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Prediction the Deformation in Upsetting Process by Neural Network and Finite Element
Abstract:In this paper back-propagation artificial neural network (BPANN) is employed to predict the deformation of the upsetting process. To prepare a training set for BPANN, some finite element simulations were carried out. The input data for the artificial neural network are a set of parameters generated randomly (aspect ratio d/h, material properties, temperature and coefficient of friction). The output data are the coefficient of polynomial that fitted on barreling curves. Neural network was trained using barreling curves generated by finite element simulations of the upsetting and the corresponding material parameters. This technique was tested for three different specimens and can be successfully employed to predict the deformation of the upsetting process
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1061579Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1230
 N. Selvakumar, P. Ganesan, P. Radha, , R. Narayanasamy, and K.S. Pandey, "Modelling the effect of particle size and iron content on forming of Al-Fe composite preforms using neural network", Materials & Design, Volume 28, Issue 1. 2007, Pages 119-130
 Siamak Serajzade, "Prediction of temperature distribution and required energy in hot forging process by coupling neural networks and finite element analysis",,MaterialsLetters Volume 61, Issues 14-15
 Siamak Serajzadeh"Prediction of thermo- mechanical behavior during hot upsetting using neural networks" Materials Science and Engineering in press 2008, Pages 140-147
 Zhihong Huanga, Margaret Lucasa, Michael J. Adam Modelling wall boundary conditions in an elasto-viscoplastic material forming processJournal of Materials Processing Technology 107 (2000) 267-275.
 Elman, J. L., "Finding structure in time", Cognitive Science, vol. 14, pp.179-211,1990
 j.SI," theory and application of supervised learning method based on gradiant algorithms", J tsinghau univ.vol 37,1997
 M.T.HAGEN,"training feed forward network with the levenbergmarquardt algorithm", IEEE, pp 989-993, 1994.