Quality assessment for resistance spot welding based on binary image of electrode displacement signal and probabilistic neural network

2014 ◽  
Vol 19 (3) ◽  
pp. 242-249 ◽  
Author(s):  
H. J. Zhang ◽  
F. J. Wang ◽  
W. G. Gao ◽  
Y. Y. Hou
Keyword(s):  
Neural Network ◽  
Quality Assessment ◽  
Resistance Spot Welding ◽  
Spot Welding ◽  
Probabilistic Neural Network ◽  
Binary Image ◽  
Resistance Spot ◽  
Displacement Signal ◽  
Electrode Displacement

Quality Estimation of the Resistance Spot Welding Based on Hopfield Network

2011 ◽  
Vol 189-193 ◽  
pp. 3364-3369
Author(s):  
Hong Jie Zhang ◽  
Yan Yan Hou
Keyword(s):  
Resistance Spot Welding ◽  
Spot Welding ◽  
Fuzzy Theory ◽  
Welding Process ◽  
Hopfield Network ◽  
Quality Estimation ◽  
Resistance Spot ◽  
Displacement Signal ◽  
Electrode Displacement ◽  
Estimation System

Lots of dynamic information, which can directly or indirectly reflect the quality of welded spot, is included within the electrode displacement signal of resistance spot welding process. In this research, the displacement signal is monitored and mapped into a 15×25 element bipolarized matrix by means of some method of fuzzy theory. Some welded spots are classified into five classes according to the prototype pattern matrixes. An effective RSW quality estimation system is developed based on Hopfield network when taking the tensile shear strength of the welded spot joint as the estimation index of welded spot quality. The results of cross-validation test shows that the Hopfield network can satisfactorily accomplish the task of classification of the welded spot and has board application prospects.

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.

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