Optimizing Process Parameters for Increasing Corrosion Resistance of Friction Stir Spot Welded Dissimilar Al-5086/C10100 Joints
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Abstract
In this paper, dissimilar aluminium Al-5086/C10100 copper spot joints were made using friction stir spot welding, by varying the significant process parameters such as tool rotational speed, plunge depth and dwell time. Using a central composite design model, twenty experiments were conducted and the welded joints were subjected to corrosion analysis. Using electro-chemical system, polarization tests were conducted and salt fog testing was conducted for 20 h. Empirical relationships were established between the process parameters with the pitting potential and the rate of mass loss. ANOVA was used to evaluate the model’s significance and optimization was done using response surface methodology. It was observed that, at 1112 rpm of tool speed, 2.07 mm of plunging depth and 12.3 s of dwelling period, most positive pitting potential of − 586.86 eV and minimum mass loss of 0.0010234 g occurred. The model was validated with error within three percentage, which indicated high predictability of the developed model.
Keywords
Friction stir spot welding Aluminium Copper Corrosion OptimizationReferences
- 1.Thomas W M, Nicholas E D, Needham J C, Murch M G, Temple Smith P, and Dawas C J, Friction Stir Welding. International Patent Application No. PCT/GB92/02203 G (1991).Google Scholar
- 2.Badarinarayan H, Fundamentals of Friction Stir Spot Welding, Ph D Thesis, Missouri University (2009).Google Scholar
- 3.Manickam S, and Balasubramanian V, J Manuf Eng 10 (2015) 207.Google Scholar
- 4.Arul S G, Pan T, Lin P C, Pan J, Feng Z, and Santella M L, Proceeding of 2005 SAE World Congress, Detroit, MI (2005).Google Scholar
- 5.Pan T Y, Joaquin A, Wilkosz D E, Reatheford L, Nicholson J M, Feng Z, and Santella M L, 5th International Symposium on Friction Stir Welding, TWI, Metz (2004).Google Scholar
- 6.Fahimpour V, Sadrnezhaad S K, and Karimzadeh F, Mater Des 39 (2012) 329.CrossRefGoogle Scholar
- 7.Weifeng X, Jnhe L, and Hongqiang Z, Electrochim Acta, 55 (2010) 2918.CrossRefGoogle Scholar
- 8.Paglia C S, and Buchheit R G, Scr Mater 58 (2008) 383.CrossRefGoogle Scholar
- 9.Yong Gui Y, Li M, Jie Z H, and Bing Z H, Corros Sci Prot Technol, 21 (2009) 119.Google Scholar
- 10.Chen Y, Liu C, Zhou J, and Wang X, Int J Fatigue, 98 (2017) 269.CrossRefGoogle Scholar
- 11.Reyes Hernández D, Manzano Ramírez A, Encinas A, Sánchez Cabrera V M, Marroquín De Jesús A, García García R, Orozco G, and Olivares Ramírez J M, Fuel, 198 (2017) 165.CrossRefGoogle Scholar
- 12.Liu W, Pan H, Li L, Lv H, Wu Z, Cao F, and Zhu J, J Manuf Process, 25 (2017) 418.CrossRefGoogle Scholar
- 13.Dick P A, Knörnschild G H, and Dick L F P, Corros Sci, 114 (2017) 28.Google Scholar
- 14.Lakshminarayanan A K, Annamalai V E, and Elangovan K, J Mater Res Technol, 4 (2015) 262.CrossRefGoogle Scholar
- 15.Montgomery D C, Design and Analysis of Experiments, John Wiley & Sons, New York (2001).Google Scholar
- 16.Venkata Rao C, Madhusudhan Reddy G, and Srinivasa Rao K, Def Technol, 11 (2015) 123.CrossRefGoogle Scholar
- 17.Srinivasa Rao K, and Prasad Rao K, Trans Indian Inst Met, 576 (2004), 593.Google Scholar
- 18.Paventhan R, Lakshminarayanan P R, and Balasubramanian V, Trans Nonferrous Met Soc China 21 (2011) 1480.CrossRefGoogle Scholar
- 19.Miller J E F, and Johnson R, Probability and Statistics for Engineers, 5, Prentice Hall, New Delhi (1996).Google Scholar