Development of an artificial neural network processing technique for the analysis of damage evolution in pultruded composites with acoustic emission

Acoustic Emission (AE) is a promising technique for the damage detection and the real-time structural monitoring of composite lightweight structures; however data interpretation and discrimination among failure modes from AE data is difficult to be carried out without proper data processing techniques. In this paper, a neural-network based classification of AE signals from tensile tests of pultruded glass-fiber specimens is proposed. A self-organizing map is trained with AE data from one specimen; then the map is clustered with the k-means algorithm. The optimal number of clusters is chosen by a voting procedure that takes into account a number of quality indexes; then the clustered neural network is used to classify AE data from other specimen. Results have shown that the classifier built from a smooth specimen was able to correctly classify other specimens with the same and with a different material layup, and is capable of recognizing signals from notched specimens, thus providing interesting and encouraging indications in view of the application on real structures. (C) 2013 Elsevier Ltd. All rights reserved.