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Knowledge-based systems using neural networks for electron beam welding process of reactive material (Zircaloy-4)

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Abstract

Bead-on-plate welding of zircaloy-4 (a reactive material) plates was conducted using electron beam according to central composite design of experiments. Its predictive models were developed in the form of knowledge-based systems in both forward and reverse directions using neural networks. Input parameters considered for this welding of reactive metals were accelerating voltage, beam current and weld speed. The responses of the welding process were measured in terms of bead width, depth of penetration and micro-hardness. Forward mapping of the welding process was conducted using regression analysis, back-propagation neural network (BPNN), genetic algorithm-tuned neural network (GANN) and particle swarm optimization algorithm-tuned neural network (PSONN). Reverse mapping of this process was also carried out using the BPNN, GANN and PSONN-based approaches. Neural network-based approaches could model this welding process of reactive material in both forward and reverse directions efficiently, which is required for the automation of the same. The performance of the neural network models was found to be data-dependent. The BPNN could outperform the other two approaches for most of the cases but not all in both the forward and reverse mappings.

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Notes

  1. Technical Data Sheet of Reactor grade zirconium available from http://www.atimetals.com.

  2. Available from http://www.cs.ru.nl/~peterl/eolss.pdf.

  3. Available from http://www.minitab.com.

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Acknowledgments

The authors are grateful to Dr. L. M. Gantayet, Director, Beam Technology Development Group, Bhabha Atomic Research Centre (BARC), Mumbai, India, for his guidance and constant encouragement for this research work. Thanks are also due to Shri Joy Mittra, Shri Santosh Kumar and Shri L. D. Verma of BARC for their whole-hearted support during various stages of this research work. This work was supported by the Department of Atomic Energy (DAE), Government of India, [Grant number 2005/34/22-BRNS/2662 Dt. 26.02.07].

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Authors and Affiliations

  1. Bhabha Atomic Research Centre, Trombay, Mumbai, 400085, India

    M. N. Jha, A. V. Bapat, Maajid Ali & A. C. Bagchi

  2. Department of Mechanical Engineering, Indian Institute of Technology, Kharagpur, 721302, India

    D. K. Pratihar & V. Dey

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  1. M. N. Jha
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  4. V. Dey
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Correspondence to D. K. Pratihar.

Appendix: Photographs showing the shapes of fusion zone for some of the EB-welded samples of Zirclaoy-4

Appendix: Photographs showing the shapes of fusion zone for some of the EB-welded samples of Zirclaoy-4

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Jha, M.N., Pratihar, D.K., Bapat, A.V. et al. Knowledge-based systems using neural networks for electron beam welding process of reactive material (Zircaloy-4). J Intell Manuf 25, 1315–1333 (2014). https://doi.org/10.1007/s10845-013-0732-3

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