Transactions of Materials and Heat Treatment

Title：Neural network model for the constitutive relationship of the Al Cu Mg (2519 )aluminum alloy at elevated temperature

Authors： LIN Qi quan 1 2 PENG Da shu 1 ZHU Yuan zhi 1 3 (1School of Material Science and Engineering Central South University Hengyang Hu'nan 410083 China 2School of Mechanical Engineering Xiangtan University Hengyang Hu'nan 410005 China 3Materials Sci

Unit：

KeyWords： aluminum alloy nonlinear relations neural network model

ClassificationCode：TG306

year,vol(issue):pagenumber：2005,30(1):75-78

Abstract：

The aluminum alloy of 2519 is a new material for armory useThere are very complex nonlinear relations among the thermal dynamical parameters in the process of deformingAn artificial neural network model for constitutive relationship is constructed with the compressing experimental data of cylinder specimens at elevated temperatures on the Gleeble 1500 thermal simulatorFlow stress of the material under various thermal dynamics conditions have been predicted by the network model, and the predicted data fit well with the experimental data (fitness is 26%)The results show that the artificial neural network model for constitutive relationship has higher predicted precision

Funds：

湖南省教育厅划块项目 (03C485)

Reference：

[1]　Sonnino,CarloB,Ford, et al.Potentiometric and potentiostatic determination of the corrosion rate of welded2519 aluminumalloy[J ].ASTM SpecialTechnicalPublication,1991,(1134):132140.
[2]　DevicentS M,DevletianJ H ,GedonS A.Weld properties ofthe newlydeveloped2519T87 aluminum armor alloy[J ].WeldingJournal,1988,67(7):3343.
[3]　航空航天材料咨询小组.航空航天材料咨询报告[M].北京:国防工业出版社,1999
[4]　StanislawDymek,MarekDollar.TEM investigation of age hard enableAl2519 alloy subjected to stress corrosion cracking tests[J].MaterialsChemistry andPhysics,2003,81(2):286288.
[5]　ChunM.S,BiglouJ,LenardJ G,KimJ G.Using neuralnetworks to predict parameters in the hot working of aluminumalloys[J].Journal ofMaterialsProcessingTechnology1998,86(1~3):245251.
[6]　KorczakP,DyjaH,AbudaE.Using neural network modelsfor predicting mechanical properties after hot plate rollingprocesses[J ].Journal ofMaterialsProcessingTechnology1998,80-81:481486.
[7]　丰建朋,郭灵,张麦仓,等.人工神经网络在建立变形高温合金本构关系中的应用[J].中国机械工程,1999,(1):4951.
[8]　齐乐华,侯俊杰,杨方,等.金属液体凝固中直接挤压工艺的神经网络[J].中国有色金属学报,1999,(3):586589.
[9]　张兴全,彭颖红,阮雪榆.Ti17合金本构关系的人工神经网络模型[J].中国有色金属学报,1999,(3):590594.
[10]　柴天佑,谢书明,杜斌,等.基于RBF神经网络的转炉炼钢终点预报[J].中国有色金属学报,1999,(4):868872.

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