Integration of feedforward neural network and finite element in the draw-bend springback prediction

نویسندگان

  • M. R. Jamli
  • A. K. Ariffin
  • D. A. Wahab
چکیده

To achieve accurate results, current nonlinear elastic recovery applications of finite element (FE) analysis have become more complicated for sheet metal springback prediction. In this paper, an alternative modelling method able to facilitate nonlinear recovery was developed for springback prediction. The nonlinear elastic recovery was processed using back-propagation networks in an artificial neural network (ANN). This approach is able to perform pattern recognition and create direct mapping of the elasticallydriven change after plastic deformation. The FE program for the sheet metal springback experiment was carried out with the integration of ANN. The results obtained at the end of the FE analyses were found to have improved in comparison to the measured data. 2013 Elsevier Ltd. All rights reserved.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Effect of Computational Parameters on Springback Prediction by Numerical Simulation

Elastic recovery of the material, called springback, is one of the problems in sheet metal forming of drawpieces, especially with a complex shape. The springback can be influenced by various technological, geometrical, and material parameters. In this paper the results of experimental testing and numerical study are presented. The experiments are conducted on DC04 steel sheets, commonly used in...

متن کامل

Evaluation of Ultimate Torsional Strength of Reinforcement Concrete Beams Using Finite Element Analysis and Artificial Neural Network

Due to lack of theory of elasticity, estimation of ultimate torsional strength of reinforcement concrete beams is a difficult task. Therefore, the finite element methods could be applied for determination of strength of concrete beams. Furthermore, for complicated, highly nonlinear and ambiguous status, artificial neural networks are appropriate tools for prediction of behavior of such states. ...

متن کامل

Numerical and Experimental Investigations on Springback of U-bending of DP600 Steel Alloy Sheet

The most prominent feature of sheet material forming process is an elastic recovery phenomenon during unloading which leads to springback and side wall curl. Therefore evaluation of springback and side wall curl is mandatory for production of precise products. In this paper, the effects of some parameters such as friction coefficient, sheet thickness, yield strength of sheet and blank-holder fo...

متن کامل

Neural Network Prediction of Nonlinear Elastic Unloading for High Strength Steel

In achieving accurate results, current nonlinear elastic recovery applications of finite element (FE) analysis have become more complicated for sheet metal springback prediction. In this paper, an artificial neural network (ANN) was used to mimic the nonlinear elastic recovery and provides a generalized solution in the FE analysis. The nonlinear elastic recovery was processed through back-propa...

متن کامل

Flow Variables Prediction Using Experimental, Computational Fluid Dynamic and Artificial Neural Network Models in a Sharp Bend

Bend existence induces changes in the flow pattern, velocity profiles and water surface. In the present study, based on experimental data, first three-dimensional computational fluid dynamic (CFD) model is simulated by using Fluent two-phase (water + air) as the free surface and the volume of fluid method, to predict the two significant variables (velocity and channel bed pressure) in 90º sharp...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • Expert Syst. Appl.

دوره 41  شماره 

صفحات  -

تاریخ انتشار 2014