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.
Acoustic Emission Source Location in Unidirectional Carbon-Fibre-Reinforced Plastic Plates Using Virtually Trained Artificial Neural Networks
LEONE, Claudio;
2010
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
