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Comparative Study of Feed-Forward and Recurrent Neural Networks in Modeling of Electron Beam Welding

  • Sanjib Jaypuria
  • Santosh Kumar Gupta
  • Dilip Kumar PratiharEmail author
Conference paper
Part of the Lecture Notes on Multidisciplinary Industrial Engineering book series (LNMUINEN)

Abstract

In this study, back-propagation neural network (BPNN) and recurrent neural network (RNN) were utilized for the modeling of electron beam welding of AISI 304 stainless steel. The input parameters considered in this study were accelerating voltage, beam current, and scanning speed. These primary parameters were modeled along with the responses like bead geometric parameters. The developed approaches of the modeling had been evaluated in terms of computational time and accuracy in prediction for the test data. The modeling capability of both BPNN and RNN was found to be significantly good, when it was compared with the experimental data. However, Elman recurrent neural network had shown the better accuracy in prediction compared to BPNN, due to the presence of feedback connection in RNN, which leads to capture the dynamics of the nonlinear system efficiently. In addition to this, BPNN was found to be computationally faster than RNN, as expected.

Keywords

Electron beam welding Modeling Back-propagation neural network Recurrent Elman neural network 

Notes

Acknowledgements

The authors thank the support received from Board of Research in Nuclear Science (BRNS), India, and from the Indian Institute of Technology (IIT) Kharagpur, for carrying out this study. The authors thank Dr. M. N. Jha for his valuable comment and suggestion.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Soft Computing Lab, Department of Mechanical EngineeringIndian Institute of Technology KharagpurKharagpurIndia
  2. 2.Advanced Technology Development CentreIndian Institute of Technology KharagpurKharagpurIndia

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