Classification of AE Sources of Carbon/Epoxy Composite Based on SVM

Carbon/epoxy specimens were made and stretched to fracture. In the process, acoustic emission signals were collected and the AE signal parameters were set as the input parameters of the neural network. It is more exact to use SVM network to recognize the different acoustic emission sources than to use the BP neural network. And also, the accuracy of the SVM increases when the number of the training set increases. It is proved that use AE signal parameters and SVM network can recognize the acoustic emission source pattern well.