Experimental Investigation and Optimization of Milling Parameters in the Machining of NEMA G -11 GFRP Composite Material using PCD Tool

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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 223-1

2 Experimental Investigation and Optimization of Milling Parameters in the Machining of NEMA G -11 GFRP Composite Material using PCD Tool relational grade.balamugundan et al. (2012) used desirability function analysis to optimize surface roughness and delamination during milling of GFRP/Epoxy composites.palanikumar (2008) discussed the use of fuzzy logic for modeling machining parameters in machining glass fiber reinforced plastics by poly-crystalline diamond tool. The cutting parameters selected were cutting speed, feed, and depth of cut. The output responses considered for the investigation were surface roughness parameters. The model predicted values and measured values were found fairly close to each other. Erkan et al. (2012) predicted the damage factor in end milling of glass fibre reinforced plastic composites using artificial neural network model. The extant literature survey reveals that woven fabric based NEMA G-11 GFRP composites have not been widely explored for their machining characteristics, despite their wide applications. 2. Methodology The methodology used for this study is as shown below in Fig.1. can only be revealed if more than two levels of the parameters are considered (Byrne & Taguchi, 1987). Thus, each selected parameter is analyzed at three levels.in the present study, three factor at three levels and one factor at two levels were selected,so thatany mix level design available for this combination couldbe considered and hence L18 OA was selected. The factors are assigned to column no, 1, 2,3 and 6 respectively. The unassigned columns are treated as error (See table 3).Each trial was repeated onceand also the trials were carried out in random order. The selected cutting parameters and their assigned values of levels are listed in Table 1. Table 1 Factor and levels (L-1,L-2,L-3) for milling process. Symbol A B C D Cutting parameter Type of milling Spindle Speed (rpm) Feed Rate (mm/min) Depth of cut (mm) L-1 L-2 L-3 Down Up The edge milling experiments were carried out on a vertical HAAS (USA), Model TM-2CNC machine centre, with following technical specifications: Axes travel 1016 x 406 x 406 mm(x,y,z), Spindle speed4000 rpm direct speed and Horse power 7.5 KW. The set up used for milling is as shown in Fig.2. Figure 1 Methodology used for the study. 3. Experimentation The experiments were planned using Taguchi s design of experiments (DOE). The nonlinear relationship among the process parameters, if it exists, Figure 2: Experimental set up

3 The work material selected for this experiment is NEMA G11 GFRP/Epoxy composite. The E-glass reinforcement is of woven fabric form, having following specifications. Type of weave: plain, weight: 180±5 gm/m 2 and of 0.18mm thickness. The work specimens are rectangular in shape, 150 mm long, 50 mm width and 8mm thickness.this material is an excellent insulating material for transformer and inductive appliances. It has good dimensional stability and very good mechanical (Table 2) and dielectric properties at elevated temperature. Table 2 Material Properties Key characteristics of NEMA G11 GFRP/Epoxy Specific gravity 1.91g/cc Rockwell hardness 110 M scale Tensile strength Mpa Compressive strength 344.5Mpa Shear strength 144.7Mpa Tool wear caused by the reinforcing fibres is an important factor, when costing the machining process. Diamond, or more usually polycrystalline diamond (PCD) tools are the best choice for fibre reinforced composites machining, in terms of tool wear, resulting in longer tool life and generating surfaces of better characteristics. The cutting tool used is polycrystalline diamond (PCD) end mill with two flutes and 10mm diameter.components of machining forces (See Fig.3) in the workpiece such as Fx, Fy and Fz. were measured with Kistler piezoelectric dynamometer of type-5233a with built in, charge amplifier up to 10KN and a least count of 1mN. Data acquisition was accomplished by connecting this dynamometer to computer and using KistlerDynoware type- 2825A software. sampling length of 0.8mm and evaluation length of 4mm.Data acquisition wasaccomplished by connecting this profiler to computer and using SESURF software. Figure 4Sample roughness profile; 4. Grey relational analysis In grey relational analysis, the data pre-processing is the first step performed to normalize the random grey data with different measurement units to transform them to dimensionless parameters. Thus, data pre-processing converts the original sequences to a set of comparable sequences. Different methods are employed to preprocess grey data depending upon the quality characteristics of the original data(deng J.L. 1989). The original reference sequence and pre-processed data (comparability sequence) are represented by ( ) ( ), and ( ), =1,2,.. ; =1,2,. respectively, where, m is the number of experiments and n is the total number of observations of data. If the original data has the quality characteristic as smaller the better, then original data is pre-processed as smaller the best : ( )= ( ) ( ) ( ) ( ) (1) Next step is the calculation of deviation sequence, Δ ( ) from the reference sequence of pre-processes data ( ) and the comparability sequence ( ).The grey relational coefficientis calculated from the deviation sequence using the following relation: Figure 3 Sample graph of cutting forces The surface roughness measurements were done using Taylor Hobson Talysurf-5 as shown in Fig.4The roughness profile assessment was conducted according to ISO standard, which employs a Gaussian filter, a ( ), ( ) = + Δ Δ ( )+ Δ (2) Where,Δ ( )is the deviation sequence of the reference sequence ( )and comparability sequence ( ) Δ ( )= ( ) ( ) (3) 223-3

4 Experimental Investigation and Optimization of Milling Parameters in the Machining of NEMA G -11 GFRP Composite Material using PCD Tool Table 3 Experimental plan with resultant machining force and surface roughness response Expt Run Factors Measured parameters A B C D F(N) Ra (µm) Expt. Run Table 4Calculated GRC and GRG Normalization Deviation Grey relational coefficient & GRG F(N) Ra ( µm) F(N) Ra ( µm) F(N) Ra (µm) GRG Rank

5 is the distinguishing coefficient [0,1] The distinguishing coefficient ( )value is chosen to be 0.5. The grey relational grade (, ) represents the degree of correlation between the reference and comparability sequences. Additionally,grey relational coefficients are averaged to obtain the grey relational grade. Grey relational coefficients and grey relational grade are calculated and listed in the table Result and Analysis From Table 4, it is found that for experiment no 13, machining parameter setting is the optimal parameter setting for attaining multiple performances, simultaneously among 18 experiments. However, the relative importance among the achieving parameters for the multiple performance characteristics still needed to be analyzed so that the optimal combinations of the milling parameter levels could be determined more clearly. For analyzing the results, mean analysis was used and is presented as response table (See table 5). Table 5 Response table for means Level 1 Level 2 Level 3 Max- Rank A B C D The procedure for the response table is to group the GRCs by factor levels and then average them. The optimal level of the milling parameters is the level with the greatest GRG value. Based on the results obtained from Fig.5, the optimal parameters achieved are milling type at level 2, spindle speed at level 2, feed rate at level 3and depth of cut at level 1 for achieving better surface roughness and resultant machining force. The optimal solution A2B2C3D1 found in this study is only the near optimal solution. Figure 5 Response graph for GRG 6. Conclusion In this study, GRA has been developed towards optimizing the resultant machining force andsurface roughness in edge milling of NEMA G11glass fiber reinforced epoxy composites. The basic idea of GRA is to find a GRG, which can be used for the optimization conversion from a multi objective case to a single objective case. GRG is also used to estimate the parameter effects on the overall performance response. By this way, a multi-response optimization problem has been converted to an equivalent single objective optimization problem, which has been further solved by Taguchi philosophy. Experiments revealed that from the surface roughness point of view, the most appropriate optimal parameter in case of edge milling are up milling, spindle speed at 1500 rpm, feed rate at 200 mm/min and depth of cut at 0.2mm. Milling strategy and feed rate have the most dominant roles in influencing the surface roughness. Also, a minimum value of surface roughness has been obtained for the up milling area. Acknowledgements The authors would like to thank the Dr. BabasahebAmbedkar Technology University (BATU), Lonere, Maharashtra and Institute for Design of Elecrtical Measuring Instruments (IDEMI),Chunabhatti,Sion, Mumbai for the permission and support given during the experimental work. References 1. Azmi A I, Lin R J T and Bhattacharyya D, 2013 Machinability study of glass fibre reinforced polymer composites during end milling. Int. J. Adv. Manuf. Technol. 64(1): Balamugundan B., L. Karthikeyan and V.S. Senthilkumar, 2012 Multi characteristics optimization during milling of friction stir processed GFRP composites Procedia Engineering 38, Deng, J.L., 1989 Introduction to Grey system. Journal of Grey System, 1: Işık B. &Kentli A., 2009, Multicriteria optimization of cutting parameters in turning of UD-GFRP materials considering sensitivity, Int J AdvManufTechnol, 44, Panneerselvam K, K. Pradeep and P. Asokan, 2012 Optimization of end milling parameters for glass fiber reinforced plastic using grey relational analysis Procedia Engineering 38, Palanikumar K. (2008) Surface roughness model for machining glass fiber reinforced 6. Palanikumar K.., 2009 Surface roughness model for machining glass fiber reinforced plastics by PCD tool using fuzzy logics, Journal of Reinforced Plastics and Composites, 28:18,