Optimization of Electrical Resistance Spot Welding and Comparison with Friction Stir Spot Welding of AA2024-T3 Aluminum Alloy Joints

2017 ◽  
Vol 4 (2) ◽  
pp. 1762-1771 ◽  
Author(s):  
R. Karthikeyan ◽  
V. Balasubramaian
Author(s):  
Kai Chen ◽  
Xun Liu ◽  
Jun Ni

A hybrid friction stir resistance spot welding (RSW) process is applied for joining aluminum alloy 6061 to TRIP 780 steel. Compared with conventional RSW, the applied current density is lower and the welding process remains in the solid state. Compared with conventional friction stir spot welding (FSSW) process, the welding force is reduced and the dissimilar material joint strength is increased. The electrical current is applied in both a pulsed and direct form. With the equal amount of energy input, the approximately same force reduction indicates that the electro-plastic material softening effect is insignificant during FSSW process. The welding force is reduced mainly due to the resistance heating induced thermal softening of materials. With the application of electrical current, a wider aluminum flow pattern is observed in the thermo-mechanically affected zone (TMAZ) of weld cross sections and a more uniform hook is formed at the Fe/Al interface. This implies that the aluminum material flow is enhanced. Moreover, the Al composition in the Al–Fe interfacial layer is higher, which means the atomic diffusion is accelerated.


2018 ◽  
Vol 786 ◽  
pp. 104-118
Author(s):  
M.H. Fahmy ◽  
Hamed Abdel-Aleem ◽  
M.R. Elkousy ◽  
N. M. Abdel-Elraheem

This investigation is performed to compare the resistance spot welding (RSW) of aluminum alloy (2024-T3) with friction stir spot welding (FSSW) techniques. In this work, parameters of both resistance spot welding (RSW) and friction stir spot welding (FSSW) techniques were optimized and the optimum welding variables for both techniques were obtained. For FSSW, the tensile shear strength increased with increasing probe length, tool rotational speed and tool holding time. Tensile shear force value of RSW is about 66% of that of FSSW. This is explained by the coarse dendritic structure in resistance spot welding compared to the plastically deformed stir zone and heat affected zone in FSSW. The ratio of nugget shear strength of RSW and FSSW to base metal is about 71% and 149% respectively. The maximum hardness was obtained in stir zone at the surface of the tool. Very fine grain size of about 4 microns was obtained in stir zone followed by elongated and rotated grains in TMAZ where dynamic recrystallization did not occur.


2014 ◽  
Vol 32 (2) ◽  
pp. 83-94 ◽  
Author(s):  
Kenji MIYAMOTO ◽  
Shigeyuki NAKAGAWA ◽  
Chika SUGI ◽  
Kenji TSUSHIMA ◽  
Shingo IWATANI ◽  
...  

2015 ◽  
Vol 3 (1) ◽  
pp. 153-160 ◽  
Author(s):  
Sakchai Chantasri ◽  
Pramote Poonnayom ◽  
Jesada Kaewwichit ◽  
Waraporn Roybang ◽  
Kittipong Kimapong

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Thongchai Arunchai ◽  
Kawin Sonthipermpoon ◽  
Phisut Apichayakul ◽  
Kreangsak Tamee

Resistance Spot Welding (RSW) is processed by using aluminum alloy used in the automotive industry. The difficulty of RSW parameter setting leads to inconsistent quality between welds. The important RSW parameters are the welding current, electrode force, and welding time. An additional RSW parameter, that is, the electrical resistance of the aluminum alloy, which varies depending on the thickness of the material, is considered to be a necessary parameter. The parameters applied to the RSW process, with aluminum alloy, are sensitive to exact measurement. Parameter prediction by the use of an artificial neural network (ANN) as a tool in finding the parameter optimization was investigated. The ANN was designed and tested for predictive weld quality by using the input and output data in parameters and tensile shear strength of the aluminum alloy, respectively. The results of the tensile shear strength testing and the estimated parameter optimization are applied to the RSW process. The achieved results of the tensile shear strength output were mean squared error (MSE) and accuracy equal to 0.054 and 95%, respectively. This indicates that that the application of the ANN in welding machine control is highly successful in setting the welding parameters.


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