摘要
为提高铝板检测质量,研究利用SH_(0)模态导波对铝板进行全聚焦成像和缺陷检测。首先,采用有限元仿真和实验测试方法获得了全聚焦成像所需的全矩阵数据,并对SH_(0)模态超声导波的传播波速进行了校正。其次,分析了波速校正前后全聚焦成像方法对缺陷定位的误差,对12个不同尺寸缺陷的实验定位结果显示:经过波速校正后的缺陷定位误差(以y方向为例)从40 mm降低至20 mm,在x轴和y轴的定位相对误差小于5%。最后,分析了缺陷几何参数(损失面积、损失体积)对导波特征参数(缺陷回波时域信号能量值、全聚焦成像强度峰值)的影响规律,导波特征参数随缺陷几何参数增加呈现良好的指数型增长规律。研究结论表明:利用SH_(0)模态导波全聚集成像,可以实现铝板中复杂缺陷的准确定位检测及几何参数的定量评估。
SH_(0)mode ultrasonic guided wave(UGW)is used for total focus imaging(TFM)and defect detection of aluminum plate.First,the full matrix data required for TFM are obtained by both finite element simulation and experimental testing methods,and the propagation velocity of SH_(0)mode is corrected.Second,the error of TFM method in defect location before and after wave velocity correction is analyzed.The results about locating 12 defects of different sizes show that the defect location error along y-axis decreases from 40 mm to 20 mm after wave velocity correction.The relative error of defect location is less than 5%along both the x-axis and y-axis.Finally,the influence of geometric parameters(area and volume)of defects on characteristic parameters of UGW(such as the energy of temporal waves reflected from defects,the peak value of intensity in TFM result)is analyzed.The characteristic parameters of UGW demonstrate a good exponential growth trend as the increase of geometric parameters of defect.The research findings proved that the TFM method using SH_(0)mode can realize accurate location and detection of complex defects in aluminum plates and can be applied for quantitative evaluation of geometric parameters of defects.
作者
韩佳琪
王欢
刘尧
赵凌瑄
郑相锋
林正根
刘秀成
HAN Jiaqi;WANG Huan;LIU Yao;ZHAO Lingxuan;ZHENG Xiangfeng;LIN Zhenggen;LIUXiucheng(Faculty of materials and manufacturing,Beijing University of Technology,Beijing 100124,China;China Energy Science and Technology Research Institute Co.,Ltd.,Nanjing 210023,China)
出处
《电力科技与环保》
2022年第6期484-491,共8页
Electric Power Technology and Environmental Protection
基金
国家自然科学基金(12272014)。
关键词
SH_(0)模态
超声导波
全聚焦成像
缺陷检测
定量表征
SH_(0)mode
ultrasonic guided wave
total focus imaging
defect detection
quantitative characterization