摘要
金属磁记忆检测技术是一种新型的利用铁磁材料内在信息对材料进行检测和评价的无损检测方法,对裂纹类缺陷进行早期检测具有潜在的优势。利用小波包分析技术,对水压试验条件下API5L X70管线钢焊缝中有无焊接裂纹的金属磁记忆信号能量特征进行了分析,确定了焊接裂纹金属磁记忆信号的小波包能量特征,并利用其作为输入特征向量建立了BP(back propagation)神经网络,对焊缝中是否含有裂纹等缺陷进行智能识别。结果表明,利用小波包能量和神经网络技术可以较好的实现焊接裂纹的识别。
Metal magnetic memory (MMM) is one of non-destructive testing method which inspection or evaluation ferromagnetic material used the inner magnetic information. It has been considered as a potential predominance for early diagnosis of crack. The wavelet analysis is employed to extract the MMM signal energy feature with or without welding crack for API 5L X70 pipeline steel at the condition of hydraulic pressure, and then the back propagation (BP) neural network is used to distinguish the weld with crack from free crack that energy feature is used as input eigenvector. The result shows that used the wavelet analysis and BP neural network can recognize the welding crack preferable.
出处
《焊接学报》
EI
CAS
CSCD
北大核心
2008年第3期13-16,共4页
Transactions of The China Welding Institution
基金
国家自然科学基金资助项目(50475113)
博士后科学基金(200700420115)
关键词
焊接裂纹
金属磁记忆
特征提取
神经网络
welding crack
metal magnetic memory
feature extraction
neural network
