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

Zhefeng Guo and Wencheng Tang

Mathematical Problems in Engineering, 2017, vol. 2017, 1-11

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.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:7834621

DOI: 10.1155/2017/7834621

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