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Bending Angle Prediction Model Based on BPNN-Spline in Air Bending Springback Process

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  • Zhefeng Guo
  • Wencheng Tang

Abstract

In order to rapidly and accurately predict the springback bending angle in V-die air bending process, a springback bending angle prediction model on the combination of error back propagation neural network and spline function (BPNN-Spline) is presented in this study. An orthogonal experimental sample set for training BPNN-Spline is obtained by finite element simulation. Through the analysis of network structure, the BPNN-Spline black box function of bending angle prediction is established, and the advantage of BPNN-Spline is discussed in comparison with traditional BPNN. The results show a close agreement with simulated and experimental results by application examples, which means that the BPNN-Spline model in this study has higher prediction accuracy and better applicable ability. Therefore, it could be adopted in a numerical control bending machine system.

Suggested Citation

  • Zhefeng Guo & Wencheng Tang, 2017. "Bending Angle Prediction Model Based on BPNN-Spline in Air Bending Springback Process," Mathematical Problems in Engineering, Hindawi, vol. 2017, pages 1-11, February.
    • Handle: RePEc:hin:jnlmpe:7834621
    DOI: 10.1155/2017/7834621
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