Optimization of Surface Roughness Based on Multi-Linear Regression Model and Genetic Algorithm

Abstract:

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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 et al., "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:

$35.00

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