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Springback prediction and optimization of variable stretch force trajectory in three-dimensional stretch bending process

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Abstract

Most of the existing studies use constant force to reduce springback while researching stretch force. However, variable stretch force can reduce springback more efficiently. The current research on springback prediction in stretch bending forming mainly focuses on artificial neural networks combined with the finite element simulation. There is a lack of springback prediction by support vector regression (SVR). In this paper, SVR is applied to predict springback in the three-dimensional stretch bending forming process, and variable stretch force trajectory is optimized. Six parameters of variable stretch force trajectory are chosen as the input parameters of the SVR model. Sixty experiments generated by design of experiments (DOE) are carried out to train and test the SVR model. The experimental results confirm that the accuracy of the SVR model is higher than that of artificial neural networks. Based on this model, an optimization algorithm of variable stretch force trajectory using particle swarm optimization (PSO) is proposed. The springback amount is used as the objective function. Changes of local thickness are applied as the criterion of forming constraints. The objection and constraints are formulated by response surface models. The precision of response surface models is examined. Six different stretch force trajectories are employed to certify springback reduction in the optimum stretch force trajectory, which can efficiently reduce springback. This research proposes a new method of springback prediction using SVR and optimizes variable stretch force trajectory to reduce springback.

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Authors and Affiliations

  1. State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian, 116024, China

    Fei Teng, Wanxi Zhang, Jicai Liang & Song Gao

  2. School of Automotive Engineering, Dalian University of Technology, Dalian, 116024, China

    Fei Teng, Wanxi Zhang, Jicai Liang & Song Gao

Authors
  1. Fei Teng
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  2. Wanxi Zhang
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  3. Jicai Liang
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  4. Song Gao
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Corresponding author

Correspondence to Jicai Liang.

Additional information

Supported by National Technical Innovation Foundation of China (Grant No. Jilin Province 350)

TENG Fei, born in 1985, is currently a PhD candidate at Automotive Engineering Institute, Dalian University of Technology, China. She received her master bachelor degree from Jilin University, China, in 2011. Her research interests include auto materials processing.

ZHANG Wanxi, born in 1954, is currently a professor at Dalian University of Technology, China. He received his master degree from Changchun Institute of Applied Chemistry, China, in 1982.

LIANG Jicai, born in 1958, is currently a professor at Jilin University, China, in 2001. His research interests include auto materials processing.

GAO Song, born in 1987, is currently a PhD candidate at Automotive Engineering Institute, Dalian University of Technology, China. His research interests include auto materials processing.

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Teng, F., Zhang, W., Liang, J. et al. Springback prediction and optimization of variable stretch force trajectory in three-dimensional stretch bending process. Chin. J. Mech. Eng. 28, 1132–1140 (2015). https://doi.org/10.3901/CJME.2015.0723.100

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  • DOI: https://doi.org/10.3901/CJME.2015.0723.100

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