technique, was used to measure the dependence of wave velocity on fibre orientation. A simple empirical formula, relying on classical lamination and suggested by wave propagation theory, was able to accurately model the experimental trend. Based on the formula, virtual training and testing data sets were generated for the case of a plate monitored by three transducers, and adopted to select two potentially effective ANN architectures. For final validation, experimental tests were carried out, positioning the source at predetermined points evenly distributed within the plate area. A very satisfactory correlation was found between the actual source locations and the ANN predictions.

Authors:
; ; ;  [1]
  1. Department of Materials and Production Engineering, University of Naples 'Federico II', Piazzale Tecchio, 80, 80125, Naples (Italy)
Publication Date:
OSTI Identifier:
21377987
Resource Type:
Journal Article
Journal Name:
AIP Conference Proceedings
Additional Journal Information:
Journal Volume: 1255; Journal Issue: 1; Conference: 5. international conference on times of polymers (TOP) and composites, Ischia (Italy), 20-23 Jun 2010; Other Information: DOI: 10.1063/1.3455565; (c) 2010 American Institute of Physics; Journal ID: ISSN 0094-243X
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; CARBON FIBERS; COMPOSITE MATERIALS; KHZ RANGE 100-1000; NEURAL NETWORKS; PIEZOELECTRICITY; PLATES; POSITIONING; REINFORCED PLASTICS; SENSORS; TRANSDUCERS; VALIDATION; WAVE PROPAGATION; ELECTRICITY; FIBERS; FREQUENCY RANGE; KHZ RANGE; MATERIALS; ORGANIC COMPOUNDS; ORGANIC POLYMERS; PETROCHEMICALS; PETROLEUM PRODUCTS; PLASTICS; POLYMERS; REINFORCED MATERIALS; SYNTHETIC MATERIALS; TESTING

Citation Formats

Caprino, G, Lopresto, V, Leone, C, and Papa, I. Acoustic Emission Source Location in Unidirectional Carbon-Fibre-Reinforced Plastic Plates Using Virtually Trained Artificial Neural Networks. United States: N. p., 2010. Web. doi:10.1063/1.3455565.
Caprino, G, Lopresto, V, Leone, C, & Papa, I. Acoustic Emission Source Location in Unidirectional Carbon-Fibre-Reinforced Plastic Plates Using Virtually Trained Artificial Neural Networks. United States. doi:10.1063/1.3455565.
Caprino, G, Lopresto, V, Leone, C, and Papa, I. Wed . "Acoustic Emission Source Location in Unidirectional Carbon-Fibre-Reinforced Plastic Plates Using Virtually Trained Artificial Neural Networks". United States. doi:10.1063/1.3455565.
@article{osti_21377987,
title = {Acoustic Emission Source Location in Unidirectional Carbon-Fibre-Reinforced Plastic Plates Using Virtually Trained Artificial Neural Networks},
author = {Caprino, G and Lopresto, V and Leone, C and Papa, I},
abstractNote = {Acoustic emission source location in a unidirectional carbon-fibre-reinforced plastic plate was attempted employing Artificial Neural Network (ANN) technology. The acoustic emission events were produced by a lead break, and the response wave received by piezoelectric sensors, type VS150-M resonant at 150 kHz. The waves were detected by a Vallen AMSY4 eight-channel instrumentation. The time of arrival, determined through the conventional threshold crossing technique, was used to measure the dependence of wave velocity on fibre orientation. A simple empirical formula, relying on classical lamination and suggested by wave propagation theory, was able to accurately model the experimental trend. Based on the formula, virtual training and testing data sets were generated for the case of a plate monitored by three transducers, and adopted to select two potentially effective ANN architectures. For final validation, experimental tests were carried out, positioning the source at predetermined points evenly distributed within the plate area. A very satisfactory correlation was found between the actual source locations and the ANN predictions.},
doi = {10.1063/1.3455565},
journal = {AIP Conference Proceedings},
issn = {0094-243X},
number = 1,
volume = 1255,
place = {United States},
year = {2010},
month = {6}
}