Host: The Japan Society of Mechanical Engineers
Name : [in Japanese]
Date : November 16, 2018
The authors proposed a new classification method of AE signals using a machine learning to identify damage types occurring inside CFRP laminates. In this method, the temporal waveforms of AE signals were used as training data to develop the autoencoder. An AE signal generated by a transverse crack and that by delamination can be differently characterized by A0 and S0 modes in AE depending on the different source orientations. The results of the data analysis indicated that the autoencoder has a good performance to identify AE signals with different source orientations. Therefore, the proposed method has a reliability to identify damage types in CFRP laminates.
