Artificial neural network-based resistance spot welding quality assessment system

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.

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

Science and Technology of Welding & Joining ◽

10.1179/1362171813y.0000000187 ◽

2014 ◽

Vol 19 (3) ◽

pp. 242-249 ◽

Cited By ~ 11

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

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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

10.20944/preprints202110.0411.v1 ◽

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.

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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 ◽

10.3390/met11111874 ◽

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.

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