Comparison of ANN and Regression Modeling for Predicting the Responses of Friction Stir Welded Dissimilar AA5083-AA6063 Aluminum Alloys Joint

Saurabh Kumar Gupta; K.N. Pandey; Rajneesh Kumar

doi:10.4028/www.scientific.net/AMM.813-814.415

Paper Titles

Process Optimization of Electric Discharge Machining Using Response Surface Methodology
p.393

Review of Composite Machining and Related Optimization Techniques
p.398

Tool Wear Analysis in Turning Using Different Inserts and Different Lubrication Systems
p.404

Comparative Study of Constant Current and Pulsed Current GTA-Welded Al-B₄C Stir Cast Composite
p.410

Comparison of ANN and Regression Modeling for Predicting the Responses of Friction Stir Welded Dissimilar AA5083-AA6063 Aluminum Alloys Joint
p.415

Effect of Friction Stir Welding Process Parameters on Temperature Profile and Tensile Strength of Dissimilar Aluminum Alloys
p.425

Effect of Friction Stir Welding Process Parameters on the Mechanical Properties of AA 6061 Aluminum Alloy Using Taguchi Orthogonal Technique
p.431

Influence of Stick-Slip Effect on Gas Metal Arc Welding
p.438

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 813-814Comparison of ANN and Regression Modeling for...

Comparison of ANN and Regression Modeling for Predicting the Responses of Friction Stir Welded Dissimilar AA5083-AA6063 Aluminum Alloys Joint

Abstract:

Joining of dissimilar aluminum alloys are widely used in automobile, aerospace and shipbuilding industries. Friction Stir Welding (FSW) has been established as one of the most promising processes to defects free joining of aluminum alloys. The aim of present work is to compare the predicted results of FS welded joint through Artificial Neural Network (ANN) modeling and regression modeling. Three responses tensile strength, average microhardness at weld nugget zone (WNZ) and average grain size at WNZ have been selected. The predicted values by ANN modeling and regression modeling of TS, MH and GS values have been found close to the experimental values. The overall average percentage prediction error of ANN model is small as compared to regression model.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 813-814)

Pages:

415-419

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.813-814.415

Citation:

Cite this paper

Online since:

November 2015

Authors:

Saurabh Kumar Gupta*, K.N. Pandey, Rajneesh Kumar

Keywords:

Aluminium Alloy, ANN Modeling, Friction Stir Welding, Regression Modeling

Export:

RIS, BibTeX

Price:

Permissions:

Request Permissions

* - Corresponding Author

References

[1] J. R. Davis, Properties and selection: nonferrous alloys and special-purpose materials, ASM International (1990).

Google Scholar

[2] W. M. Thomas E. D. Nicholas, Friction stir welding for the transportation industries, Mater. Des. 18 (1997) 269–273.

DOI: 10.1016/s0261-3069(97)00062-9

Google Scholar

[3] A. P. Reynolds, Visualization of material flow in autogenous friction stir welds, Sci. Technol. Weld. Join. 5 (2000) 120–124.

Google Scholar

[5] A. K. Lakshminarayanan, V. Balasubramanian, Comparison of RSM with ANN in predicting tensile strength of friction stir welded AA7039 aluminium alloy joints, Trans. Nonferrous Metals Society of China: 19 (2009) 9-18.

DOI: 10.1016/s1003-6326(08)60221-6

Google Scholar

[6] H. Okuyucu H, A. Kurt, E. Arcaklioglu, Artificial neural network application to the friction stir welding of aluminum plates, Mater. Des. 28 (2007) 78–84.

DOI: 10.1016/j.matdes.2005.06.003

Google Scholar

[7] M. H. Shojaeefard, R. A. Behnagh, M. Akbari, Modelling and Pareto optimization of mechanical properties of friction stir welded AA7075/AA5083 butt joints using neural network and particle swarm algorithm, Mater. Des. 44 (2013) 190–198.

DOI: 10.1016/j.matdes.2012.07.025

Google Scholar

[8] ASTM E8 M-04, Standard test method for tension testing of metallic materials, ASTM International (2006).

Google Scholar