Prediction of tensile strength of friction stir weld joints with adaptive neuro-fuzzy inference system (ANFIS) and neural network

Friction-stir-welding (FSW) is a solid-state joining process where joint properties are dependent on welding process parameters. In the current study three critical process parameters including spindle speed(N), plunge force (Fz), and welding speed (V) are considered key factors in the determination...

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Vydáno v:Materials & design Ročník 92; s. 288 - 299
Hlavní autoři: Dewan, Mohammad W., Huggett, Daniel J., Warren Liao, T., Wahab, Muhammad A., Okeil, Ayman M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Marshall Space Flight Center Elsevier Ltd 15.02.2016
Elsevier
Témata:
Adaptive neuro-fuzzy inference system
Adaptive systems
Artificial neural networks
Cybernetics, Artificial Intelligence And Robotics
Friction stir welding
Fuzzy logic
Learning theory
Leave-one-out cross validation
Mathematical models
Mechanical Engineering
Neural networks
Process parameters
Tensile strength
Welding process parameters
Welding process parameters
Adaptive neuro-fuzzy inference system
Friction stir welding
Tensile strength
Leave-one-out cross validation
ISSN:0264-1275, 1873-4197
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Shrnutí:Friction-stir-welding (FSW) is a solid-state joining process where joint properties are dependent on welding process parameters. In the current study three critical process parameters including spindle speed(N), plunge force (Fz), and welding speed (V) are considered key factors in the determination of ultimate tensile strength (UTS) of welded aluminum alloy joints. A total of 73 weld schedules were welded and tensile properties were subsequently obtained experimentally. It is observed that all three process parameters have direct influence on UTS of the welded joints. Utilizing experimental data, an optimized adaptive neuro-fuzzy inference system (ANFIS) model has been developed to predict UTS of FSW joints. A total of 1200 models were developed by varying the number of membership functions (MFs), type of MFs, and combination of four input variables (N,V,Fz,EFI) utilizing a MATLAB platform. Note EFI denotes an empirical force index derived from the three process parameters. For comparison, optimized artificial neural network (ANN) models were also developed to predict UTS from FSW process parameters. By comparing ANFIS and ANN predicted results, it was found that optimized ANFIS models provide better results than ANN. This newly developed best ANFIS model could be utilized for prediction of UTS of FSW joints. Development of a model to predict UTS from FSW process parameter. [Display omitted] •FSW joint tensile properties are dependent on welding process parameters.•A total of 73 weld schedules were welded and tensile properties obtained.•An optimized ANFIS model was developed to predict UTS from weld process parameters.•An optimized ANN model was also developed for comparison.•Optimized ANFIS model provides better results than ANN model.
Bibliografie:ISSN: 0264-1275
Report Number: M16-5013
M16-5013
Marshall Space Flight Center
MSFC
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0264-1275
1873-4197
DOI:10.1016/j.matdes.2015.12.005