Open AccessOptimization of Cutting Conditions in End Milling Process with the Approach of Particle Swarm Optimization
Author(s) -
Vikas Pare,
Ganga Agnihotri,
Chimata Murali Krishna
Publication year - 2011
Publication title -
international journal of mechanical and industrial engineering
Language(s) - English
Resource type - Journals
ISSN - 2231-6477
DOI - 10.47893/ijmie.2011.1025
Subject(s) - particle swarm optimization , surface roughness , end milling , range (aeronautics) , macro , mathematical optimization , variable (mathematics) , process (computing) , response surface methodology , process engineering , computer science , surface finish , production (economics) , multi swarm optimization , mechanical engineering , engineering , mathematics , machining , materials science , machine learning , economics , mathematical analysis , aerospace engineering , composite material , programming language , operating system , macroeconomics
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 roughnessat 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. Particle swarm optimization technique is used for finding the optimum set of values of input variables and the results are compared with those obtained by GA optimization in the literature.