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An ANFIS based approach for predicting the weld strength of resistance spot welding in artificial intelligence development

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Abstract

Artificial intelligence (AI) is a modern approach which has the ability to capture nonlinear relationships and interaction effects. Frequently, AI methods have been used by researchers to predict output responses of the Resistance spot welding (RSW) due to the complex- ity during the welding process and numerous interferential factors, especially the short-time property of the process. The present study is to investigate the weld strength of spot weld for high strength steel sheets of CR780 using the Adaptive neuro fuzzy inference system (ANFIS). These results were compared with those obtained by conventional Artificial neural network (ANN). The input parameters were extracted through the dynamic resistance signal which was obtained from the primary circuit of the welding machine. Both the ANN and ANFIS models were utilized for the formulation of mathematical model with an off-line dynamic resistance response of the RSW at a particular parameters setting. The performances of both models were compared in terms of correlation coefficient value (R), Root mean squared error (RMSE), and Mean absolute percentage error (MAPE). While both methods were capable of predicting the weld strength, it was found that ANFIS model could predict more precisely than ANN.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Hanyang University, Hanyang, Korea

    Mohd Faridh Ahmad Zaharuddin, Donghyun Kim & Sehun Rhee

  2. Department of Materials, Manufacturing and Industrial Engineering, Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia

    Mohd Faridh Ahmad Zaharuddin

Authors
  1. Mohd Faridh Ahmad Zaharuddin
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  2. Donghyun Kim
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  3. Sehun Rhee
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Corresponding author

Correspondence to Sehun Rhee.

Additional information

Recommended by Associate Editor Young Whan Park

Mohd Faridh Bin Ahmad Zaharuddin is currently a Ph.D. student in Mechanical Engineering at Hanyang University, Seoul Korea. He received a B.Sc. in Manufacturing System Engineering in 1997 and M.Sc. in Advanced Manufacturing Technology in 2003 from University of Portsmouth, England.

Donghyun Kim is currently a Ph.D. student in Mechanical Engineering (Ultrasonic Welding Monitoring) at Hanyang University, Seoul, Korea. He received a B.Sc.in Mechanical Engineering in 2011 and M.Sc. in Mechanical Engineering (Resistance Spot Welding Monitoring) in 2013 from Hanyang University.

Sehun Rhee is currently a Professor of Mechanical Engineering at Hanyang University, Seoul, South Korea. He received his Doctor Philosophy (Ph.D.) in Mechanical Engineering from the University of Michigan, United State of America, in 1990. His research interests include printing electronics and welding monitoring system on resistant spot welding, ultrasonic welding, arc welding and laser welding.

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Zaharuddin, M.F.A., Kim, D. & Rhee, S. An ANFIS based approach for predicting the weld strength of resistance spot welding in artificial intelligence development. J Mech Sci Technol 31, 5467–5476 (2017). https://doi.org/10.1007/s12206-017-1041-0

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  • DOI: https://doi.org/10.1007/s12206-017-1041-0

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