Evaluation of welds of aluminum alloy AA6022-T4 welded using an electrode force changeable resistance spot welding machine

2007 ◽  
Vol 21 (7) ◽  
pp. 471-481 ◽  
Author(s):  
K. Furukawa ◽  
M. Katoh ◽  
K. Nishio ◽  
T. Yamaguchi ◽  
F. Nagata
Keyword(s):  
Aluminum Alloy ◽  
Resistance Spot Welding ◽  
Spot Welding ◽  
Resistance Spot ◽  
Welding Machine ◽  
Electrode Force

Resistance Spot Welding between Copper Coated Steel and Aluminum Alloy

2014 ◽  
Vol 675-677 ◽  
pp. 19-22 ◽  
Author(s):  
Li Hu Cui ◽  
Ran Feng Qiu ◽  
Hong Xin Shi ◽  
Yao Min Zhu
Keyword(s):  
Aluminum Alloy ◽  
Resistance Spot Welding ◽  
Spot Welding ◽  
Welding Current ◽  
Middle Layer ◽  
Welding Parameters ◽  
Coated Steel ◽  
Resistance Spot ◽  
Nugget Diameter ◽  
Electrode Force

Aluminum alloy A6061 and copper coated steel was welded by resistance spot welding with. The mechanical properties of the joint were investigated; the effects of welding parameters on nugget diameter and tensile shear load of the joints were discussed. The results show that the joint strength and nugget diameter increases with the increase of welding current and welding time and decreases with the increase of electrode force. As a result, copper plating as the middle layer of resistance spot welding is suitable for welding of aluminum alloy/steel.

Dissimilar metals joining of steel and aluminum alloy by resistance spot welding

2014 ◽  
Vol 32 (2) ◽  
pp. 83-94 ◽  
Author(s):  
Kenji MIYAMOTO ◽  
Shigeyuki NAKAGAWA ◽  
Chika SUGI ◽  
Kenji TSUSHIMA ◽  
Shingo IWATANI ◽  
...  
Keyword(s):  
Aluminum Alloy ◽  
Resistance Spot Welding ◽  
Spot Welding ◽  
Dissimilar Metals ◽  
Resistance Spot

scholarly journals Effect of Resistance Spot Welding Parameters on AA1100 Aluminum Alloy and SGACD Zinc coated Lap Joint Properties

2015 ◽  
Vol 3 (1) ◽  
pp. 153-160 ◽  
Author(s):  
Sakchai Chantasri ◽  
Pramote Poonnayom ◽  
Jesada Kaewwichit ◽  
Waraporn Roybang ◽  
Kittipong Kimapong
Keyword(s):  
Aluminum Alloy ◽  
Resistance Spot Welding ◽  
Spot Welding ◽  
Welding Parameters ◽  
Lap Joint ◽  
Resistance Spot ◽  
Joint Properties ◽  
Aa1100 Aluminum Alloy

scholarly journals Resistance Spot Welding Optimization Based on Artificial Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Thongchai Arunchai ◽  
Kawin Sonthipermpoon ◽  
Phisut Apichayakul ◽  
Kreangsak Tamee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Shear Strength ◽  
Aluminum Alloy ◽  
Parameter Optimization ◽  
Resistance Spot Welding ◽  
Spot Welding ◽  
Tensile Shear Strength ◽  
Tensile Shear ◽  
Resistance Spot

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.

Dynamic Electrode Force and Displacement in Resistance Spot Welding of Aluminum

2004 ◽  
Vol 126 (3) ◽  
pp. 605-610 ◽  
Author(s):  
C. T. Ji, ◽  
Y. Zhou,
Keyword(s):  
Resistance Spot Welding ◽  
Spot Welding ◽  
Welding Current ◽  
Monitoring And Control ◽  
Resistance Spot ◽  
Nugget Diameter ◽  
Electrode Force ◽  
Rate Of Increase ◽  
Process Monitoring And Control ◽  
Electrode Displacement

Dynamic electrode displacement and force were characterized during resistance spot welding of aluminum alloy 5182 sheets using a medium-frequency direct-current welder. It was found that both electrode displacement and force increased rapidly at the beginning of the welding stage and then at a reducing rate. Rates of increase in electrode displacement and force were both proportional to welding current. And both electrode displacement and force experienced a sudden drop when weld metal expulsion occurred. However, the rate of increase in electrode displacement did not reach zero during welding even for joints with sufficient nugget diameter, while electrode force peaked when a large nugget diameter was produced. Possible strategies for process monitoring and control were also discussed.

scholarly journals Expulsion Identification in Resistance Spot Welding by Electrode Force Sensing Based on Wavelet Decomposition with Multi-Indexes and BP Neural Networks

2019 ◽  
Vol 9 (19) ◽  
pp. 4028 ◽  
Author(s):  
Shujun Chen ◽  
Na Wu ◽  
Jun Xiao ◽  
Tianming Li ◽  
Zhenyang Lu
Keyword(s):  
Neural Networks ◽  
Resistance Spot Welding ◽  
Spot Welding ◽  
Wavelet Decomposition ◽  
Peak Amplitude ◽  
Force Sensing ◽  
Work Piece ◽  
Bp Neural Networks ◽  
Resistance Spot ◽  
Electrode Force

Expulsion identification is of significance for welding quality assessment and control in resistance spot welding. In order to improve the identification accuracy, a novel wavelet decomposition and Back Propagation (BP) neural networks with the peak-to-peak amplitude and the kurtosis index were proposed to identify the expulsion from electrode force sensing signals. The rapid step impulse and resultant damping vibration of electrode force was determined as a robust indication of expulsion, and this feature was extracted from the electrode force waveform by seven-layer wavelet decomposition with Daubechies5 wavelets. Then, the energy distribution proportion of the decomposed detail signals were calculated, and the highest-energy one was selected as the target signal. Two statistical indexes were introduced in this paper to measure the target signal in overall situation and volatility. The bigger the peak-to-peak amplitude is, the more violent the fluctuation is. Moreover, the higher the kurtosis index is, the stronger the impact is, and the lower the dispersion degree of the data is. Experimental analysis showed that neither the peak-to-peak amplitude nor the kurtosis index could accurately judge the expulsion defect individually, because of the early signal fluctuation, likely affected by the work-piece clamping, work-piece clearance, or the oxide film thickness. Therefore, the BP neural networks were introduced to identify the expulsion defects, which is a mature and stable non-linear pattern recognition method. Testing experiments presented good results with the trained networks and improved the evaluable accuracy effectively in the quality assessment of the resistance spot welding.

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