Quality evaluation by acoustic emission aided with a fuzzy neural network

The acoustic emission signals produced when an iron workpiece is loaded and unloaded are used as input characteristics of pattern recognition with a neural network. The fuzzy subordinate degree is calculated for network input and output, and the connection weight factors are optimized through BP learning of steepest fall. Training and classification of workpiece sample group are successfully fulfilled.