Interfacial characterization of joint between mild steel and aluminum alloy welded by resistance spot welding

2010 ◽  
Vol 61 (7) ◽  
pp. 684-688 ◽  
Author(s):  
Ranfeng Qiu ◽  
Hongxin Shi ◽  
Keke Zhang ◽  
Yimin Tu ◽  
Chihiro Iwamoto ◽  
...  
2014 ◽  
Vol 675-677 ◽  
pp. 15-18 ◽  
Author(s):  
Long Long Hou ◽  
Ran Feng Qiu ◽  
Hong Xin Shi ◽  
Jun Qing Guo

Aluminum alloy A6061 and mild steel Q235 was welded using resistance spot welding with an interlayer of AlCu28. The mechanical properties of the joint were investigated; the effects of various welding parameters on nugget diameter and tensile shear load of the joints were systematically discussed. The results reveal that it is effective to weld aluminum alloy and mild steel using resistance spot welding with an interlayer of AlCu28.


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.


2020 ◽  
Vol 111 (5-6) ◽  
pp. 1671-1682
Author(s):  
Michael Piott ◽  
Alexandra Werber ◽  
Leander Schleuss ◽  
Nikolay Doynov ◽  
Ralf Ossenbrink ◽  
...  

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