Paper Titles
Green, Flexible, Intelligent: Clamping System Based on Thermal Sensitive Material with Volume Effect
p.1028
An Integrated Method for Product Material Selection Considering Environmental Factors and a Case Study
p.1032
A New Approach to Achieve Lean Production in Workshop-Production Preparation Package
p.1036
A CAS Approach for the Coordination and Optimization Problem in Supply Chains
p.1040
Prediction of Spring Back of the Two-Axle Rotary Shaping Based on Neural Network
p.1044
Research on Man-Machine Cooperation Manipulator and Information Integrated Technology in Advanced Manufacturing
p.1048
An Exploratory Study of Logistics Service Quality Model
p.1052
A Study on Multi-Colony Diploid Immune Algorithm and Its Application in Manufacture
p.1056
A Study on the Aviation Manufacture Cell Scheduling Based on Adaptive Ant Colony Algorithm
p.1060

Prediction of Spring Back of the Two-Axle Rotary Shaping Based on Neural Network

Article Preview

Abstract:

Two-axle rotary shaping is one of advanced sheet metal forming process that combined stamping ascendant used elastic medium with traditional rotary shaping principle. The prediction model of two-axle rotary shaping is set up to predict the springback for two-axle rotary shaping. It used the back propagation neural network because of the better nonlinear mapping ability. Some of data from the experiment and FEM simulation is applied to train the network; the other data is used to test the prediction result. The result showed that the value of prediction and experiment is in good agreement, and just small error is existed. It demonstrated that the neural network model might predict the springback of two-axle rotary shaping and reduce the number of simulation calculation and experiment operation. It can offer a powerful guidance for rapid choice of process parameters in production.

You might also be interested in these eBooks
Advances in Materials Manufacturing Science and Technology II View Preview

Info:

Periodical:

Materials Science Forum (Volumes 532-533)

Pages:

1044-1047

DOI:

https://doi.org/10.4028/www.scientific.net/MSF.532-533.1044

Citation:

Cite this paper

Online since:

December 2006

Authors:

Shi Hong Lu, Jing Wang

Keywords:

Artificial Neural Network (ANN), Process Parameter, Springback, Two-Axle Rotary Shaping

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

© 2006 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

References

[1] J. Wang, X. Wu, and P.F. Thomson: Mater. Process. Technol., Vol. 105 (2000), pp.215-220.

Google Scholar

[2] Forcellese, F. Gabrielli and R. Ruffini: Mater. Process. Technol., Vol. 80-81 (1998), pp.493-500.

Google Scholar

[3] M.V. Inamdar, P.P. Date and U.B. Desai: Mater. Process. Technol., Vol. 108 (2000), pp.45-54.

Google Scholar

[4] H.B. Ridha and G.R. Fabrice: Finite Elements in Analysis and Design, Vol. 39 (2003), pp.1039-1052.

Google Scholar

[5] P. Korczak, H. Dyja and E. L, Abuda: Mater. Process. Technol., Vol. 80-81 (1998), pp.481-486.

Google Scholar

[6] D.J. Kim and B.M. Kim: Mach. Tools Manuf., Vol. 40 (2000), pp.911-925.

Google Scholar

[7] I. M. Zakirov, A.G. Martyanov and K. Ružička: STU Bratislava, (1997).

Google Scholar

[8] G.Q. Yu: Theory and Finite Element Analysis on the Two-axle Rotary Shaping (Master Paper, Nanjing University of Aeronautics and Astronautics, 2005).

Google Scholar