scholarly journals Particle simulation of nugget formation process during steel/aluminum alloy dissimilar resistance spot welding and thickness estimation of intermetallic compounds

2021 ◽  
Vol 39 (4) ◽  
pp. 371-378
Author(s):  
Shinnosuke CHIKUCHI ◽  
Masaya SHIGETA ◽  
Hisaya KOMEN ◽  
Manabu TANAKA
2013 ◽  
Vol 859 ◽  
pp. 7-10 ◽  
Author(s):  
Nan Nan Wang ◽  
Ran Feng Qiu ◽  
Fang Fang Liu ◽  
Man Zhang ◽  
Qing Qing Xuan

In this paper, nugget growth stages during resistance spot welding between aluminum alloy and steel, such as surface breakdown, asperity softening, temperature rising, molten nugget formation, nugget growth and mechanical collapse, was analyzed according to resistance dynamic variation during welding. The effects of work pieces surface condition and electrode force on the occurrence of inter-expulsion and surface expulsion was discussed during resistance spot welding.


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 ◽  
...  

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

Author(s):  
Zhijun Wu ◽  
Guanlin Zhang ◽  
Bingxu Wang ◽  
Kelvin Shih

Resistance Spot Welding (RSW) is one of the most common and dominant technologies utilized in the automotive industry to join the thin sheet metals together, and expulsion is a common phenomenon during the operation. How to ensure the high quality nugget formation and joining performance is essential to ensure the quality and integrity of structures. In this study, solid state resistance spot welding is introduced in order to prevent expulsion. The effect of welding current and welding time on the mechanical performance of the solid state RSW in terms of nugget size, tensile performance and nugget formation will be investigated experimentally by using steel sheet metals. Microstructure and micro-hardness of the nugget cross-section will be evaluated as well.


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