Feasibility study of dissimilar joining of aluminum alloy 5052 to pure copper via thermo-compensated resistance spot welding

2016 ◽  
Vol 106 ◽  
pp. 235-246 ◽  
Author(s):  
Yu Zhang ◽  
Yang Li ◽  
Zhen Luo ◽  
Tao Yuan ◽  
Jing Bi ◽  
...  
2009 ◽  
Vol 2 (1) ◽  
pp. 58-67 ◽  
Author(s):  
Kenji Miyamoto ◽  
Shigeyuki Nakagawa ◽  
Chika Sugi ◽  
Hiroshi Sakurai ◽  
Akio Hirose

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.


2014 ◽  
Vol 98 ◽  
pp. 186-192 ◽  
Author(s):  
Yuichiro Suzuki ◽  
Tomo Ogura ◽  
Makoto Takahashi ◽  
Akio Hirose

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

2014 ◽  
Vol 32 (2) ◽  
pp. 95-106
Author(s):  
Kenji MIYAMOTO ◽  
Shigeyuki NAKAGAWA ◽  
Chika SUGI ◽  
Tomo OGURA ◽  
Akio HIROSE

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