INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 12, No. 3, pp. 383-391 JUNE 2011 / 383 DOI: 10.1007/s12541-011-0050-7 NOMENCLATURE N = spindle speed f = feed rate d = depth of cut R a = surface roughness 1. Introduction Surface roughness is an important measure of product quality since it greatly influences the performance of mechanical parts as well as production cost. Surface roughness has an impact on the mechanical properties like fatigue behavior, corrosion resistance, etc. and functional attributes like friction, wear, light reflection, heat transmission and electrical conductivity, etc. There have been many research developments in modeling surface roughness and optimization of the controlling parameters to obtain a surface finish of desired level since only proper selection of cutting parameters can produce a better surface finish. In the manufacturing industries, various machining processes are adapted for removing the material from the workpiece for a better product. Out of these, end milling process is one of the most vital and common metal cutting operations used for machining parts because of its ability to remove materials faster with a reasonably good surface quality. In recent times, computer numerically controlled (CNC) machine tools have been implemented to utilize full automation in milling since they provide greater improvements in productivity, increase the quality of the machined parts and require less operator input. A brief review of literature on surface roughness modeling in milling is presented here. Surface roughness and dimensional accuracy have been important factors in predicting the machining performances of any machining operation. 1 Kline et al. 2 investigated the effect of vibration, deflection and chatter of the tool-workpiece system on roughness in end milling. Alauddin et al. 3 developed the mathematical model of surface roughness for the end milling of 190 BHN steel considering only the centre line average (CLA) roughness parameter (R a ) in terms of cutting speed, feed rate and depth of cut using response surface method (RSM). Fuht and Wu 4 studied using RSM the influence of tool geometries (nose radius and flank width) and cutting parameters (cutting speed, feed rate, depth of cut) on surface roughness in end milling of Al alloy. Chun and Ko 5 studied machining error caused by tool deflection in the internal boring process using RSM. Chen 6,7 and his co-workers studied the effect of spindle speed, feed rate and depth of cut on R a Optimization of Cutting Conditions for Surface Roughness in CNC End Milling Kantheti Venkata Murali Krishnam Raju 1,# , Gink Ranga Janardhana 2 , Podaralla Nanda Kumar 3 and Vanapalli Durga Prasada Rao 1 1 Department of Mechanical Engineering, S. R. K. R. Engineering College, Bhimavaram, A. P., India, 534 204 2 Department of Mechanical Engineering, J. N. T. U. College of Engineering, Kakinada, A. P., India, 533 003 3 Department of Mechanical Engineering, Narayanadri Institute of Science and Technology, Rajampet, A. P., India, 516 115 # Corresponding Author / E-mail: raju_kvm@yahoo.com, TEL: +91-09441169979, FAX: +91-08816-224516 KEYWORDS: End milling, Cutting parameters, Surface roughness, Multiple regression, Genetic algorithm, Optimization The aim of this research is to develop an integrated study of surface roughness to model and optimize the cutting parameters when end milling of 6061 aluminum alloy with HSS and carbide tools under dry and wet conditions. A multiple regression analysis using analysis of variance is conducted to determine the performance of experimental measurements and to show the effect of cutting parameters on the surface roughness. The second-order mathematical models in terms of machining parameters have been developed for each of these conditions on the basis of experimental results. Genetic algorithm (GA) supported with the regression equation is utilized to determine the best combinations of cutting parameters providing roughness to the lower surface through optimization process. The value obtained from GA is compared with that of experimental value and found reliable. It is observed from the results that the developed study can be applied to other machining processes operating under different machining conditions. Manuscript received: July 10, 2010 / Accepted: January 26, 2011 © KSPE and Springer 2011