针对超声TOFD存在近表面盲区及近表面缺陷自动识别分类的问题，提出了基于超声TOFD直通波及神经网络对近表面孔状缺陷识别分类的方法。在近表面缺陷检测信号的直通波部分选取多个关键点，揭示了各关键点幅度分布与近表面缺陷深度的关系，获得了用于近表面缺陷检测的幅度分布特征值，并将该特征值用于BP神经网络对缺陷识别分类。试验结果表明，该方法能够对铝合金板近表面孔状缺陷进行准确、有效的自动识别分类。

Aiming at the problem of near surface dead zones and defects automatic identification in ultrasonic TOFD technique, a automatic identification technology of near surface defects is proposed based on through wave of ultrasonic TOFD and neural network. Several key points in the part of through wave of testing signal are extracted and relationship between the amplitude distribution of key points and depth of near-surface defect is analyzed. The characteristic numbers of amplitude distribution which can be used to test near-surface defect are obtained. Moreover, the characteristic numbers can be used to defects recognition and classification in BP neural network. The experimental results showed that this technique can be used for accurate and effective classification and automatic identification.