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Springback prediction in T-section beam bending process using neural networks and finite element method

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Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper, three point bending method is used for the T-section beam bending process. The prediction model of springback is developed using artificial neural network approach. The corresponding loading stroke that can theoretically eliminate the residual deflection of a beam after springback is determined. Application examples indicate that the proposed approach could achieve an allowable straightness error. Numerical simulations using finite element method are also performed to investigate the effect of material properties on springback. A neural network for identification of material parameters is developed by the simulation data. Besides, the residual stress distributions across the beam section are analyzed. The finite element model is validated with experimental results of springback.
Rocznik
Strony
229--241
Opis fizyczny
Bibliogr. 24 poz., rys., tab., wykr.
Twórcy
autor
  • Department of Mechanical Engineering, Zhejiang University, Hangzhou 310027, PR China
autor
  • Department of Mechanical Engineering, Zhejiang University, Hangzhou 310027, PR China, caq_221@zju.edu.cn
Bibliografia
  • [1] A.A. Elsharkawy, A.A. El-Domiaty, Determination of stretch-bendability limits and springback for T-section beams, Journal of Materials Processing Technology 110 (2001) 265–276.
  • [2] I.F. Volebov, A.P. Polyakov, S.V. Kolmogorov, Mathematical model of rail straightening and experimental estimation of its adequacy, Journal of Materials Processing Technology 40 (1994) 213–218.
  • [3] X.C. Li, Y.Y. Yang, Y.Z. Wang, J. Bao, S.P. Li, Effect of the material-hardening mode on the springback simulation accuracy of V-free bending, Journal of Materials Processing Technology 123 (2002) 209–211.
  • [4] M.C. Oliveira, J.L. Alves, B.M. Chaparro, L.F. Menezes, Study on the influence of work-hardening modeling in springback prediction, International Journal of Plasticity 23 (2007) 516–543.
  • [5] S.K. Panthi, N. Ramakrishnan, M. Ahmed, S.S. Singh, M.D. Goel, Finite Element Analysis of sheet metal bending process to predict the springback, Materials and Design 31 (2010) 657–662.
  • [6] S.K. Panthi, N. Ramakrishnan, K.K. Pathak, J.S. Chouhan, An analysis of springback in sheet metal bending using Finite Element Method (FEM), Journal of Materials Processing Technology 186 (2007) 120–124.
  • [7] W. Johnson, T. Yu, On springback after the pure bending of beams and plates of elastic work-hardening materials III, International Journal of Mechanical Sciences 23 (10) (1981) 687–695.
  • [8] F. Kossel, T. Videnic, T. Kosel, M. Brojan, Elasto-plastic springback of beams subjected to repeated bending/unbending histories, Journal of Materials Engineering and Performance 20 (2011) 846–854.
  • [9] J. Li, H.J. Zou, G.L. Xiong, Establishment and application of load-deflection model of press straightening, Key Engineering Materials 274–276 (2004) 475–480.
  • [10] V. Viswanathan, B. Kinsey, J. Cao, Experimental implementation of neural network springback control for sheet metal forming, Journal of Engineering Materials and Technology Transactions of the ASME 125 (2) (2003) 141–147.
  • [11] R. Kazan, M. Firat, A.E. Tiryaki, Prediction of springback in wipe-bending process of sheet metal using neural network, Materials and Design 30 (2) (2009) 418–423.
  • [12] Z.M. Fu, J.H. Mo, Springback prediction of high-strength sheet metal under air bending forming and tool design based on GA–BPNN, International Journal of Advanced Manufacturing Technology 53 (2011) 473–483.
  • [13] H. Baseri, M. Bakhshi-Jooybari, B. Rahmani, Modeling of spring-back in V-die bending process by using fuzzy learning back-propagation algorithm, Expert Systems with Applications 38 (2011) 8894–8900.
  • [14] F.C. Khaw, B.S. Lim, L.E.N. Lim, Optimal design of neural networks using Taguchi method, Neurocomputing 7 (1995) 225–245.
  • [15] P.G. Benardos, G.C. Vosniakos, Prediction of surface roughness in CNC face milling using neural network and Taguchi’s design of experiment, Robotics and Computer-Integrated Manufacturing 18 (2002) 343–354.
  • [16] A. Kohli, U.S. Dixit, A neural-network-based methodology for the prediction of surface roughness in a turning process, International Journal of Advanced Manufacturing Technology 25 (2005) 118–129.
  • [17] G. Ambrogio, L. Filice, F. Guerriero, R. Guido, D. Umbrello, Prediction of incremental sheet forming process performance by using a neural network approach, International Journal of Advanced Manufacturing Technology 54 (2011) 921–930.
  • [18] K. Hornik, M. Stinchhcombe, H. White, Multilayer feedforward networks are universal approximators, Neural Networks 68 (1989) 359–366.
  • [19] W. Sukthomya, J. Tannock, The optimization of neural network parameters using Taguchi’s design of experiments approach: an application in manufacturing process modeling, Neural Computing & Applications 14 (2005) 337–344.
  • [20] H. Saglam, Tool wear monitoring in bandsawing using neural networks and Taguchi’s design of experiments, International Journal of Advanced Manufacturing Technology 55 (2011) 969–982.
  • [21] S. Palani, U. Natarajan, Prediction of surface roughness in CNC end milling by machine vision system using artificial neural network based on 2D Fourier transform, International Journal of Advanced Manufacturing Technology 54 (2011) 1033–1042.
  • [22] E. Omerspahice, K. Mattiasson, B. Enquist, Identification of material hardening parameters by three-point bending of metal sheets, International Journal of Mechanical Sciences 48 (2006) 1525–1532.
  • [23] C.B. Biempica, J.J.D. Diaz, P.J.G. Nieto, I.P. Sanchez, Nonlinear analysis of residual stresses in a rail manufacturing process by FEM, Applied Mathematical Modelling 33 (1) (2009) 34–53.
  • [24] P.J. Withers, H.K.D.H. Bhadeshia, Overview residual stress Part 2 nature and origins, Materials Science and Technology 17 (2001) 366–375.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-98a06eb2-4ed6-419e-a120-1ea1c8a0b31c
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