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Metals 2018, 8(6), 453; https://doi.org/10.3390/met8060453

Prediction of Resistance Spot Weld Quality of 780 MPa Grade Steel Using Adaptive Resonance Theory Artificial Neural Networks

1
Joining R&D Group, Korea Institute of Industrial Technology, 156 Gaetbeol-ro (Songdo-dong), Yeonsu-Gu, Incheon 21999, Korea
2
School of Mechanical Engineering, Hanyang University, 222 Wangsimni-ro, Seoul 04763, Korea
*
Author to whom correspondence should be addressed.
Received: 31 May 2018 / Revised: 12 June 2018 / Accepted: 12 June 2018 / Published: 13 June 2018
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Abstract

In this study, the weld quality of 780 MPa grade dual phase (DP) steel with 1.0 mm thickness was predicted using adaptive resonance theory (ART) artificial neural networks. The welding voltage and current signals measured during resistance spot welding (RSW) were used as the input layer data, and the tensile shear strength, nugget size, and fracture shape of the weld were used as the output layer data. The learning was performed by the ART artificial neural networks using the input layer and output layer data, and the patterns of learning result were classified by the setting of vigilance parameter, ρ. When the vigilance parameter is 0.8, the best-predicted results were obtained for the tensile shear strength, nugget size, and fracture shape of welds. View Full-Text
Keywords: adaptive resonance theory; artificial neural networks; resistance spot welding; weld quality adaptive resonance theory; artificial neural networks; resistance spot welding; weld quality
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Hwang, I.; Yun, H.; Yoon, J.; Kang, M.; Kim, D.; Kim, Y.-M. Prediction of Resistance Spot Weld Quality of 780 MPa Grade Steel Using Adaptive Resonance Theory Artificial Neural Networks. Metals 2018, 8, 453.

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