Research on Error Model of the Al5052 Sheet Stamping Forming Parts

Li Zhi Gu; Yun Ma; Qian Deng

doi:10.4028/www.scientific.net/KEM.667.149

Paper Titles

Test and Analysis of Electric Arc Machining Characteristics of Titanium Alloy and High-Temperature Alloy
p.123

Research on Surface Quality for CBN Grinding Wheel Based on “Speed Effect”
p.130

Simulation and Experiment Study on Cutting Temperature in Diamond Ultra-Precision Cutting of Aluminum Alloy Mirror
p.136

Study on Influence Laws of Surface Roughness of Micro-Groove in Single Crystal Silicon under Diamond Fly-Cutting
p.142

Research on Error Model of the Al5052 Sheet Stamping Forming Parts
p.149

Test and Analysis of Low Temperature Air Cutting Performance to Difficult-to-Machine Materials
p.155

The Experiment Investigation of the Tool Wear in Diamond Ultra-Precision Cutting of Aluminum Alloy Mirror
p.162

The Influence of Welding Sequence on Welding Deformation of Sheet Metal Stamping
p.168

Dynamic Model of Material Removal Process in Through-Feed Centerless Grinding Based on the Lagrange Equation
p.173

HomeKey Engineering MaterialsKey Engineering Materials Vol. 667Research on Error Model of the Al5052 Sheet...

Research on Error Model of the Al5052 Sheet Stamping Forming Parts

Abstract:

Abstract. How to improve the precision of the stamping forming has been one of the stamping researchers concern in the stamping technology.This paper analysis the material stamping performance of AL5052 aluminum alloy sheet,study the influencing factors of forming precision,and find the the mapping relationship between influence factors and stamping forming to effectively predict the forming error .The first part is to analyze the factors that affect the forming process, then get the main factors that influence the part of stamping forming. And then establish the main influence factors^’ mathematical model of stamping forming error based on BP neural network, and through the training of BP neural network to prove the model^’s practicability.

You might also be interested in these eBooks

Advanced Engineering Materials and Processing Technologies

View Preview

Info:

Periodical:

Key Engineering Materials (Volume 667)

Pages:

149-154

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.667.149

Citation:

Cite this paper

Online since:

October 2015

Authors:

Li Zhi Gu*, Yun Ma, Qian Deng

Keywords:

AL5052, BP Neural Network, Influence Factors, Material Punching Performance, Mathematical Model

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] M.Y. Yang, X.P. Sun and Z.Y. Hu: Die and Mould Technology, (2006)No. 1, pp.3-7.

Google Scholar

[2] X.W. Lai: Research on Springback Forecasting of Sheet Metal Stamping based on a Combination Forecasting Method of Artificial Neural Network (Diplom, Zhejiang University, in chinese, 2006). pp.23-28.

Google Scholar

[3] O. Ghouati, D. Joannic, J.C. Gelin: Numiform(The Netherlands, 1998. 9), (1998). pp.819-824.

Google Scholar

[4] B. Carleer: Numiform(Dearborn, USA, 1996. 10), (1996). pp.25-32.

Google Scholar

[5] H. Naceur, Y.Q. Guo, J.L. Batoz, S. Bouabdallah,: Numiform (Besancon, France, 1999. 7). Vol. 1(1999). p.517–522.

Google Scholar

[6] Y.C. Zhu, X.D. Wu and M. Wan: Forging & Stamping Technology, Vol. 37 (2012) No. 5, pp.31-34.

Google Scholar

[7] J.H. Huang, X.F. Li, J.J. Rao, X.Z. Xiao and X.H. Dong: China Mechanical Engineering, Vol. 14(2003) No. 2, pp.145-148.

Google Scholar

[8] W.G. Chen: Technology and test, Vol. 12 (2002), pp.50-52.

Google Scholar

[9] X.B. Zhang, Y. Sun H.M. Fan and L.N. Jia: Forging & stamping technology, Vol. 32 (2007) No. 6, pp.6-12.

Google Scholar