Paper Title:
Optimization of Surface Roughness Based on Multi-Linear Regression Model and Genetic Algorithm
  Abstract

During the high-speed milling operations of 7050-T7451 aluminum alloy using solid carbide end mills, helical angle, axial and radial depth-of-cut have significant effects on the milling uniformity. A surface roughness predictive model of work-piece was developed by using a full-factorial experimental design and multi-linear regression technology. Genetic algorithm was utilized to optimize the helical angle and cutting parameters by means of a series of operations of selection, crossover and mutation based on genetics. The result shows that it is possible to select optimum axial depth-of-cut, radial depth-of-cut and helical angle for obtaining minimum cutting force and reasonably good metal removal rate.

  Info
Periodical
Advanced Materials Research (Volumes 97-101)
Edited by
Zhengyi Jiang and Chunliang Zhang
Pages
3050-3054
DOI
10.4028/www.scientific.net/AMR.97-101.3050
Citation
Y. Z. Pan, J. Zhao, X. L. Fu, X. Ai, "Optimization of Surface Roughness Based on Multi-Linear Regression Model and Genetic Algorithm", Advanced Materials Research, Vols. 97-101, pp. 3050-3054, 2010
Online since
March 2010
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Price
$32.00
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