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JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE)
    
Defect identification method for aluminum plate based on electromagnetic acoustic technique
WANG Kai-can, XU Ji, ZHAI Guo-fu
Military Apparatus Research Institute, Harbin Institute of Technology, Harbin 150001, China
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Abstract  
Currently, defect types were desired to obtain in order to guide the improvement of the production process. A defect identification method for the aluminum plate based on electromagnetic acoustic technique was proposed. Artificial defects such as flat and round bottom holes were used to represent the real defects in aluminum plates and multiple sets of echo signal samples were obtained by the bulk wave electromagnetic acoustic transducer (EMAT). The adaptive neighboring coefficients method based on wavelet was adopted, which effectively increased the signal noise rate (SNR). Multiply feature extraction methods were proposed to extract 45 kinds of signal features in the time domain, frequency domain and time-frequency domain. Optimum feature vector was obtained through the class distance separation criterion and
sequential floating forward selection (SFFS) methods, which effectively deduced the dimension of the feature vector. The k-fold cross validation method was applied to choose the classifier's parameters. The experiment shows that the design of the support vector machine (SVM) classifier can identify the flat and round bottom holes effectively. The recognition accuracy rate is 96.7%.


Published: 01 November 2014
CLC:  TB 55  
  TP 391  
Cite this article:

WANG Kai-can, XU Ji, ZHAI Guo-fu. Defect identification method for aluminum plate based on electromagnetic acoustic technique. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2014, 48(11): 2031-2038.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2014.11.017     OR     http://www.zjujournals.com/eng/Y2014/V48/I11/2031


基于电磁超声的铝板缺陷识别方法

针对铝板中典型缺陷类型,提出基于电磁超声的铝板缺陷识别方法.通过平底孔和圆底孔人工缺陷模拟实际缺陷,使用电磁超声体波获得多组缺陷回波信号样本,采用小波邻域自适应阈值消噪方法,提升信号信噪比.从时域、频域和时频域提出多种信号特征方法,提取45种信号特征.以基于类内类间距离的类别可分性判据为评价指标,通过序列浮动前向选择算法(SFFS)搜索得到最优特征向量用于缺陷识别.采用k-折交叉确认方法确定支持向量机分类器的最优参数.试验结果表明,设计的分类器能够有效地识别文中铝板内的平底孔和圆底孔缺陷,识别正确率达到了96.7%.
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