Defect Recognition of Resistance Spot Welding Based on Artificial Neural Network

2012 ◽  
pp. 423-430 ◽  
Author(s):  
Li Yongyan ◽  
Zhao Weimin ◽  
Xue Haitao ◽  
Ding Jian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Resistance Spot Welding ◽  
Spot Welding ◽  
Resistance Spot ◽  
Artificial Neural ◽  
Defect Recognition

scholarly journals Study on Nugget Diameter Prediction of Resistance Spot Welding Using an Artificial Neural Network

2021 ◽  
Author(s):  
Jongkyu Kim ◽  
JaHun Ku ◽  
Yeongdo Park ◽  
Youngchang Kim ◽  
Youngmin Hwang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Resistance Spot Welding ◽  
Spot Welding ◽  
Resistance Spot ◽  
Nugget Diameter ◽  
Artificial Neural

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.

scholarly journals Investigation of the Extrapolation Capability of an Artificial Neural Network Algorithm in Combination With Process Signals in Resistance Spot Welding of Advanced High-Strength Steels

2021 ◽  
Author(s):  
Bassel El-Sari ◽  
Max Biegler ◽  
Michael Rethmeier
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Process Parameters ◽  
Resistance Spot Welding ◽  
Spot Welding ◽  
Predictive Accuracy ◽  
Training Data ◽  
Multi Layer Perceptron ◽  
Data Set ◽  
Artificial Neural

Resistance spot welding is an established joining process in the production of safety-relevant components in the automotive industry. Therefore, a consecutive process monitoring is essential to meet the high-quality requirements. Artificial neural networks can be used to evaluate the process parameters and signals to ensure the individual spot weld quality. The predictive accuracy of such algorithms depends on the provided training data set and the prediction of untrained data is challenging. The aim of this paper is to investigate the extrapolation capability of the multi-layer perceptron model. That means, that the predictive performance of the model will be tested with data that clearly differs from the training data in terms of material and coating composition. Therefore, three multi-layer perceptron regression models were implemented to predict the nugget diameter from process data. The three models were able to predict the trained datasets very well. The models, which were provided with features from the dynamic resistance curve predicted the new dataset better than the model with only process parameters. This study shows the beneficial influence of the process signals on the predictive accuracy and robustness of artificial neural network algorithms. Especially, when predicting a data set from outside of the training space.

scholarly journals Investigation of the Extrapolation Capability of an Artificial Neural Network Algorithm in Combination with Process Signals in Resistance Spot Welding of Advanced High-Strength Steels

Metals ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1874
Author(s):  
Bassel El-Sari ◽  
Max Biegler ◽  
Michael Rethmeier
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Process Parameters ◽  
Resistance Spot Welding ◽  
Spot Welding ◽  
Predictive Accuracy ◽  
Training Data ◽  
Multi Layer Perceptron ◽  
Data Set ◽  
Artificial Neural

Resistance spot welding is an established joining process for the production of safety-relevant components in the automotive industry. Therefore, consecutive process monitoring is essential to meet the high quality requirements. Artificial neural networks can be used to evaluate the process parameters and signals, to ensure individual spot weld quality. The predictive accuracy of such algorithms depends on the provided training data set, and the prediction of untrained data is challenging. The aim of this paper was to investigate the extrapolation capability of a multi-layer perceptron model. That means, the predictive performance of the model was tested with data that clearly differed from the training data in terms of material and coating composition. Therefore, three multi-layer perceptron regression models were implemented to predict the nugget diameter from process data. The three models were able to predict the training datasets very well. The models, which were provided with features from the dynamic resistance curve predicted the new dataset better than the model with only process parameters. This study shows the beneficial influence of process signals on the predictive accuracy and robustness of artificial neural network algorithms. Especially, when predicting a data set from outside of the training space.

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
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