基于神经网络的金属与非金属材料粘接质量定量检测

Quantitative Detection of Adhesive Bond Quality Between Metallic and Nonmetallic Components Based on the Neural Networks

根据超声无损检测中偏重于定性研究的现状,提出一种基于神经网络的金属与非金属材料粘接质量的定量检测方法.在深入分析粘接界面超声检测回波信号特点的基础上,通过小波变换得到能反映粘接质量的特征值,使用Elman神经网络对粘接质量进行了定量分类识别.实验结果表明,该特征提取和神经网络分类算法对于粘接质量定量识别的准确度很高.

The most studies on ultrasonic nondestructive testing detection laid particular were qualitative,so the authors put forward a quantitative detection method of adhesive bond quality between metallic and nonmetallic components based on the neural networks.After considering the characteristic of the echo signals of ultrasonic detection to adhesive interface,the authors gained the characteristics that can reflect the adhesive bond quality by using the wavelet transformation.Then,the Elman neural network was use...