Quality evaluation by classification of electrode force patterns in the resistance spot welding process using neural networks

2004 ◽  
Vol 218 (11) ◽  
pp. 1513-1524 ◽  
Author(s):  
Y J Park ◽  
H Cho
Keyword(s):  
Neural Networks ◽  
Resistance Spot Welding ◽  
Spot Welding ◽  
Quality Evaluation ◽  
Welding Process ◽  
Resistance Spot ◽  
Electrode Force

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.

Effect of Resistance Spot Welding Parameters on the Austenitic Stainless Steel 304 Grade by Using 23 Factorial Design

2011 ◽  
Vol 216 ◽  
pp. 666-670 ◽  
Author(s):  
Prachya Peasura
Keyword(s):  
Stainless Steel ◽  
Austenitic Stainless Steel ◽  
Resistance Spot Welding ◽  
Spot Welding ◽  
Welding Process ◽  
Welding Current ◽  
Welding Time ◽  
Tensile Shear ◽  
Resistance Spot ◽  
Electrode Force

This research was study the effect of resistance spot welding process on physical properties. The specimen was austenitic stainless steel sheet of 1 mm. The experiments with 23 factorial design. The factors used in this study are welding current at 8,000 and 12,000 Amp, welding time at 8 and 12 cycle and electrode force were set at 1.5 and 2.5 kN. The welded specimens were tested by tensile shear testing according to JIS Z 3136: 1999 and macro structure testing according to JIS Z 3139: 1978. The result showed that the welding current, welding time and electrode force had interaction on tensile shear and nugget size at 95% confidential (P value < 0.05). Factors affecting the tensile shear are the most welding current of 12,000 amp., welding time of 8 cycle and electrode force of 2.5 kN. were tensile shear of 9.83 kN. The nugget size was maximum at 7.15 mm. on welding current of 12,000 amp., welding time of 12 cycle and electrode force of 1.5 kN This research can bring information to the foundation in choosing the appropriate parameters to resistance spot welding process.

Development of advanced resistance spot welding process using control of electrode force and welding current during welding

2012 ◽  
Vol 28 (1) ◽  
pp. 13-20 ◽  
Author(s):  
Rinsei Ikeda ◽  
Yasuaki Okita ◽  
Moriaki Ono ◽  
Koichi Yasuda ◽  
Toshio Terasaki
Keyword(s):  
Resistance Spot Welding ◽  
Spot Welding ◽  
Welding Process ◽  
Welding Current ◽  
Resistance Spot ◽  
Electrode Force

The Influence of Welding Parameters on Effected the Complete for Resistance Spot Welding on Mild Steel

2011 ◽  
Vol 214 ◽  
pp. 113-117 ◽  
Author(s):  
Prachya Peasura
Keyword(s):  
Mild Steel ◽  
Resistance Spot Welding ◽  
Spot Welding ◽  
Welding Process ◽  
Welding Current ◽  
Welding Parameters ◽  
Welding Time ◽  
Tensile Shear ◽  
Resistance Spot ◽  
Electrode Force

This research was study the effect of resistance spot welding process on physical properties. The specimen was mild steel sheet metal. The experiments with full factorial design. The factors used in this study are welding current, welding time and electrode force. The welded specimens were tested by tensile shear testing according to JIS Z 3136: 1999 and macro structure testing according to JIS Z 3139: 1978. The result showed that both of welding current, welding time and electrode force had interaction on tensile shear and nugget size at 95% confidential (P value < 0.05). Factors affecting the tensile shear and nugget size are the most welding current 10,000 amp., welding time 10 cycle and electrode force 1 kN. were tensile shear 7.13 kN. and nugget size maximum 6.75 mm. This research can bring information to the foundation in choosing the appropriate parameters to resistance spot welding process.

THERMOMECHANICAL ANALYSIS OF THE RESISTANCE SPOT-WELDING PROCESS

2018 ◽  
Vol 10 (5) ◽  
pp. 449-455 ◽  
Author(s):  
Habib Lebbal ◽  
Lahouari Boukhris ◽  
Habib Berrekia ◽  
Abdelkader Ziadi
Keyword(s):  
Resistance Spot Welding ◽  
Spot Welding ◽  
Welding Process ◽  
Thermomechanical Analysis ◽  
Resistance Spot

Analysis of Electromagnetic Inverse Model of Resistance Spot Welding

2010 ◽  
Vol 160-162 ◽  
pp. 974-979
Author(s):  
Nai Feng Fan ◽  
Zhen Luo ◽  
Yang Li ◽  
Wen Bo Xuan
Keyword(s):  
Resistance Spot Welding ◽  
Regularization Method ◽  
Spot Welding ◽  
Influence Factors ◽  
Great Influence ◽  
Welding Process ◽  
Inverse Model ◽  
Resistance Spot ◽  
Inversion Theory

Resistance spot welding (RSW) is an important welding process in modern industrial production, and the quality of welding nugget determines the strength of products to a large extent. Limited by the level of RSW quality monitor, however, RSW has rarely been applied to the fields with high welding quality requirements. Associated with the inversion theory, in this paper, an electromagnetic inverse model of RSW was established, and the analysis of influence factors, such as the layout of the probes, the discrete program and the regularization method, was implemented as well. The result shows that the layout of the probe and the regularization method has great influence on the model. When the probe is located at the y direction of x-axis or the x direction of y-axis and Conjugate Gradient method is selected, a much better outcome can be achieved.

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