Neural network application to the reconfigurable multipoint forming process

  • Viorel Păunoiu Dunarea de Jos University of Galati, Department of Manufacturing, Robotics and Welding Engineering, Romania
  • Virgil Teodor Dunarea de Jos University of Galati, Department of Manufacturing, Robotics and Welding Engineering, Romania
  • Alexandru Epureanu Dunarea de Jos University of Galati, Department of Manufacturing, Robotics and Welding Engineering, Romania
  • Eugen Găvan Dunarea de Jos University of Galati, Department of Ship Structure
  • Gabriel Bercu Dunarea de Jos University of Galati, Department of Mathematics and Computer Science
Keywords: multipoint forming, modelling, FEM, neural network, sheet metal

Abstract

Multipoint forming of thin sheets plates is based on the discrete die-punch reconfigurable tooling concept. The paper is concerned with the application of the neural network method in studying the springback phenomenon in multipoint forming. The method of neural network is first presented. An algorithm based on FEM and neural network modeling is then presented. Using the FEM simulation, the springback values for a simply curved geometry are obtained. On this basis, a neural network is trained, using as input parameters the rubber thickness, the rubber elastic modulus and the pins stroke and as output the springback in width and height defined. The conclusions obtained from the neural network modeling certify the validity of the developed method.

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Published
2011-06-10
How to Cite
1.
Păunoiu V, Teodor V, Epureanu A, Găvan E, Bercu G. Neural network application to the reconfigurable multipoint forming process. Annals of ”Dunarea de Jos” University of Galati, Fascicle V, Technologies in machine building [Internet]. 10Jun.2011 [cited 29Mar.2024];29(1):75-0. Available from: https://www.gup.ugal.ro/ugaljournals/index.php/tmb/article/view/1775
Section
Articles

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