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    李萍, 倪军, 李喜孟, 王来. 神经网络在20钢时效组织状态无损识别中的应用[J]. 机械工程材料, 2006, 30(12): 33-35.
    引用本文: 李萍, 倪军, 李喜孟, 王来. 神经网络在20钢时效组织状态无损识别中的应用[J]. 机械工程材料, 2006, 30(12): 33-35.
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    LI Ping, NI Jun, LI Xi-meng, WANG Lai. The Application of Artificial Neural Networks to Non-destructive Identification of Microstructure of 20 Steel[J]. Materials and Mechanical Engineering, 2006, 30(12): 33-35.
    Citation: LI Ping, NI Jun, LI Xi-meng, WANG Lai. The Application of Artificial Neural Networks to Non-destructive Identification of Microstructure of 20 Steel[J]. Materials and Mechanical Engineering, 2006, 30(12): 33-35.
    shu

    神经网络在20钢时效组织状态无损识别中的应用

    The Application of Artificial Neural Networks to Non-destructive Identification of Microstructure of 20 Steel

      摘要
    • 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.

       

      Abstract: 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.

       

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