薄壁小直径柱/板扩散焊界面超声信号特征分析与缺陷智能识别

Ultrasonic Signal Characteristic Analysis and Intelligent Defect Identification of Thin-wall Small-diameter Column/Plate Diffusion Welding Interface

为解决薄壁小直径柱/板扩散焊质量超声检测时波幅信号中缺陷与界面信号混叠,难以判断焊接接头是否存在微小缺陷的问题,采用基于粒子群优化的支持向量机技术(PSO-SVM),以不同界面类型的多特征参量为输入,对扩散焊界面进行缺陷识别。首先,使用水浸超声检测系统采集试样的C扫描数据,以金相试验得到的焊接截面为参照,运用快速傅里叶变换、经验模态分解等方法提取无缺陷、焊瘤、未焊合3种界面类型的时域、频域特征值;然后使用主成分分析法(PCA)对多特征参量进行融合得到融合特征值;最后输入到PSO-SVM模型中进行缺陷智能识别,并且与未经过多特征融合的预测结果进行对比分析。结果表明:经过PCA处理后,测试结果中3种类型界面的识别准确率为100%,比未经过PCA处理的测试结果准确率提高4.5%。

To address the problem of aliasing in amplitude signals from the defects and interface during ultrasonic inspection of thinwall and small-diameter column/plate diffusion welding quality,which complicates the determination of small defects in the welded joint,a support vector machine based on particle swarm optimization(PSO-SVM)was used to identify the defects at the diffusion welding interface with multi-feature parameters of different interface types as input.Firstly,the C-scan data from the sample was collected using a water immersion ultrasonic detection system.Taking the welding cross-section obtained from metallographic testing as a reference,the time and frequency domain characteristic values for three types of interfaces,i.e.defect-free,weld bead,and lack of fusion were extracted by using fast Fourier transform and empirical mode decomposition methods.Afterwards,principal component analysis(PCA)was used to integrate the multi-feature parameters to obtain the fusion eigenvalues.Finally,the defects were inputed into the PSO-SVM model for intelligent identification,and the prediction results were compared with those without extensive feature fusion.The results show that after principal component analysis processing,the recognition accuracy of the three types of interfaces in the test results is 100%,demonstrating 4.5%improvement over the accuracy of the test results without PCA processing.