Recognition of Welding Flaws Based on Wavelet Packet and PNN

In ultrasonic testing of welding flaws,the qualitative analysis for flaws is a key content,hot and difficult point of ultrasonic NDT and NDE.Aimed at the characteristics of ultrasonic echo-signals of weld flaws,wavelet packet transform was applied to extracting flaw features,and flaws were recognised by using probabilistic neural network (PNN).Then,its recognition result was compared with that of BP and RBF neural network.The experimental results for actual welding flaws show that the recognition accuracy of PNN is high,its training and testing speed is fast,moreover,the reliability is also high.