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

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.

[1]  U. Natarajan,et al.  Prediction of surface roughness in CNC end milling by machine vision system using artificial neural network based on 2D Fourier transform , 2011 .

[2]  George-Christopher Vosniakos,et al.  Prediction of surface roughness in CNC face milling using neural networks and Taguchi's design of experiments , 2002 .

[3]  James Tannock,et al.  The optimisation of neural network parameters using Taguchi’s design of experiments approach: an application in manufacturing process modelling , 2005, Neural Computing & Applications.

[4]  Rosita Guido,et al.  Prediction of incremental sheet forming process performance by using a neural network approach , 2011 .

[5]  Uday S. Dixit,et al.  A neural-network-based methodology for the prediction of surface roughness in a turning process , 2005 .

[6]  Jian Cao,et al.  Experimental Implementation of Neural Network Springback Control for Sheet Metal Forming , 2003 .

[7]  N. Ramakrishnan,et al.  Finite Element Analysis of sheet metal bending process to predict the springback , 2010 .

[8]  Haci Saglam,et al.  Tool wear monitoring in bandsawing using neural networks and Taguchi’s design of experiments , 2011 .

[9]  B. S. Lim,et al.  Optimal design of neural networks using the Taguchi method , 1995, Neurocomputing.

[10]  Luís Menezes,et al.  Study on the influence of work-hardening modeling in springback prediction , 2007 .

[11]  Bertil Enquist,et al.  Identification of material hardening parameters by three-point bending of metal sheets , 2006 .

[12]  H. Bhadeshia,et al.  Residual stress. Part 2 – Nature and origins , 2001 .

[13]  Jun Bao,et al.  Effect of the material-hardening mode on the springback simulation accuracy of V-free bending , 2002 .

[14]  N. Ramakrishnan,et al.  An analysis of springback in sheet metal bending using finite element method (FEM) , 2007 .

[15]  Mehmet Firat,et al.  Prediction of springback in wipe-bending process of sheet metal using neural network , 2009 .

[16]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[17]  Mohammad Bakhshi-Jooybari,et al.  Modeling of spring-back in V-die bending process by using fuzzy learning back-propagation algorithm , 2011, Expert Syst. Appl..

[18]  Tongxi Yu,et al.  On springback after the pure bending of beams and plates of elastic work-hardening materials—III , 1981 .

[19]  Jianhua Mo,et al.  Springback prediction of high-strength sheet metal under air bending forming and tool design based on GA–BPNN , 2011 .

[20]  Franc Kosel,et al.  Elasto-Plastic Springback of Beams Subjected to Repeated Bending/Unbending Histories , 2011 .

[21]  Jun Li,et al.  Establishment and Application of Load-Deflection Model of Press Straightening , 2004 .

[22]  A. A. El-Domiaty,et al.  Determination of stretch-bendability limits and springback for T-section beams , 2001 .

[23]  P. J. García Nieto,et al.  Nonlinear analysis of residual stresses in a rail manufacturing process by FEM , 2009 .

[24]  A. P. Polyakov,et al.  Mathematical model of rail straightening and experimental estimation of its adequacy , 1994 .