摘要
针对焊接过程的高度非线性,多种因素的复杂交互作用,难以预测焊接接头力学性能的问题和常用反馈(Back propagation,BP)神经网络的不足,利用模糊C均值(Fuzzy C-means,FCM)聚类算法和伪逆法相结合,建立焊接接头力学性能模糊径向基(Radial basis function,RBF)神经网络预测模型。以TC4钛合金惰性气体钨极保护焊(Tungsten inert gas arc welding,TIG焊)焊接工艺参数(焊接电流、焊接速度和氩气流量)作为模型的输入参数,以焊后力学性能(抗拉强度、抗弯强度、伸长率、焊缝硬度和热影响区硬度)作为模型的输出参数。利用27组试验数据对所建模型进行学习训练,用另外9组试验数据进行仿真。结果表明,利用该方法所建模型具有结构稳定、训练速度快、适应性强、鲁棒性好、预测精度高的特点,能够预测焊接接头力学性能。通过数学解析,用函数形式表达焊接工艺参数与接头力学性能之间的规律,可以优化焊接工艺参数,为调控焊接接头的质量提供依据。
For high nonlinear,complex interaction of many factors in welding process,it is difficult to predict the mechanical properties of welded joints.At the same time to overcome the lack of common back propagation（BP） neural network.A fuzzy radial basis function（RBF） neural network model is built to predict mechanical properties of welded joints based on Fuzzy C-means cluster and Pseudo-inverse method.Take the TC4 titanium alloy Tungsten inert gas（TIG） arc welding process parameters including：Welding current,welding speed,argon gas flux for the input parameters,take the mechanical properties of welded joints including：Tensile strength,bend strength,extensibility,weld hardness and heat affected zone hardness for the output parameters.The 27 sets experiment data are used to train this model,other 9 sets are used to simulate this model.Simulation results show that the model＇s structural is stability,training speed is fast,adaptability is strong,robustness is good,prediction accuracy is high,it can be used to predict the mechanical properties of welded joints.Through mathematical analysis,it can express the rule between welding process parameters and mechanical properties of joints use functional from,also can be used to optimize the welding parameters,provide the basis to adjust and control the quality of welded joints.
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2014年第12期58-64,共7页
Journal of Mechanical Engineering
基金
国家自然科学基金资助项目(51165027)
关键词
模糊C均值聚类
模糊径向基神经网络
预测
焊接
建模
fuzzy C-means cluster
fuzzy radial basis function（RBF） neural network
prediction
welding
modeling
