Cutting Parameters Optimization by Fuzzy Synthetic Evaluation and BP Neural Network in Milling Aluminum Alloy

Abstract:

Aluminum alloy, as a kind of large-scaled structures, have been widely used in modern aerospace industry. In order to reduce its machining deformation, cutting parameter optimization is absolutely necessarily. By fuzzy synthetic evaluation, cutting parameters are optimized based on factors: surface roughness, residual stress, radial milling force and milling temperature. By maximal grade of membership rule, optimized values are obtained by different two methods. And by BP network with Bayesian regularization method the corresponding milling parameters are obtained too.

• Periodical: Key Engineering Materials (Volumes 431-432)
• Pages: 543-546
• DOI: https://doi.org/10.4028/www.scientific.net/KEM.431-432.543
• Online since: March 2010
• Authors: Xiao Hui Zhang, Guo Giang Guo, Ming Chen, Bin Rong, Bing Han, Gang Liu, Yun Shan Zhang
• Keywords: Fuzzy Synthetic Evaluation, Milling, Neural Network (NN), Parameter Optimization

References

[1] T.Y. Wang, N. He and L. Li: Transactions of Nanjing University of Aeronautics & Astronautics, Vol. 24 (2007), pp.112-117.

[2] A.J. Tang and Z. Q Liu: Journal of Materials Processing Technology, (2008), pp.1-7.

[3] G. Onkal-Engin, I. Demir and H. Hiz: Atmosphere environment, Vol. 38 (2004), pp.3809-3815.

DOI: 10.1016/j.atmosenv.2004.03.058

[4] J.F. Briceno, H. El-Mounayri and S. Mukhopadhyay: International Journal of Machine Tools & Manufacture, Vol. 42 (2002), pp.663-674.

DOI: 10.1016/s0890-6955(02)00008-1

[5] W.S. Hao, X.S. Zhu, X.F. Li and G. Turyagyenda: Journal of Materials Processing Technology, Vol. 180 (2006), pp.23-29.