Application of Artificial Neural Network to Flaw Classification in Ultrasonic Testing

摘要：

Aiming at the difficult question of flaw qualitative analysis during industrial ultrasonic testing, a method of flaw classification based on the combination of wavelet packet transform (WPT) with artificial neural network (ANN) is proposed in this paper. Firstly, WPT is applied to feature extraction of ultrasonic flaw echo signals, and then BP neural network (BPNN), RBF neural network (RBFNN) and probabilistic neural network (PNN) are respectively used to perform flaw classification by means of the features. To validate the method above, some experiments of feature extraction and flaw classification are performed utilizing a series sample of butt girth welds of seamless steel tube with four types of welding flaws, such as crack, stomata, incomplete penetration and slag inclusion. The results show that the accuracy of flaw classification by three kinds of neural networks respectively reached to 91.25％, 92.50％ and 93.75％, and the better classification effect is obtained.

关键词： Ultrasonic testing Flaw classification Artificial neural network Wavelet packet transform

作者: Yuan CHEN Hongwei MA

作者单位: College of Science, Xian University of Science and Technology, Xian, China College of Mechanical Engineering, Xian University of Science and Technology, Xian, China

会议类型: 国际会议

会议名称: 2011 International Conference on Mechatronics and Materials Processing(2011年机电一体化与材料加工国际会议 ICMMP)

会议地点: 广州

会议语种:英文

页码: 1876-1880

在线出版日期: 2011-11-18（万方平台首次上网日期，不代表论文的发表时间）