Bending Angle Prediction Model Based on BPNN-Spline in Air Bending Springback Process. (27th February 2017)

• Record Type: Journal Article
• Authors: Guo, Zhefeng; Tang, Wencheng
• Other Names: Lefik Marek Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2017(2017)
• Publication Date: 2017-02-27
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2017/7834621
• Languages: English
• ISSNs: 1024-123X

Abstract

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.