Artificial neural network application to the friction-stir welding of aluminum plates

被引：198
作者：
Okuyucu, Hasan
Kurt, Adem [1 ]
Arcaklioglu, Erol
机构：
[1] Gazi Univ, Fac Tech Educ, TR-06500 Ankara, Turkey
[2] Kirikkale Univ, Dept Mech Engn, TR-71450 Kirikkale, Turkey
来源：
MATERIALS & DESIGN | 2007年 / 28卷 / 01期
关键词：
friction stir welding; mechanical properties; ANN; modeling;
D O I：
10.1016/j.matdes.2005.06.003
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
An artificial neural network (ANN) model was developed for the analysis and simulation of the correlation between the friction stir welding (FSW) parameters of aluminium (Al) plates and mechanical properties. The input parameters of the model consist of weld speed and tool rotation speed (TRS). The outputs of the ANN model include property parameters namely: tensile strength, yield strength, elongation, hardness of weld metal and hardness of heat effected zone (HAZ). Good performance of the ANN model was achieved. The model can be used to calculate mechanical properties of welded Al plates as functions of weld speed and TRS. The combined influence of weld speed and TRS on the mechanical properties of welded Al plates was simulated. A comparison was made between measured and calculated data. The calculated results were in good agreement with measured data. (c) 2005 Elsevier Ltd. All rights reserved.
引用
收藏
页码：78 / 84
页数：7
相关论文
共 15 条
[1]   Thermodynamic analyses of refrigerant mixtures using artificial neural networks [J].
Arcaklioglu, E ;
Çavusoglu, A ;
Erisen, A .
APPLIED ENERGY, 2004, 78 (02) :219-230
[2]   The influence of stirrer geometry on bonding and mechanical properties in friction stir welding process [J].
Boz, M ;
Kurt, A .
MATERIALS & DESIGN, 2004, 25 (04) :343-347
[3]  
DOLBY RE, 2001, 7 INT AACH WELD C 3, P59
[4]   Modelling the correlation between processing parameters and properties of maraging steels using artificial neural network [J].
Guo, Z ;
Sha, W .
COMPUTATIONAL MATERIALS SCIENCE, 2004, 29 (01) :12-28
[5]   A study on the prediction of the mechanical properties of a ceramic tool based on an artificial neural network [J].
Huang, CZ ;
Zhang, L ;
He, L ;
Sun, J ;
Fang, B ;
Zou, B ;
Li, ZQ ;
Ai, X .
JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 2002, 129 (1-3) :399-402
[6]   Modelling the correlation between processing parameters and properties in titanium alloys using artificial neural network [J].
Malinov, S ;
Sha, W ;
McKeown, JJ .
COMPUTATIONAL MATERIALS SCIENCE, 2001, 21 (03) :375-394
[7]   Application of artificial neural networks for modelling correlations in titanium alloys [J].
Malinov, S ;
Sha, W .
MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING, 2004, 365 (1-2) :202-211
[8]   PVDF multifilament yarns grafted with polystyrene induced by γ-irradition:: Influence of the grafting parameters on the mechanical properties [J].
Marmey, P ;
Porté, MC ;
Baquey, C .
NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION B-BEAM INTERACTIONS WITH MATERIALS AND ATOMS, 2003, 208 :429-433
[9]  
MASSIE DD, 2001, 1 INT C APPL THERM I, P123
[10]   Development of prediction model for mechanical properties of batch annealed thin steel strip by using artificial neural network modelling [J].
Myllykoski, P ;
Larkiola, J ;
Nylander, J .
JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 1996, 60 (1-4) :399-404
12