Paper Title:
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.

  Info
Periodical
Key Engineering Materials (Volumes 431-432)
Edited by
Yingxue Yao, Dunwen Zuo and Xipeng Xu
Pages
543-546
DOI
10.4028/www.scientific.net/KEM.431-432.543
Citation
X. H. Zhang, G. G. Guo, M. Chen, B. Rong, B. Han, G. Liu, Y. S. Zhang, "Cutting Parameters Optimization by Fuzzy Synthetic Evaluation and BP Neural Network in Milling Aluminum Alloy", Key Engineering Materials, Vols. 431-432, pp. 543-546, 2010
Online since
March 2010
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Yan Huang, Jia Qiang E, Chen Tao, Zhi Qiang Wang
Chapter 3: Communication, Information Science and Data Processing, Mechatronics and Control
Abstract:Network security assessment is a complex multi-criteria problem including both quantitative and qualitative factors. In this paper, taken the...
286
Authors: Abhijit Saha, Himadri Majumder
Abstract:Turning is one amongst the most adequate and cost-effective method in manufacturing environment. To meet the difficulties of extensive...
24
Authors: A.K. Parida, K.P. Maity
Abstract:In the present work DEA (data envelopment analysis) coupled with Taguchi method has used for optimization in process parameters of hot...
57