Application of Neural Networks in Preform Design of Aluminium Upsetting Process Considering Different Interfacial Frictional Conditions

  • Authors:

  • Ajay Kumar Kaviti;K. K. Pathak;M. S. Hora

  • Affiliations:

  • Department of Mechanical Engineering, SISTec, Bhopal, India;Advanced Materials and Processes Research Institute (CSIR), Bhopal, India;Department of Applied Mechanics, MANIT, Bhopal, India

  • Venue:
  • PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
  • Year:
  • 2009

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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, the same is arrived at using artificial neural networks (ANN) considering different interfacial friction conditions between top and bottom die and billet interface. Back propagation neural networks is trained based on finite element analysis results considering ten different interfacial friction conditions and varying geometrical and processing parameters, to predict the optimum preform for commercial Aluminium. Neural network predictions are verified for three new problems of commercial aluminum and observed that these are in close match with their simulation counterparts.