A Vision Inspection System for the Defects of Resistance Spot Welding Based on Neural Network

2017 ◽  
pp. 161-168 ◽  
Author(s):  
Shaofeng Ye ◽  
Zhiye Guo ◽  
Peng Zheng ◽  
Lei Wang ◽  
Chun Lin
Keyword(s):  
Neural Network ◽  
Resistance Spot Welding ◽  
Spot Welding ◽  
Inspection System ◽  
Resistance Spot ◽  
Vision Inspection

Weld quality monitoring research in small scale resistance spot welding by dynamic resistance and neural network

Measurement ◽  
2017 ◽  
Vol 99 ◽  
pp. 120-127 ◽  
Author(s):  
Xiaodong Wan ◽  
Yuanxun Wang ◽  
Dawei Zhao ◽  
YongAn Huang ◽  
Zhouping Yin
Keyword(s):  
Neural Network ◽  
Resistance Spot Welding ◽  
Spot Welding ◽  
Quality Monitoring ◽  
Small Scale ◽  
Dynamic Resistance ◽  
Weld Quality ◽  
Resistance Spot ◽  
Scale Resistance

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.

Expulsion detection system for resistance spot welding based on a neural network

2004 ◽  
Vol 15 (3) ◽  
pp. 592-598 ◽  
Author(s):  
Primo Podr aj ◽  
Ivan Polajnar ◽  
Janez Diaci ◽  
Zoran Kari
Keyword(s):  
Neural Network ◽  
Resistance Spot Welding ◽  
Spot Welding ◽  
Detection System ◽  
Resistance Spot
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