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| 高强度管线钢焊接接头韧性参数CVN的神经网络预测系统 |
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白世武1, 童莉葛2, 刘方明3, 王立2
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1.天津大学材料科学与工程学院, 天津 300072;2.北京科技大学机械工程学院, 北京 100083;3.中国石油天然气管道科学研究院, 河北 廊坊 065000
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| 摘要: |
| 使用VC++6.0建立了多层BP人工神经网络模型预测高强度管线钢焊接接头韧性参数夏比冲击(CVN)值。根据现场X70管线钢焊接参数,选择平均线能量、壁厚、预热温度、焊接位置和取样位置作为模型输入量,建立了节点数为14的一个隐层,激活函数为Sigmoid型的接头韧性参数CVN预测程序。194组样本数据均来自现场焊接数据,随机选取150组样本作为训练样本,其余44组样本作为预测结果的检验样本。分析了神经网络结构对预测结果的影响。预测值误差在20%以内的样本占测试样本数的77%。结果表明,在高强度管线钢焊接中,基于ANN(artificial neural network)的CVN预测方法可为合理选择焊接工艺参数提供一种有效途径。 |
| 关键词: 高强度管线钢 夏比冲击韧性参数 人工神经网络 |
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| Artificial neural network to predict toughness parameter CVN of welded joint of high strength pipeline steel |
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BAI Shiwu1, TONG Lige2, LIU Fangming3, WANG Li2
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1.School of Materials Science and Engineering, Tianjin University, Tianjin 300072, China;2.School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China;3.Pipeline Research Institute of China National Petroleum Corporation, Langfang 065000, China
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| Abstract: |
| The artificial neural network (ANN)model was developed with VC++6.0 based on multiplayer back propagation (BP) to analyze and predict the Charpy-V notch (CVN)impact toughness parameter of the pipeline steel welded joint.Based on the practical welding parameters of X70 steel, the mean energy input, wall thickness, preheat temperature, welding position and sampling position were used as the input parameters of ANN, which includes one hidden layer with 14 nodes and Sigmoid activation function.The 194 sets of data, obtained from the practical welding, were divided randomly into two parts, in which 150 were used as training data and the other as testing data.The influence of structure of ANN on prediction results was analyzed.The number of the sample whose error is less than 20% is about 77% in the total testing data. |
| Key words: high strength pipeline steel Charpy-V notch impact toughness parameters artificial neural network |
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