Estimation of acoustic emission source waveform of fracture using a neural network

The applicability of a neural network to acoustic emission (AE) is presented. It is shown that the shape of the simulated source waveform using piezoelectric ceramics is steplike, similar to that of mode I crack extension, and its rise-time can be varied by the resonance frequency in the thickness direction. The results imply that the simulated source can provide learning waveforms for the network. Actual AE waveforms were also acquired by conducting a tensile test of a chevron-notched graphite specimen. It was demonstrated that the appropriate source waveform associated with mode I crack extension was successfully determined by the network taught with simulated waveforms.