5 th International & 26 th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12 th –14 th , 2014, IIT Guwahati, Assam, India 223-1 Experimental Investigation and Optimization of Milling Parameters in the Machining of NEMA G -11 GFRP Composite Material using PCD Tool Hari Vasudevan 1 , Ramesh Rajguru 2* , Naresh Deshpande 3 Sandip Mane 4 1 Principal, D.J. Sanghvi College of Engineering, Mumbai, India, harivasudevan@iitb.ac.in 2* Faculty, Department of Mechanical Engineering, D.J. Sanghvi College of Engineering, Mumbai, India,ramesh.rajguru9@gmail.com 3,4 Faculty, Department of Production Engineering, D.J. Sanghvi College of Engineering, Mumbai, India, ncdeshpande72@yahoo.co.in Abstract GFRP/Epoxy composite NEMA G-11 possesses excellent physical, mechanical and electrical properties at both room temperature & elevated temperatures and finds wide applications, such as insulation in aerospace and defense systems. The material withstands temperatures in excess of 300°C and is considered a premier material for use as Class F insulation in electrical power generation and transmission equipments.Milling is one of the most practical machining processes for removing excess material to produce high quality surface. However, milling of composite materials is a rather complex task owing to its heterogeneity and poor surface finish, which includes fibre pullout, matrix delamination, sub-surface damage and matrix polymer interface failure. In this study, an attempt has been made to optimize milling parameters with multiple performance characteristics, based on the Grey Relational Analysis coupled with Taguchi method. The milling experiments were carried out on a vertical HAAS TM-2 CNC Milling machine. The experiments were conducted according to L18 (OA). The four cutting parameters selected for this investigation are milling strategy, spindle speed, feed rate and depth of cut. Response table of grey relational grade for four process parameters is used for the analysis to produce the best output; the optimal combination of the parameters.From the response table of the average GRG, it is found that the largest value of the GRG is for the up milling, spindle speed is of 1500 rpm, feed rate of 200 mm/min and depth of cut 0.2 mm. Milling strategies and feed rate have the most dominant roles in influencing the surface roughness. Keywords:Milling, Surface roughness, Machining force, Grey relational analysis; Taguchi methodology. 1. Introduction Machining aspects in the case ofglass fiber reinforced plastics(GFRP) differ incomparison to that of metals. Machining of most of the homogeneous and ductile metals is characterized by shearing & plastic deformation and the formation of a continuous chip, whereas machining of GFRPs, is characterized by uncontrolled intermittent fracture. Oscillating cutting forces are typical, because of the intermittent fracture of the fibers (Jamal, 2009). The quality of the machined surface depends upon the type of fiber and matrix materials used; type of weave of the fabric etc. Some of the typical problems faced during the machining of GFRP are fibers pull out, matrix debonding, burning, short tool life, powder type chips, high cutting forces and poor surface finish. Cutting forces have a direct effect on power consumption and tool wear. They are oscillating and periodic in nature. The oscillations are generated due to repeated running of cutting tool into fibers and matrix phases. This results in strong variations of magnitudes of cutting forces. In order to achieve good machinability, it is desirable to have minimum values of cutting force. Azmi, Lin &Bhattachayyra(2013) investigated the end milling of glass fiber reinforced polymer (GFRP) composites using uncoated tungsten carbide tool. Machinability data were evaluated in the form of Taylor’s equation in order to predict the tool performance, while machining this composite material.Panneerselvam et al. (2012) studied the effect of machining parameters on end milling of GFRP composites in order to minimize surface delamination, machining forces, cutting torque and surface roughness. The four cutting parameters selected for this study were tool condition, number of flutes, cutting speed and feed rate. These parameters were optimized using grey