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The application of neural networks in the preform design of the upsetting process

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Abstract

Design of the optimum preform for near net shape manufacturing is a crucial step in upsetting process design. In this study, artificial neural networks (ANN) are used to consider different interfacial friction conditions between the top and bottom die and billet interface. Two back propagation neural networks are trained based on finite element analysis results considering ten interfacial friction conditions and varying geometrical and processing parameters, to predict the optimum preform for high strength (HS) steel and commercial aluminum. Neural network predictions were verified for three new problems of both HS steel and commercial aluminum and observed that these are in close match with their simulation counterparts. It was further Experimentally verified with two commercial aluminum specimens and observed that the preform values predicted by ANN are in good agreement with experimental results.

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Authors and Affiliations

  1. Advanced Materials and Processes Research Institute (CSIR), Bhopal, MP, 462026, India

    K. K. Pathak

  2. Department of Applied Mechanics, Maulana Azad National Institute of Technology (MANIT), Bhopal, MP, 462003, India

    Ajay Kumar Kaviti & M. S. Hora

Authors
  1. K. K. Pathak
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  2. Ajay Kumar Kaviti
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  3. M. S. Hora
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Pathak, K.K., Kaviti, A.K. & Hora, M.S. The application of neural networks in the preform design of the upsetting process. JOM 62, 55–59 (2010). https://doi.org/10.1007/s11837-010-0079-6

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  • DOI: https://doi.org/10.1007/s11837-010-0079-6

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