Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2017, Article ID 7834621, 11 pages
https://doi.org/10.1155/2017/7834621
Research Article

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

Department of Mechanical Engineering, Southeast University, Nanjing 211189, China

Correspondence should be addressed to Wencheng Tang; nc.ude.ues@cwgnat

Received 16 October 2016; Revised 16 January 2017; Accepted 7 February 2017; Published 27 February 2017

Academic Editor: Marek Lefik

Copyright © 2017 Zhefeng Guo and Wencheng Tang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.