Impact Location and Quantification on a Composite Panel using Neural Networks and a Genetic Algorithm

The problem of impact detection in composite panels using artificial neural networks is addressed in this paper. The data were taken from an experiment in which time dependent strain data were recorded on a network of surface-mounted piezoceramic sensors when the plate was impacted. Neural networks were trained to locate and quantify the impact event when presented with features extracted from the measured data. An important problem for detection systems like this is that of optimal sensor placement; this is solved here by means of a Genetic Algorithm. The study shows that a relatively small number of sensors can be used to detect reliably impacts on a composite plate.