Predictive modeling of mechanical properties of welded joints based on generalized dynamic fuzzy RBF neural network

Koganti R, Karas C, Joaquin A,etal. Metal inert gas (MIG) welding process optimization for joining aluminum sheet material using OTC/DAIHEN equipment[J]. Proceedings of IMECE, 2003,10(3): 15-21.[2] Benyounis K Y, Olabi A G, Hashmim S J. Multi-response optimization of CO2laser-welding process of austenitic stainless steel[J]. Optics & Laser Technology, 2008， 40(3)： 76-87.[3] Benyounis K Y, Olabi A G. Optimization of different welding processes using statistical and numerical approaches-A reference guide[J]． Advances in Engineering Software, 2008， 39(3)： 483-496.[4] Pan L K, Wang C C, Hsiso Y C,etal. Optimization of Nd-YAG laser welding onto magnesium alloy via Taguchi analysis[J]. Optics and Laser Technology, 2005, 37(2): 33-42.[5] Peyre P, Sierra G,Deschaux B,etal. Generation of aluminum-steel joints with laser-induced reactive wetting[J]. Materials Science and Engineering, 2007， 444(1)： 327-338.[6] Shojaeefard M H, Behnagh R A, Akbari M,etal. Modeling and pareto optimization of mechanical properties of friction stir welded AA7075/AA5083 butt joints using neural network and particle swarm algorithm[J]. Materials & Design, 2013, 44(2): 190-198.[7] 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. Zhang Yongzhi, Dong Junhui, Zhang Yanfei. 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.[8] 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. Zhang Yongzhi, Dong Junhui. 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.[9] 张艳飞, 董俊慧, 张永志. 基于自适应模糊神经网络焊接接头力学性能预测[J]. 焊接学报, 2007， 28(9): 5-8. Zhang Yanfei, Dong Junhui, Zhang Yongzhi. Prediction mechanical properties of welded joints based on ANFIS[J]. Transactions of the China Welding Institution, 2007， 28(9): 5-8.[10] 罗 薇. 基于广义回归神经网络的广西农业机械需求预测[J]. 农机化研究, 2013， 35(1): 49-52. Luo Wei. Agricultural machinery demand forecasting in guangxi province based on generalized regression neural network[J]. Journal of Agricultural Mechanization Research, 2013， 35(1): 49-52.[11] 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.[12] Chuen C L. Fuzzy logic in control systems: fuzzy logic controller part.I[J]. IEEE Transactions on Systems, Man and Cyberneties, 1990， 20(2): 404-418.[13] Daya R, Lai S, Manjaree P,etal. Corrective action planning using RBF neural network[J]. Applied Soft Computing, 2007(7)： 1055-1063.[14] 唐正魁， 董俊慧， 张永志, 等. 混合聚类RBF神经网络焊接接头力学性能预测[J]. 焊接学报， 2014， 35(12): 105-108. Tang Zhengkui, Dong Junhui, Zhang Yongzhi,etal. Prediction of mechanical properties of welding joints by hy-brid cluster fuzzy RBF neural network[J]. Transactions of the China Welding Institution, 2014， 35(12): 105-108.