A neural network model for analyzing vibration waveform of impact sound

Abstract

The method to estimate the feature of the material by the impact sound was proposed ((M. Sakata and H. Ohnabe, 1994). To design the structure of composites taking into account the characteristic of the ceramics, a method was proposed to obtain the elastic moduli and the dumping ratio from the vibration of the material. To estimate their parameters, it is necessary to model the vibration precisely. In previous work, the vibration is analyzed by the fast Fourier transforms. On the other hand, the artificial neural network has been used to model the signal source, recently. The multilayer neural network adaptively models the signal source by error backpropagation. We propose a new neural network model for vibrational analysis of the material. We examined the model by the vibration waveform of actual ceramics composite. Also, the waveform at the high temperature is analyzed from the impact sound waveform of room temperature.