Detection of impact location and magnitude for isotropic plates using neural networks

A neural network-based method of determining the location and magnitude of transverse impact events on isotropic plates is investigated experimentally. Time data from four sensors mounted in the corners of an aluminum plate was processed to provide inputs for two backpropagation neural networks. The first neural network was responsible for detecting impact location. After 1 million iterations of training, this neural network was able to locate impacts with an average RMS error of 1.55 radial centimeters on a 58.5 centimeter by 36.8 centimeter (23 inch by 14.5 inch) fully-clamped plate. The second neural network was responsible for impact magnitude detection. This neural network was able to determine the impact magnitude with an average of 13.8% error.