摘要
通过提取试样超声回波信号的小波系数作为表征组织状态的特征参量,探讨了 BP 神经网络在20钢球化组织状态和30Mn2SiV 结构钢轧制组织状态无损识别中的应用。结果表明:构造 BP 型神经网络可实现对20钢和30Mn2SiV 结构钢组织状态的无损识别与分类,平均识别率可达到86.6%和88.3%,为钢铁材料组织分析提供了一种先进而有效的新方法。
The wavelet coefficient of ultrasonic signal was extracted as the characteristic parameter of microstructure. The application of BP neural networks to the non-destructive identification of spheroidized microstructure of 20 steel and rolled microstructure of 30Mn2SiV was discussed. The BP neural networks is available for non-destructive identification and classification of 20 steel spheroidized microstructure as well as 30Mn2SiV rolled microstructure. The average reliability is 86. 6% and 88. 3% respectively. It provides an advanced and valid method for analyzing the microstructure of steel
出处
《机械工程材料》
CAS
CSCD
北大核心
2006年第12期33-35,39,共4页
Materials For Mechanical Engineering
关键词
BP神经网络
无损识别
组织状态
BP neural network
non-destructive identification
microstructure
