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

Yong Zhi Pan; Jun Zhao; Xiu Li Fu; Xing Ai

doi:10.4028/www.scientific.net/AMR.97-101.3050

Paper Titles

Simulation of Aluminium Foam Compressive Properties
p.3033

Moment Stability of Stochastic Regenerative Cutting Process
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Simulation of Microstructure Evolution for Two-Phase Ceramic Tool Materials at Different Duration Time
p.3042

An Integration-Based Stochastic Finite Element Method
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Optimization of Surface Roughness Based on Multi-Linear Regression Model and Genetic Algorithm
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A New Ultraprecision Machining Method with Softness Abrasive Flow Based on Discrete Phase Model
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Research on Precision Forming Technology of Car Steering Knuckle
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Finite Element Analysis on the Roll Milling Process of Twisted Oblate Tube
p.3065

Mechanical Analysis on the Inner Surface Crack of Modified 9Cr-1Mo Seamless Steel Tubes Rolled by Mandrel Mill
p.3070

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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.