JME_V4_N2_RP2 K. Rajasekhar N. Naresh Journal on Mechanical Engineering 2249 - 0744 4 2 18 26 Milling, Modeling, Taguchi Orthogonal Array, Fuzzy Logic, Surface Roughness, MRR; Tungsten Carbide End Mill, ANOVA Analysis of surface roughness and Material Removal Rate play vital roles in machining Austenitic steels. Austenitic stainless steels have high ductility, low yield stress and relatively high ultimate tensile strength, when compared to typical carbon steel. In the present work, prediction of surface roughness and material removal rate in milling of AISI 304 stainless steel is carried out using fuzzy logic. 27 experiments based on Taguchi's L orthogonal array were carried out 27 involving three machining parameters namely, cutting speed, feed rate and depth of cut, each defined at three levels. Subsequently, the prediction model was developed using triangular fuzzy logic membership function. It is found that the predicted values of proposed responses such as surface roughness and Material Removal Rate are very close to the experimental values within the chosen ranges of the process parameters. The statistical analysis using ANOVA on machining parameters are also presented and discussed. February - April 2014 Copyright © 2014 i-manager publications. All rights reserved. i-manager Publications http://www.imanagerpublications.com/Article.aspx?ArticleId=2681