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

Abstract:

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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 et al., "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

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Price:

$35.00

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