摘要
超声无损检测已被广泛用来检测材料内部的缺陷,然而对缺陷性质的识别始终是检测的难点,为此研究了一种基于超声信号和图像融合的焊缝缺陷识别新方法.该方法充分利用检测数据,通过对缺陷回波信号特征与缺陷形态特征的数据融合,实现了焊缝缺陷的有效识别.利用自主研制的超声成像手动检测系统对含有气孔、夹渣、裂纹、未焊透和未熔合五类典型焊接缺陷的焊件进行了检测,分别提取缺陷的超声回波信号特征和缺陷图像的形态特征,构建神经网络实现超声信号和图像特征的数据融合.结果表明,该方法实现了多类缺陷的识别,提高了缺陷识别率,有助于焊缝质量评定.
Ultrasonic testing is widely applied to detect the inner flaws of materials, but it is still difficult to recognize the flaw properties. In this paper, a new method for flaw recognition based on feature fusion of ultrasonic signal and image was proposed. The detection data was used to identify the weld flaw by the data fusion of ultrasonic signal feature and morphological feature. The welds containing defects such as hole, slag, crack, lack of penetration and lack of fusion were inspected with the manual ultrasonic testing system. Then the ultrasonic signal features of flaw echo and morphological features of flaw image were extracted respectively. Finally, BP neural network was used to carry out the data fusion of these features. The results show that the multi-class flaws could be identified effectively, and the recognition rate of weld flaws was improved by this method.
出处
《焊接学报》
EI
CAS
CSCD
北大核心
2013年第4期53-56,115-116,共4页
Transactions of The China Welding Institution
基金
国家自然基金资助项目(51175113
51105033)
国际合作资助项目(2007DFR70070)
关键词
超声检测
缺陷识别
数据融合
神经网络
ultrasonic testing
flaw recognition
data fusion
neural network
