基于神经网络的超声检测缺陷表征

提出了一种基于神经网络的缺陷表征方法．该方法采用Fischer线性判别分析对表征缺陷的时域信号的波形参数进行选择，并将这些参数作为神经网络的输入对智能缺陷表征系统进行训练，用概率神经网络和BP神经网络分别对缺陷的类型和大小进行识别．对135种人造焊接缺陷(裂纹、夹杂和气孔)的试验结果表明，文中方法对辨识缺陷表征信息和提高缺陷识别率非常有效．

Neural Network-Based Characterization of Flaws Tested by Ultrasonic

This paper proposes a method for flaw characterization on the basis of neural networks. In this me- thod, a selection of the shape parameters defining the pulse-echo envelope reflected from a flaw is carried out by Fischer linear discriminant analysis. The selected parameters are then used as the inputs of neural networks to train the proposed intelligent flaw characterization system. Moreover, probabilistic neural networks and back propagation neural networks are respectively adopted to determine the sizes and numbers of flaws. Experimental results for 135 systematic weld flaws (crack, slag and porosity) indicate that the proposed method is effective in the flaw characterization with great classification rate.