Study on Prediction Model of Springback Based on Support Vector Machine

Abstract:

Accurate prediction and effective control springback has a great significance to the plate cold bending forming. Based on the analysis of the support vector machine method principle and structure, Study on support vector machine prediction process based on MATLAB and prediction models about radius and resilience is set up. And the results predicted were compared with BP neural network prediction results to verify the accuracy.To provide an effective method for the accurate prediction springback.

• Periodical: Applied Mechanics and Materials (Volumes 633-634)
• Pages: 734-737
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.633-634.734
• Online since: September 2014
• Authors: Shao Juan Su*, Yong Hu, Cheng Fang Wang
• Keywords: MATLAB, Springback, Support Vector Machine (SVM)

References

[1] TAN Xiao-long. Support vector machine prediction model based on slope displacement monitoring data[J]. Chinese Journal of Geotechnical Engineering, 2009, 31(5): 750-755.

[2] LI Liang- min, WEN Guang- rui, WANG Sheng- chang. Parameters selection of support vector regression based on genetic algorithm[J]. Computer Engineering and Applications, 2008, 44( 7) : 23- 26.

[3] Guo Xiaohui, Ma Xiaoping. Support vector machines toolbox in matlab environment[J]. computer application and software, 2007, 24(12): 57-59.

[4] Roger Stein. Preprocessing date for data for neural network[J]. AI Expert, March, 2001, 25(2): 69-78.