电阻点焊缺陷超声信号多特征优化试验研究  

Experimental Study on Optimization of Multi-Features of Ultrasonic Signals of Resistance Spot Welding Defects

作　　者：黄宏 吴玮[1,2] 李坤航 杨宏睿 殷相杰 蒋启明 邓展鹰 HUANG Hong;WU Wei;LI Kunhang;YANG Hongrui;YIN Xiangjie;JIANG Qiming;DENG Zhanying(Chongqing University of Technology,Chongqing 400054,China;Chongqing Engineering Research Center for Special Welding Materials and Technology,Chongqing 400054,China)

机构地区：[1]重庆理工大学,重庆400054 [2]重庆市特种焊接材料与技术高校工程研究中心,重庆400054

出　　处：《航空制造技术》2021年第21期43-50,共8页Aeronautical Manufacturing Technology

基　　金：重庆理工大学研究生科研创新项目(clgycx 20202003)。

摘　　要：针对铝合金点焊接头常见的气孔、未熔合、无缺陷等超声回波信号,首先采用经验模态分析进行降噪与重构,利用统计学方法分别提取时域、频域中多尺度特征值,分析不同缺陷的特征值变化规律。其次采用主成分分析法与线性判别分析法对特征值进行优化,获得缺陷回波信号的主元特征。最后以主元特征做BP神经网络的输入,对缺陷信号进行识别。试验结果表明,两种降维方法构造后的特征量与未经过降维的特征量相比具有更好的分类结果,其中PCA作用更优,有效提高了BP神经网络的缺陷识别准确率。For the common ultrasonic echo signals of porosity,unfused and defect-free aluminum alloy spot welded joints,noise reduction and reconstruction were first performed with empirical mode decomposition(EMD)analysis,and multi-scale eigenvalues in the time and frequency domains were extracted using statistical methods to analyze the eigenvalue variation patterns of different defects.The principal component analysis(PCA)and linear discriminant analysis(LDA)are used to optimize the feature values and obtain the principal element features of the defective echo signal,finally the principal element features are used as the input of the BP neural network to identify the defect signal.The experimental results show that the feature quantities constructed by the two dimensionality reduction methods have better classification results compared with those without dimensionality reduction,with PCA acting better and effectively improving the defect recognition accuracy of BP neural networks.

关 键 词：电阻点焊 超声检测 特征提取 经验模态分解 主成分分析 

分 类 号：TG441.7[金属学及工艺—焊接] TB553[理学—物理]