  In this study, methodology of acoustic emission source localization in
composite materials based on artificial intelligence was presented. Carbon
fiber reinforced plastic was selected for specimen, and acoustic emission
signal were measured using piezoelectric devices. The measured signal was
wavelet-transformed to obtain scalograms, which were used as training data for
the artificial intelligence model. AESLNet(acoustic emission source
localization network), proposed in this study, was constructed convolutional
layers in parallel due to anisotropy of the composited materials. It is
regression model to detect the coordinates of acoustic emission source
location. Hyper-parameter of network has been optimized by Bayesian
optimization. It has been confirmed that network can detect location of
acoustic emission source with an average error of 3.02mm and a resolution of
20mm.
