[1]李　琳,李继红,余　晗,等.连续油管TIG焊焊接接头最薄弱区工艺-性能神经网络预测模型[J].焊管,2012,35(1):5-7.[doi:１００１－３９３８（２０１２）０１-0005-03]
　LI Lin,LI Ji-hong,YU Han,et al.The Neural Network Prediction Model of Process-property in the Weakest Area of Coiled Tubing TIG Welded Joint[J].,2012,35(1):5-7.[doi:１００１－３９３８（２０１２）０１-0005-03]
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连续油管TIG焊焊接接头最薄弱区工艺-性能神经网络预测模型()
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《焊管》[ISSN:1001-3938/CN:61-1160/TE]

卷:
35
期数:
2012年第1期
页码:
5-7
栏目:
试验与研究
出版日期:
2012-01-28

文章信息/Info

Title:
The Neural Network Prediction Model of Process-property in the Weakest Area of Coiled Tubing TIG Welded Joint
文章编号:
１００１－３９３８（２０１２）０１-0005-03
作者:
李　琳1; 李继红1; 余　晗2; 3; 赵鹏康1; 毕宗岳1; 2; 3; 张　敏1
（1．西安理工大学 材料科学与工程学院，西安 710048；
2.国家石油天然气管材工程技术研究中心，陕西 宝鸡 721008；
3.宝鸡石油钢管有限责任公司 钢管研究院，陕西 宝鸡 721008）
Author(s):
LI Lin1;  LI Ji-hong1; YU Han2; 3; ZHAO Peng-kang1;  BI Zong-yue1; 2; 3 ; ZHANG Min1
（ 1. School of Materials Science and Engineering， Xi’an University of Technology， Xi’an 710048， China；
2.National Petroleum and Gas Tubular Goods Engineering Technology Research Center，Baoji 721008，Shaanxi，China；3. Steel Pipe Research Institute， Baoji Petroleum Steel Pipe Co.， Ltd.， Baoji 721008， Shaanxi， China）
关键词:
连续油管; TIG焊; BP神经网络; 冲击韧性
Keywords:
coiled tube;  TIG welded joint;  BP neural network;  impact toughness
分类号:
TE973
DOI:
１００１－３９３８（２０１２）０１-0005-03
文献标志码:
A
摘要:
通过试验检测出连续油管TIG焊接头最薄弱区力学性能，采用BP神经网络对该区域工艺性能进行仿真预测，研究了不同训练函数对网络性能的影响，通过对比分析不同训练函数下网络性能，得出连续油管TIG焊接头最薄弱区线能量-冲击功预测模型，最终选取LM算法、SCG算法对网络进行训练，采用这两种算法建立起的线能量-冲击功模型精度较高，测试数据预测值与实测值平均相对误差分别为0.785%和0.34%，网络能够很好地对“未知”冲击功进行预测。
Abstract:
The mechanical properties of the weakest areas in the joint of coiled tube welded by TIG was obtained according to the experiment. BP neural network was used to simulate and predict the process performance of the region， The influence on network performance was studied under different training function， Line Energy-Impact energy prediction model of the weakest areas in the joint of coiled tube welded by TIG was obtained by comparing the Network performance which received under different training function. LM algorithm and SCG algorithm was selected to train the network finally. Both the algorithms present higher precision of Line Energy-Impact energy prediction model. The average relative error of predicted and measured values of test data were 0.785% and 0.34% respectively. It was very well that the impact energy were predicted in the network.

参考文献/References:

［1］ 朱凯，王正林．精通MATLAB神经网络［M］ ．北京:电子工业出版社，2010:193-224．
［2］ 朱大奇，史慧．人工神经网络原理及应用［M］．北京:科学出版社，2006:33-53．
［3］ 王清，那月，孙东立，等．GH99合金TIG焊焊接接头拉伸性能的人工神经网络预测［J］．焊接学报，2010，31（3）:77-80．
［4］ 周开利，康耀红．神经网络模型及其MATLAB仿真程序设计［M］．北京:清华大学出版社，2005:69-100．
［5］ 赵章明.连续油管工程技术手册［M］.北京:石油工业出版社，2011．

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备注/Memo

备注/Memo:
作者简介：李　琳（1987—） ，女，西安理工大学材料科学与工程学院硕士研究生。
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