Optimization of Surface Roughness Based on Multi-Linear Regression Model and Genetic Algorithm
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.
Zhengyi Jiang and Chunliang Zhang
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