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

Song, Y. / Yu, Z.

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.

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

Song, Y. / Yu, Z.

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.

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

Song, Y. / Yu, Z.

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.

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Title:

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

Additional title:

Archiv.Civ.Mech.Eng

Contributors:

Song, Y. (author) / Yu, Z. (author)

Published in:

Archives of Civil and Mechanical Engineering ; 13 ; 229-241

Publication date:

2013-06-01

Size:

13 pages

ISSN:

DOI:

https://doi.org/10.1016/j.acme.2012.11.004

Type of media:

Article (Journal)

Type of material:

Electronic Resource

Language:

English

Keywords:

T-section beam , Springback prediction , Loading stroke , Residual stress distributions Information and Computing Sciences , Artificial Intelligence and Image Processing , Engineering , Civil Engineering , Mechanical Engineering , Structural Materials

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