Study on the Intelligent Control of Springback in Stretch Bending Process Based on Neural Networks

Article Preview

Abstract:

In extrusion stretch bending process, there are many factors which affect springback of the workpiece such as mechanical properties of the material, friction condition and process parameters. The springback of same batch of extrusion is different at same forming parameters because of the variation of the mechanical properties of the material and the friction condition. A method of intelligent control of springback in stretch bending process is proposed by using ANN(artificial neural networks). The online identification model of the mechanical properties of the material and friction coefficient and the online prediction control model of springback of workpiece in stretch bending process are established by using ANN ,which are trained by the data of analysis calculation. It realizes the intelligent control on springback of stretch bending to online identify the material properties and friction coefficient and predict springback and adjust process parameters dynamically through the whole process of stretch bending. The results from the experiment state that the intelligent control method can suit the variation of mechanical properties of material and friction condition and improve the geometry precision.

You might also be interested in these eBooks

Info:

Periodical:

Materials Science Forum (Volumes 532-533)

Pages:

604-607

Citation:

Online since:

December 2006

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2006 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] K.A. Stelson: J. Eng. Ind., Vol. 105 (1983) No. 2, pp.45-53.

Google Scholar

[2] M. Yang, S. Shima and T. Watanabe: Flexible Automation ASME, (1992) No. 2, pp.1485-1490.

Google Scholar

[3] K.L. Elkins: On-Line Angle Control for Small Radius Air Bending (Ph.D. Thesis, Carnegie Mellon University, UMI Dissertation Services 1994).

Google Scholar

[4] M.V. Inamdar, P.P. Date and U.B. Desai: Journal of Materials Processing Technology, Vol. 108 (2000), pp.45-54.

Google Scholar

[5] D.J. Kim and B.M. Kim: International Journal of Machine Tools & Manufacture, Vol. 40 (2000), pp.911-925.

Google Scholar