*Assiatant Professor, Department of Mechanical Engineering, Excel College of Engineering and Technology, Pallakapalayam, Komarapalayam -637 303, Tamilnadu, India
**Associate Professor, Department of Mechanical Engineering, Excel College of Engineering and Technology, Pallakapalayam, Komarapalayam -637 303, Tamilnadu, India
***Associate Professor, Department of Mechanical Engineering, Excel College of Engineering and Technology, Pallakapalayam, Komarapalayam -637 303, Tamilnadu, India
****PG Scholar, Department of Aeronautical Engineering, Excel Engineering college, Pallakapalayam, Komarapalayam -637 303, Tamilnadu, India
*****UG Scholar, Department of Mechanical Engineering, Excel College of Engineering and Technology, Pallakapalayam, Komarapalayam -637 303, Tamilnadu, India
Online published on 20 July, 2015.
Milling is one of the progressive enhancements of miniaturized technologies which has wide range of application in industries and other related areas. Milling like any metal cutting operation is used with an objective of optimizing surface roughness at micro level and economic performance at macro level. In addition to surface finish, modern manufacturers do not want any compromise on the achievement of high quality, dimensional accuracy, high production rate, minimum wear on the cutting tools, cost saving and increase of the performance of the product with minimum environmental hazards. In order to optimize the surface finish, the empirical relationships between input and output variables should be established in order to predict the output. Optimization of these predictive models helps us to select appropriate input variables for achieving the best output performance. In this paper, four input variables are selected and surface roughness is taken as output variable, Genetic algorithm technique is used for finding the optimum set of values of input variables and the results are compared with REGRESSION ANALYSIS in the literature.
