动态模糊RBF神经网络焊接接头力学性能预测建模

张永志 董俊慧 朱红玲

张永志, 董俊慧, 朱红玲. 动态模糊RBF神经网络焊接接头力学性能预测建模[J]. 航空材料学报, 2016, 36(5): 26-30. doi: 10.11868/j.issn.1005-5053.2016.5.005
引用本文: 张永志, 董俊慧, 朱红玲. 动态模糊RBF神经网络焊接接头力学性能预测建模[J]. 航空材料学报, 2016, 36(5): 26-30. doi: 10.11868/j.issn.1005-5053.2016.5.005
Yongzhi ZHANG, Junhui DONG, Hongling ZHU. Predictive Modeling of Mechanical Properties of Welded Joints Based on Dynamic Fuzzy RBF Neural Network[J]. Journal of Aeronautical Materials, 2016, 36(5): 26-30. doi: 10.11868/j.issn.1005-5053.2016.5.005
Citation: Yongzhi ZHANG, Junhui DONG, Hongling ZHU. Predictive Modeling of Mechanical Properties of Welded Joints Based on Dynamic Fuzzy RBF Neural Network[J]. Journal of Aeronautical Materials, 2016, 36(5): 26-30. doi: 10.11868/j.issn.1005-5053.2016.5.005

动态模糊RBF神经网络焊接接头力学性能预测建模

doi: 10.11868/j.issn.1005-5053.2016.5.005
基金项目: 

国家自然科学基金 51165027

详细信息
    通讯作者:

    董俊慧(1961-), 男, 博士, 教授, 主要从事焊接结构强度与断裂、焊接应力与变形、新材料开发与连接技术、模糊神经网络在焊接中的应用等方面的研究, (E-mail)jhdong1009@163.com

  • 中图分类号: TG407

Predictive Modeling of Mechanical Properties of Welded Joints Based on Dynamic Fuzzy RBF Neural Network

  • 摘要: 建立动态模糊径向基神经网络RBF(Radial Basis Function, RBF)焊接接头力学性能预测模型, 克服静态RBF和模糊神经网络(Fuzzy Neural Network, FNN)在结构辨识、动态样本训练及学习算法的不足。该模型的结构参数不再提前预设, 在训练过程中动态自适应调整, 适用动态样本数据学习, 学习算法引入分级学习和模糊规则修剪策略, 加速训练并使模型结构更加紧凑。利用三种厚度、不同工艺TC4钛合金TIG焊接试验数据对该模型进行仿真。结果表明:模型具有较高的预测精度, 适用于预测焊接接头力学性能, 为焊接过程在线控制开辟了新的途径。

     

  • 图  1  D-FRBFNN的结构

    Figure  1.  Structure of D-FRBFNN

    图  2  D-FRBFNN模糊规则数曲线

    Figure  2.  Fuzzy rules curve of D-FRBFNN

    图  3  训练完成后的均方根误差

    Figure  3.  RMSE after training completed

    表  1  TC4钛合金化学成分(质量分数/%)

    Table  1.   Chemical composition of TC4 titanium alloy (mass fraction/%)

    COHNAlVFeSiTi
    0.100.150.0150.055.5-6.83.5-4.50.300.15Bal.
    下载: 导出CSV

    表  2  D-FRBFNN模型初始化参数

    Table  2.   Initialization parameters of D-FRBFNN model

    ParameterValueMeaning
    dmax27.200The maximum Euclidean distance between the input variables
    dmin0.300The minimum length of the modeling
    β0.300The boundary of convergence constant
    γ0.350Error attenuation coefficient
    emax10.000Predefined maximum system error
    emin0.200Desired accuracy of the model
    σ013.600The first radial unit width
    k1.050Coefficient overlapping of radial basis units
    kw10.000The adjustment coefficient of Radial Basis unit width
    kerr0.002Threshold of importance fuzzy rules
    下载: 导出CSV

    表  3  D-FRBFNN模型焊接接头力学性能仿真结果

    Table  3.   Simulation results of D-FRBFNN model of mechanical properties of welded joints

    NoThickness/mmWelding voltage/VWeldingcurrent/AArg gas flow/(L·min-1)Welding speed/(m·h-1)Tensile strength/MPaElongation/%Weld hardness/HRCHAZ hardness/HRC
    Experi-mentPredictionExperi-mentPredictionExperi-mentPredictionExperi-mentPrediction
    12.01508.014.08417804.04.238.542.342.028.5
    22.01507.015.08818477.06.143.735.444.338.6
    32.51109.011.09017562.51.837.139.736.936.4
    42.51107.016.08807362.43.040.533.935.934.9
    53.018015.07.59019111.82.144.244.938.438.8
    下载: 导出CSV
  • [1] ADEL M, MOHAMED B.Sizing of stand-alone photo voltaic systems using neural network adaptive model[J].Desalination, 2007, 209(6):64-72.
    [2] 张永志, 董俊慧, 张艳飞.基于径向基神经网络焊接接头力学性能预测[J].焊接学报, 2008, 29(7):81-84. http://www.cnki.com.cn/Article/CJFDTOTAL-HJXB200807020.htm

    ZHANG Y Z, DONG J H, ZHANG Y F.Prediction of mechanical properties of titanium alloy welding joints based on RBF neural network[J].Transactions of the China Welding Institution, 2008, 29(7):81-84. http://www.cnki.com.cn/Article/CJFDTOTAL-HJXB200807020.htm
    [3] 唐正魁, 董俊慧, 张永志.基于RBF神经网络预测接头力学性能的优化算法研究[J].材料科学与工艺, 2009, 17(增刊2):93-97. http://www.doc88.com/p-409566150701.html

    TANG Z K, DONG J H, ZHANG Y Z.Research on optimization based of RBF neural network to predict mechanical properties of joints[J].Materials Science and Technology, 2009, 17 (Suppl 2):93-97. http://www.doc88.com/p-409566150701.html
    [4] 张永志, 董俊慧.两种预测焊接接头力学性能的模糊神经网络[J].焊接学报, 2011, 32(11):104-107. http://www.cnki.com.cn/Article/CJFDTOTAL-HJXB201111028.htm

    ZHANG Y Z, DONG J H.Research on two fuzzy neural networks to predict mechanical properties of welded joints[J].Transactions of the China Welding Institution, 2011, 32(11):104-107. http://www.cnki.com.cn/Article/CJFDTOTAL-HJXB201111028.htm
    [5] 张永志, 董俊慧.基于FCM聚类的模糊RBF神经网络预测焊接接头力学性能建模[J].机械工程学报, 2014, 50(12):58-64. doi: 10.3901/JME.2014.12.058

    ZHANG Y Z, DONG J H.Modeling fuzzy RBF neural network to predict of mechanical properties of welding joints based on fuzzy C-meaning cluster[J].Journal of Mechanical Engineering, 2014, 50(12):58-64. doi: 10.3901/JME.2014.12.058
    [6] 吕其兵, 谭克利, 骆德阳, 等.基于RBF神经网络的钢轨交流闪光焊接头灰斑面积预测[J].焊接学报, 2008, 29(2):93-99. http://www.cnki.com.cn/Article/CJFDTOTAL-HJXB200802025.htm

    LYU Q B, TAN K L, LUO D Y, et al.Prediction of area of gray-spots flaw in alternate rail flash butt welded joint based on RBF neural network[J].Transactions of the China Welding Institution, 2008, 29(2):93-99. http://www.cnki.com.cn/Article/CJFDTOTAL-HJXB200802025.htm
    [7] 王清, 那月, 孙东立, 等.GH99合金TIG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):77-82. http://www.cnki.com.cn/Article/CJFDTOTAL-HJXB201003022.htm

    WANG Q, NA Y, SUN D L, et al.Prediction of tensile property of TIG welding joints in GH99 alloy by artificial neural network[J].Transactions of the China Welding Institution, 2010, 31(3):77-82. http://www.cnki.com.cn/Article/CJFDTOTAL-HJXB201003022.htm
    [8] 张旭明, 吴毅雄, 徐滨士, 等.BP神经网络及其在焊接中的应用[J].焊接, 2003, 2:9-14. http://www.cnki.com.cn/Article/CJFDTOTAL-HAJA200302003.htm

    ZHANG X M, WU Y X, XU B S, et al.BP neural network and its application to welding [J].Welding, 2003(2):9-14. http://www.cnki.com.cn/Article/CJFDTOTAL-HAJA200302003.htm
    [9] FATHI A, AGHAKOUCHAK A A.Prediction of fatigue crack growth rate in welded tubular joints using neural network[J].International Journal of Fatigue, 2007, 29:261-275. doi: 10.1016/j.ijfatigue.2006.03.002
    [10] WU S Q, MENG J E.Dynamic fuzzy neural networks—a novel approach to function approximation[J].IEEE Transactions on Systems, Man and Cybernetics B, 2000, 30(2):358-364. doi: 10.1109/3477.836384
    [11] 倪志盛, 王明彦.基于动态模糊神经网络的多余力矩抑制方法[J].哈尔滨工业大学学报, 2012, 44(10):79-83. http://www.cnki.com.cn/Article/CJFDTOTAL-HEBX201210016.htm

    NI Z S, WANG M Y.A novel method for restraining the redundancy torque based on DFNN[J].Journal of Harbin Institute of Technology, 2012, 44(10):79-83. http://www.cnki.com.cn/Article/CJFDTOTAL-HEBX201210016.htm
    [12] 马草原, 刘建峰, 毛家松, 等.基于D-FNN的电网混沌振荡系统的自适应控制[J].电测与仪表, 2012, 49(4):51-54. http://www.cnki.com.cn/article/cjfdtotal-dcyq201204015.htm

    MA C Y, LIU J F, MAO J S, et al.Adaptive control of power grid chaotic oscillation system based on dynamic fuzzy neural network[J].Electrical Measurement & Instrumentation, 2012, 49(4):51-54. http://www.cnki.com.cn/article/cjfdtotal-dcyq201204015.htm
    [13] WU S Q, MENG J E, GAO Y.A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks[J].IEEE Transactions on Fuzzy Systems, 2001, 9(4):578-594. doi: 10.1109/91.940970
    [14] 伍世虔, 徐军.动态模糊神经网络——设计与应用[M].北京:清华大学出版社, 2008:27-36.

    WU S Q, XU J.Dynamic fuzzy neural network design and application[M].Beijing:Tsinghua University Press, 2008:27-36.
    [15] CHEN S, COWAN C, GRANT P.Orthogonal least squares learning algorithm for radial basis function network[J].IEEE Transactions on Neural Networks, 1991, 2(2):302-309. doi: 10.1109/72.80341
    [16] DAYA R, LAXMI S, MANJAREE P, et al.Corrective action planning using RBF neural network[J].Applied Soft Computing, 2007, 7:1055-1063. doi: 10.1016/j.asoc.2006.10.007
  • 加载中
图(3) / 表(3)
计量
  • 文章访问数:  2658
  • HTML全文浏览量:  1240
  • PDF下载量:  13
  • 被引次数: 0
出版历程
  • 收稿日期:  2016-01-11
  • 修回日期:  2016-02-18
  • 刊出日期:  2016-10-01

目录

    /

    返回文章
    返回