Surface Roughness Analysis in Machining of GFRP Composites by Carbide Tool (K20)

Transcription

1 European Journal of Scientific Research ISSN X Vol.41 No.1 (2010), pp EuroJournals Publishing, Inc Surface Roughness Analysis in Machining of GFRP Composites by Carbide Tool (K20) Syed Altaf Hussain Department of Mechanical Engineering, R.G.M College of Engineering & Technology Nandyal , A.P., India Tel: V. Pandurangadu Departments of Mechanical Engineering, J.N.T.Univrsity, Anantapur , A.P., India Tel: K. Palanikumar Department of Mechanical Engineering, Sri Sai Ram Institute of Technology Chennai-44, T.N, India Tel: Abstract Nowadays, glass fiber reinforced plastics (GFRP) play a vital role in many engineering applications as an alternative to various heavy exotic materials. In GFRP composites, the matrix of polymer (resin) is reinforced with glass fibers. However, the users of FRP are facing difficulties to machine it, because if fiber delamination, fiber pullout, short tool life, matrix debonding, burning and formation of powder like chips. The surface quality and dimensional precision greatly affect the parts during their useful life, especially in cases where the components come in contact with other elements or materials. The present work deals with the study and development of a surface roughness prediction model for the machining of GFRP tubes using Response Surface Methodology (RSM). Experiments were conducted through the established Taguchi s Design of Experiments (DOE) on an all geared lathe using carbide (K20) tool. The cutting parameters considered were cutting speed, feed, depth of cut, and work piece (fiber orientation). A second order mathematical model in terms of cutting parameters was developed using RSM. The results indicate that the developed model is suitable for prediction of surface roughness in machining of GFRP composites. The effect of different parameters on surface roughness are analyzed and presented in this study. Keywords: GFRP composites, Modeling, Response Surface methodology, Surface roughness, Carbide tool (K20).

2 Surface Roughness Analysis in Machining of GFRP Composites by Carbide Tool (K20) Introduction Glass fiber reinforced plastics (GFRP) are increasingly being used for varieties of engineering applications because of their superior advantage over other engineering materials. The advantages include high strength to weight ratio, high fracture toughness and excellent corrosion and thermal resistance. The tailarability of composites for specific applications has been one of their greater advantages and also one of the more perplexing challenges to adopting them as alternative to conventional materials. Even though Glass fiber reinforce polymer (GFRP) tube made by filament wind technique require further machining to facilitate dimensional control for easy assembly and control of surface quality for functional aspects [1]. The users of FRP are facing difficulties when machining it, because knowledge and experience acquired for conventional materials cannot be applied for such new materials, whose machinability is different from that of conventional materials [2]. Thus it is desirable to investigate the behavior of FRPs during the machining process. Everstine and Rogers, [3], proposed an analytical theory of machining FRPs. In a classical study, they developed a theory of plane deformation of incompressible composites reinforced by strong parallel fibers. Bhatnagar et al [4], studied how the fiber orientation influence both the quality of the machined surfaces and tool wear. The machinability of composite materials is influenced by the type of fiber embedded in the composites, and more particularly by the mechanical properties. On the other hand, the selection of cutting parameters and the cutting tool are dependent on the type of fiber used in the composites and which is very important in the machining process. Davim JP, Mata F. [5] studied the influence of cutting parameters on surface roughness in turning glass-fiber reinforced plastics using statistical analysis. Ramulu et al, [6], carried out a study on machining of polymer composites and concluded that higher cutting speeds give better surface finish.tekeyama et al, [7] studied the surface roughness on machining of GFRP composites, according to them, higher cutting speed produce more damage on the machined surface. This is attributed to higher cutting temperature, which results in local softening of work material. They also studied the machinability of FRP composites using the ultra sonic machining technique. According to Koing et al [8], measurement of surface roughness in FRP is less dependable compared to that in metals, because protruding fiber tips may lead to incorrect results. Additional errors may result from the hooking of the fibers to the stylus. Palanikumar et al, [9], studied the effect of cutting parameters on surface roughness on machining of GFRP composites by polycrystalline diamond (PCD) tool by developing a second order model for predicting the surface roughness. Palanikumar et al, [10], have developed a procedure to asses and optimize the chosen factors to attain minimum surface roughness by incorporating response table and response graph, normal probability plot, interaction graphs, and analysis of variance (ANOVA) technique. In this article, effects of cutting parameters on surface roughness on the machining of GFRP composites by carbide (K20) tool are evaluated. A second order quadratic model is developed for predicting the surface roughness in machining of GFRP composites by response surface methodology approach. The predicted and measured values are fairly close to each other. Their proximity to each other indicates the developed models can be effectively used to predict the surface roughness in the machining of GFRP composites. 2. Materials and Methods The work material used for the present investigation is glass fiber reinforced plastics (GFRP) pipes. The inner diameter of the tube is 30mm; outer diameter is 60mm and length 500mm respectively shown in figure 1. The pipes used in the study are manufactured by filament winding process. The orientation of the fibers on the works piece has been set during the manufacture of tubes. The fiber used in the tubeis E-glass and resign used is epoxy. The specification of the material used in this study is given in Table 1.

3 86 Syed Altaf Hussain, V. Pandurangadu and K. Palanikumar Table 1: Specifications of fiber and resign Fiber: E-glass R P556 Resin: Epoxy Manufacturer: Saint Gobain vetrotex India Ltd. Manufacturer: CIBA GEIGY R099- Multi filament Roving Product: ARALDITE MY 740 IN 1200-Linear Density, Tex 110KG Q2 P556- Sizing reference for vetrotex Hardner: HT 972 Figure 1: GFRP Composite Pipe Specimens 2.1. Response Surface Methodology The surface finish of machined GFRP composite parts is important in manufacturing engineering applications which have considerable effect on some properties such as wear resistance, light reflection, heat transmission, coating and resisting fatigue. While machining, quality of the part can be achieved through proper cutting conditions. In order to know the surface quality and dimensional properties in advance, it is necessary to employ theoretical models making it feasible to do prediction in function of operation condition. [11], Response surface methodology (RSM) is a collection of mathematical and statistical techniques that are useful for modeling and analysis of problems in which a response of interest is influenced by several variables and the objective is to optimize this response. In many engineering fields, there is a relationship between an output variable of interest y and a set of controllable variables {x 1,x 2 x n }, in some systems, the nature of relation ship between y and x values might be known. Then, a model can be written in the form Y= f(x 1, x 2, -----Xn ) + ε (1) Where ε represents noise or error observed in the response y If we denote the expected response be E(Y) = f(x 1, x 2, -----Xn ) =Y ) is called response surface. The first step is to find suitable approximation for the true functional relation ship between y and set of independent variables employed usually a second order model is used in RSM. ) k k 2 Y = β 0 + β i X i + β ii X i + β ij X j + ε (2) i= 1 i= 1 i j

4 Surface Roughness Analysis in Machining of GFRP Composites by Carbide Tool (K20) 87 The β coefficients, used in the above model can be calculated by means of using least square method. The second-order model is normally used when the response function is not known or nonlinear Experimental Details The experiments are planned using Taguchi s orthogonal array in the design of experiments (DoE), which helps in reducing the number of experiments. The experiments were conducted according to orthogonal array. The four cutting parameters selected for the present investigation is cutting speed (v)m/min, feed (f)mm/rev, depth of cut (d)mm and work piece (fiber orientation Ф ) in degrees. Since the considered factors are multi-level variables and their outcome effects are not linearly related. Taguchi s orthogonal array of L 25 is most suitable for this experiment [12]. This needs 25 runs and has 24 degrees of freedom (DOFs). The machining parameter used and their levels are shown in Table 2. All the GFRP pipes are turned in a BHARAT all-geared lathe of model NAGMATI-175 with a maximum speed of 1200 rpm and power of 2.25KW. The ISO specification of the toll holder used for the turning operation is a WIDAX tool holder PC LNR 2020 K12 and the tool insert used for the study is carbide (K-20) sandvik make. Table 2: Cutting parameters, their notations and their limits Levels Process parameters with units Notation Variable Speed, m/min v x Feed, mm/rev f x Depth of cut, mm d x Fiber orientation Ф x angle, deg The average surface roughness (Ra), which is mostly used in industrial environments, is takenup for this study. In a composite machined surface, the result of the roughness depends mainly on the stylus path with respect to fiber direction since the main direction of fibers may change from layer to layer. For this reason, the roughness has been measured several times and averaged. The average surface roughness is the integral of the absolute value of the roughness profile height over the evaluation length and is denoted by the following equation. L 1 R a = Y x dx L ( ) (3) 0 Where L is the length taken for observation, and Y is the ordinate of the profile curve. The surface roughness tester (FORM TALY SURF) used in this work is shown in figure:2, with the following specifications: manufacturer: Taylor Hobson, U.K, Traveling length:01mm-50mm, force: 4mN, stylus: Diamond 2µm tip radius, Resolution: 16nm/1.0mm, software: Form ultra software. The design matrix and the experimental results are listed in Table 3.

5 88 Syed Altaf Hussain, V. Pandurangadu and K. Palanikumar Table 3: Experimental Results Coded variables Uncoded variables Average Surface roughness, Trail No. x 1 x 2 x 3 x 4 v f d Ф Ra, μm Figure 2: Surface roughness tester Figure 3:(a) (e) shows the surface roughness variations of the machined surfaces at different machining conditions. The variation in surface roughness, this is due to two different phases in the composite material. The figures have been taken using form ultra software.

6 Surface Roughness Analysis in Machining of GFRP Composites by Carbide Tool (K20) 89 Figure 3: (a) (e) Surface roughness variation with respect to different machining conditions measured using surface roughness tester (FORM TALY SURF). (a) (b) (c)

7 90 Syed Altaf Hussain, V. Pandurangadu and K. Palanikumar (d) (e) 3. Results and Discussion Surface roughness plays a predominant role in determining the machining accuracy. The study of surface roughness characteristics of GFRP composites dependent on many factors, it is more influenced by the cutting parameters like cutting speed, feed, depth of cut, etc., for a given machine tool and work piece set-up. The influence of different cutting parameters on machining of GFRP composites can be studied by using response graph and response table. The influence of cutting parameters on surfaces roughness is shown in Figure 4. The observed surface roughness at high cutting speed is low as compared to low cutting speed. The experimental results indicated that the surface roughness parameter is low at low feed as compared to the high feed. The effect of depth of cut on machining of GFRP composite indicated that the surface roughness reduces with increase of depth of cut. The experimental results indicated that low surface roughness is observed for low fiber orientation angle as compared to high fiber orientation angle. The response table for surface roughness Table 4, shows the effect of different cutting parameters. From the response table, it can be asserted that feed is the main parameters which affect the surface roughness. When GFRP composites are machined, discontinuous chips in powder form are produced, which is entirely different from machining of metals. The machining of GFRP composites differ from machining of metals, because they are anisotropic and inhomogeneous materials.

8 Surface Roughness Analysis in Machining of GFRP Composites by Carbide Tool (K20) 91 The SEM image of the carbide (K-20) tool used in this study is shown in Figure 5. Figure 5 (a) shows the flank face, whereas Figure 5 (b) shows the end face of the cutting tool. As seen from the figures, as the machining is carried out without any coolant, the sediments of molten matrix and /or fiber particles are got welded up on the tool face due to the temperature developed during the machining process. This is similar to that of the built-up edge formation in aluminum machining. Figure 6 shows the SEM photographs of the machined surfaces of GFRP composites at different machining conditions. In Figure 6 (a), in the machined surface voids are observed. These voids are due to the insufficient distribution of resin and fibers in the composite material. Figure 6(a)- (e) shows the SEM images of the GFRP composite work pieces after the cutting operation. These figures show the distribution of glass fibers in the polymer matrix material after the machining operation. Table 4: Response table for surface roughness Level Cutting speed (v),m/min Feed (f), mm/rev Depth of cut (d), mm Fiber orientation angle (Ф) degrees Delta Rank Figure 4: Effect plot for surface roughness

9 92 Syed Altaf Hussain, V. Pandurangadu and K. Palanikumar Figure 5: (a)- (b) SEM images of carbide (K-20) tool insert (a) flank face (b) Side face. (a) (b) Figure 6: (a) (e) SEM images of the machined surface at different cutting conditions Voids in the work piece due to the insufficient distribution of resin (a) (b) (c) (d)

10 Surface Roughness Analysis in Machining of GFRP Composites by Carbide Tool (K20) 93 (e) The experimental values are analyzed using response surface analysis and the following relation has been established for surface roughness (Ra) in uncoded units as: Ra = v f d Ф v f d Ф v*f v*d v*ф f*d f*ф d*ф A result of ANOVA for the response function surface roughness is presented in Table 5. This analysis is carried out for a level of significance of 5% i.e., for a level of confidence of 95%. From the analysis of Table 4, it is apparent that, the F calculated value is greater than the F-table value (F 0.05, 14,10= 2.6) and hence the second order response function developed is quite adequate. Table 5: ANOVA for the response function of the surface roughness Source Degrees of freedom Sum of squares Mean squares F value P value Regression Residual Error Total Figure 7 shows the correlation between the predicted and experimental values for surface roughness (Ra). The difference between the observed values and predicted or fitted values is called residuals. The residuals are calculated and ranked in the ascending order. The normal probabilities of residuals are shown in Figure 8. The normal probability plot is used to verify the normality assumption. As shown in figure 8, the data are spread roughly along the straight line. Hence it is concluded that the data are normally distributed [13].

11 94 Syed Altaf Hussain, V. Pandurangadu and K. Palanikumar Figure 7: Correlation graph R 2 =0.982 Figure 8: Normal Probability plot of residuals. R 2 = For analyzing the influence of cutting parameters in machining of GFRP composites, the surface roughness values are calculated at different machining conditions and are plated as shown in Figures Figure 9 shows the variation of surface roughness with cutting speed for different feeds at constant depth of cut and fiber orientation angle. It can be observed that the surface roughness gradually decreases with increasing the cutting speed up to 145m/min and thereafter it increases slightly. This is because, at higher cutting speed debonding and fiber breakage are the reasons for poor surface roughness. The surface roughness values are gradually increasing with increasing the feed rates. Figure10 show the variation of surface roughness with cutting speed at different depth of cut, keeping the feed rate and fiber orientation angle constant. It is observed from the figure that with the increase in the depth of cut up to 1.0mm, the surface roughness decreases thereafter increases slightly. Figure11 is a graph between cutting speed and surface roughness for various fiber orientation angles, keeping the feed and depth of cut constant. A gradual decrease in surface roughness is observed for various fiber orientation angles at a cutting speed of 145m/min, thereafter increases slightly. Figure12 shows the plot between the feed and surface roughness for different depth of cut values keeping the cutting speed and fiber orientation angle constant. A steady increase in the surface roughness is observed with increase in feed rate. It is observed that lower values of surface roughness are observed at a depth of cut 1.0mm for all the range of feed rates. Figure13 is a plot of feed verses and surface roughness for various fiber orientation angles, keeping the cutting speed and depth of cut constant. A better surface roughness is observed at lower feed and lower fiber orientation angle.

12 Surface Roughness Analysis in Machining of GFRP Composites by Carbide Tool (K20) 95 Figure14 is a graph between depth of cut and surface roughness for various fiber orientation angles, keeping the cutting speed and feed constant. From the graph it is observed that surface roughness is increasing gradually with the increase in the fiber orientation angle and is reducing gradually with the increase in the depth of cut up to a value of 1.0mm and thereafter slightly increasing. From the above figures, it is asserted that moderate cutting speed, low feed, low fiber orientation angle and moderate depth of cut are preferred for machining of GFRP composites. Figure 9: Variation of surface roughness with cutting speed for different feeds at central values of depth of cut and fiber orientation angle. Figure 10: Variation of surface3 roughness with cutting speed for different depth of cut at central values of feed and fiber orientation angle. Figure 11: Variation of surface roughness with cutting speed for different fiber orientation angles at central values of feed and depth of cut.

13 96 Syed Altaf Hussain, V. Pandurangadu and K. Palanikumar Figure 12: Variation of surface roughness with feed for different depth of cut at central values of cutting speed and fiber orientation angle. Figure 13: Variation of surface roughness with feed for different fiber orientation angles at central values of cutting speed and depth of cut. Figure 14: Variation of surface roughness with depth of cut for different fiber orientation angles at central values of cutting speed and feed.

14 Surface Roughness Analysis in Machining of GFRP Composites by Carbide Tool (K20) Conclusions The surface roughness in the turning process has been measured for machining of GFRP composites under different cutting conditions with a carbide (K-20) tool using Taguchi s orthogonal array. Based on the experimental and analytical results the following conclusions are drawn. 1. The developed second-order response surface model can be used to calculate the surface roughness of the machined surfaces at different cutting conditions with the chosen range with 95% confidence intervals. Using such model, one can obtain remarkable savings in time and cost. 2. From the results, it can be asserted that moderate cutting speed, low feed, low fiber orientation angle and moderate depth of cut are preferred for machining of GFRP composites. 3. The feed is the dominant parameter which affects the surface roughness of GFRP composites, followed by cutting speed, fiber orientation angle. Depth of cut shows a minimal effect on surface roughness compared to other parameters. Nomenclature Ra = Average surface roughness value in μm y i = height of surface roughness from the mean value v = cutting speed in m/min f = feed in mm/rev d = depth of cut in mm Ф = fiber orientation angle in degrees. Acknowledgement The authors are highly thankful to M/s ICP India (P) Limited, Bangalore for supplying the GFRP pipes used in this work.

15 98 Syed Altaf Hussain, V. Pandurangadu and K. Palanikumar References [1] Bhatnagar, N., Ramakrishnan, N., Naik, N.K., and. Komandurai, R., 1995, On the machining of fiber Reinforced plastics (FRP) composite laminates, Int J.Machine Tool Manuf., 35 (5), pp [2] Davim, JP., Mata, F,. 2004, Influence of cutting parameters on surface roughness using statistical analysis. Indus Lubrication Tribol, 56(5), pp [3] Evestine, G.C., and. Rogers. T.G, 1971, A Theory of machining of fiber reinforced materials, J. Comp. Mater, 5; pp [4] Koing, W., Wulf, Ch., Grab, P., and Willerscheid, H., 1985, Machining of Fiber Reinforced Plastics Annals of CIRP, 34, pp [5] Montgomery, DC., 1991, Design and analysis of experiments. John Wiley and sons, NewYork. [6] Palanikumar, K.., Karunamoothy, L.., and Karthikeyan, R., 2006, Assessment of Factors Influencing Surface Roughness on the Machining of Glass Fiber- Reinforced Composites, J. of Materials and Design, 27 (10), pp [7] Palanikumar, K., 2008, Application of Taguchi and Response surface Methodology for surface Roughness In Machining Glass Fiber Reinforced Plastics by PCD Tooling, IJMPT, 36 (1-2), pp [8] Ramulu, M., Arola, D., and Colligan, K.., 1994, Preliminary Investigation on the surface Integrity of fiber Reinforced Plastics, Engineering systems Design and Analysis, ASME, 64(2), pp [9] Ross, PJ., 1996 Taguchi techniques for quality engineering. Mc Graw- Hill, New York. [10] Santhana krishanan,g., Krishnamoorthy, R., and Malhotra, SK., 1989, Machinability Characteristics of Fiber Reinforced Plastics Composites., JMWT., (17), pp [11] Shew, YW., Kwong, CK., 2002, Optimization of plated through hole process using experimental design and response surface methodology. Int Adv Manuf Technol, 20, pp [12] Takeyama, H., and Lijama, N., 1988, Machinability of Glass Fiber Reinforced Plastics and Application of Ultrasonic machining, Annal of CIRP, 97 (1),