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1 Research Paper EXPERIMENTAL INVESTIGATION AND OPTIMIZATION IN TURNING OF UD-GFRP COMPOSITE MATERIAL BY REGRESSION ANALYSIS USING PCD TOOL Surinder Kumar a *; Meenu b and P.S. Satsangi c Address for Correspondence a * Research Scholar, b Associate Professor, Department of Mechanical Engineering, National Institute of Technology, Kurukshetra , India. c Associate Professor, Department of Mechanical Engineering, PEC University of Technology, Chandigarh , India. ABSTRACT Glass fibre-reinforced plastics (GFRP) composite materials are one of the important materials and are economic alternative to engineering materials because of their superior properties. This paper presents an effective approach for the optimization of turning parameters based on the Taugchi s method with regression analysis. Taguchi s L 16, 2-level orthogonal array is used for the experimentation. The turning parameters such as cutting speed, depth of cut, environment (dry and wet cutting) and feed rate. Response table and response graph are used for the analysis. The effect of turning parameters on surface roughness is evaluated and the optimum cutting condition for minimizing the surface roughness is determined. The experimental results reveal that the most significant machining parameter for surface roughness is feed rate followed by cutting environment (dry and wet). The results are confirmed by further experiments. KEYWORDS UD-GFRP Composites, Response table, ANOVA, Surface Roughness, polycrystalline diamond (PCD) tool, Taguchi method, Regression modelling. 1. INTRODUCTION Composite materials have the potential to play an important role in the drive for product miniaturization. Their appeal comes from the unique material property enhancements that can be achieved by using appropriate combinations of the reinforcing phase (carbon fiber, alumina, etc.) and matrix phase (polymer, ceramic, metals, etc.). Composite materials are used extensively due to specific properties such as light weight, high stiffness, high specific strength, high specific modulus compared to metals. Conventional machining practices, such as turning, drilling and milling are widely applied to the machining of composite materials in view of the availability of equipment and experience in conventional machining. Glass fiber reinforced plastic (GFRP), an advanced composite material, is widely used in a variety of applications including aircraft, house building, storage tanks, robots, machine tools and piping. The advantages include high strength to weight ratio, high fracture toughness, 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 adopt them as an alternative to conventional materials. GFRP pipe made by filament wind technique require further machining to facilitate dimensional control for easy assembly and control of surface quality for functional aspects [1]. Everstine et al. [2] 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. The machining of GFRP composites is different from conventional materials. The behaviour of composites is anisotropic. The quality of machined products depends upon the fibers, matrix materials used, bond strength between fiber and matrix, type of weave etc. Unlike the machining of traditional materials, problems which are typical for FRP machining encountered are as delimitation of the material, especially when fibers are not backed up adequately by the filler materials, differential expansion, fuzzy surface due to thermal conductivity between the fibers and filler material damaging of the cutting tool considerably [3, 4]. Kevlar fibers reinforced plastics (KFRP) machined surface exhibit poor surface finish due to the fussiness caused by delaminated, dislocated and strain ruptured tough Kevlar fibers [4]. Konig et al. [5] found that measurement of surface roughness in FRP is less dependable than in metal, because protruding fiber tips may lead to incorrect result or at least to large variation of the reading. Fereirra et al. [6] also reported that a diamond tool could be used for finish machining as it produces low surface roughness with minimum tool wear. Lee [7] investigated the machinability of glass fiber reinforced plastics by means of tool made of various materials and geometries. Three parameters such as cutting speed, feed rate and depth of cut were selected and measurements were taken by the Kistler (9257B) piezoelectric dynamometer. Single crystal diamond, poly crystal diamond and cubic boron nitride were used for turning process. It was found that, the single crystal diamond tool is excellent for GFRP cutting. Palanikumar et al. [8] developed a procedure to assess and optimize the chosen factors to attain minimum surface roughness by analysis of variance (ANOVA) technique taking interaction into consideration. Sakuma et al. [9] measured cutting resistance and surface roughness for analyzing the machinability and tool wear in face turning of glass fibre-reinforced plastics. They also studied the effect of fibre orientation on both the quality of the machined surfaces and tool wear. Isik et al. [10] proposed an approach for turning of a glass fiber reinforced plastic composites using cemented carbide tool. Three parameters such as depth of cut, cutting speed and feed rate were selected to minimize the tangential and feed force measurements. Weighting techniques was used. The idea of this technique consists in adding all the objective functions together using different coefficients for each. It means that multicriteria

2 optimization problem is changed to a scalar optimization problem by creating one function. It was found that this technique will be more economical to predict the effect of different influential combination of parameters. Hussain et al. [11] developed a surface roughness prediction model for the machining of GFRP pipes using response surface methodology by using carbide tool (K20). Four parameters such as cutting speed, feed rate, depth of cut and work piece (fiber orientation) were selected to minimize the surface roughness. It was found that, the depth of cut shows a minimum effect on surface roughness as compared to other parameters. Kini et al. [12] proposed an approach for turning of a glass fiber reinforced plastic composites using coated tungsten carbide inserts. Four parameters such as depth of cut, tool nose, cutting speed and feed rate were selected to minimize the surface roughness. It was concluded that the same surface roughness for different material removal rate can be obtained using the overlaid contour graphs. Palanikumar [13] predicted and evaluated the surface roughness of GFRP work piece using response surface method. Four parameters such as work piece (fiber orientation), depth of cut, feed rate and cutting speed were selected to minimize the surface roughness. Coated cermets tool was used for turning process. Scanning electron microscope was used for micrographs. Palanikumar [14] evaluated the effect of cutting parameters on the surface roughness of the GFRP composites using PCD tool. Three parameters such as cutting speed, feed rate and depth of cut were selected to minimize the surface roughness. It was found that, for achieving good surface finish on the GFRP work piece, high cutting speed, and high depth of cut is to be used. Depth of cut shows minimum effect on surface roughness compared to other parameters. Rajasekaran et al. [15] used fuzzy logic for modeling and prediction of CFRP work piece. Three parameters such as depth of cut, feed rate and cutting speed were selected to minimize the surface roughness. Cubic boron nitride tool was used for turning process. Scanning electron microscope was used for micrographs. It was found that the fuzzy logic modeling technique can be effectively used for the prediction of surface roughness in machining of CFRP composites. GFRP is a cheaper option than Carbon or Kevlar, so GFRP rods were used in this work. Advantages of GFRP are [16]: more compatible with resin and timber, high resistance to corrosion, useful in a humid or acid environment, improved performance due to better resin bonding, more light weight connection, hence easier handling. The aim of this study is to use statistical multiple regression method to derive predictive models for surface roughness in machining of UD-GFRP. In this paper Taugchi s DOE approach is used to analyze the effect of turning process parameters - (cutting speed, depth of cut, cutting environment (dry and wet) and feed rate) are considered. 2 EXPERIMENT DETAIL 2.1 Material and Method In the present study, Pultrusion processed unidirectional glass fiber reinforced composite rods is used. The fiber used in the rod is E-glass and resin used is epoxy and properties of material are shown in Table 1. Table 1 Mechanical and Thermal Properties of the UD - GFRP material The composite specimens are 840 mm is length and diameter of the rod is 42 mm. All the turning experiments were conducted on a NH22 lathe machine with the following specifications: a height of center 220 mm, swing over bed 500 mm, spindle speed range rpm, feed range mm /rev and main motor 11 kw. Tool holder SVJCR steel EN47 was used during the turning operation. Polycrystalline diamond insert was used for machining. The surface roughness was measured by using Tokyo Seimitsu Surfcom 130A type instrument as shown in Figure 1. Figure 1 Surface Roughness Tester: - Tokyo Seimitsu surfcom 130A From the literature and the previous work done, the independent controllable predominant machining parameters identified were: (1) cutting speed (A), (2) depth of cut (B), (3) cutting environment parameters (dry and wet) (C) and (4) feed rate (D).Out of which cutting environment parameters (dry and wet) was especially applied to composite rods. The machining tests were carried out dry and wet, (using water - soluble cutting fluid). Sufficient care was taken to remove the highly abrasive UD-GFRP machining chips by directing the coolant on the rod. The cutting environment (dry and wet) on the workpiece was set during the machining of the rods, so as to get a comparative assessment of the performance of cutting environment which has not been studied earlier. The feasible ranges of the above factors were arrived and the factors were set at two different levels. The notations units and their levels chosen are summarized in Table 2. The L 16 OA (DOF = 15) was thus selected for the present case study as shown in Table 3. All possible combinations of levels were included so that there are 2 n (where n refers to the number of machining parameters i.e., 2 4 =16) trials in the experiment.

3 Table 2 Control parameters and their level Table 3 Experimental layout using L 16 orthogonal array Table 4 Test data summary for surface roughness Table 5 Response Table for Surface roughness (S/N Ratio) at different Factor Levels Table 6 Response Table for Surface roughness (Mean) at different Factor Levels The quality characteristic in surface roughness is taken as of Lower the better type. The S/N ratio for the Lower the better type of response can be computed [17, 18] as: Smaller the better: S/N = 10 Log Eq. (1) Where n is the number of observations, y is the observed data. The quality characteristic to be studied is surface roughness. The experimentally collected data are then subjected to optimization using ANOVA obtained from regression analysis. Table 4 shows average surface roughness and S/N ratio for all the 16 trials. The response (surface roughness and S/N ratios) of various parameters at different levels are reported in Table 5 and Table 6 respectively.

4 3. ANALYSIS OF RESULTS AND DISCUSSION Glass fibre reinforced composite materials are finding increased applications in many engineering field. From the analysis of the literature, it has been known that the mechanism of GFRP machining is different from the metals. Combinations of plastic deformation, shearing and bending rupture are the common criteria observed during the machining of these composites. The mechanism of machining depends on the flexibility, orientation and toughness of the fibers used in the composite materials [18, 19]. The present study deals with the optimization of turning process parameters in turning of UD-GFRP composites. Our objective is to minimize the surface roughness. Typically, lower value of surface roughness, is desirable for getting good quality in turning of UD- GFRP composites. The analysis on effect of cutting parameters on turning composite materials is more important. The influence of cutting parameters such as cutting speed, depth of cut, environment (dry and wet cutting), and feed rate on turning of UD- GFRP composites is analyzed by using response graphs. The response graphs are drawn with the use of response table with average response values. The response table for surface roughness is presented in Table 5 and Table 6. From the analysis of the table, it has been asserted that feed rate is the highly influential parameters which affect the responses in turning of UD-GFRP composites. The influence of each machining parameter on surface roughness and S/N ratio can be more clearly presented by means of a response graph as shown in Figure 2. Fig. 2 (a) shows the effect of cutting speed and S/N ratio on surface roughness in turning of UD-GFRP composites. The results indicated that the increase of cutting speed reduces the surface roughness observed at (m/min). The influence of turning parameters on surface roughness in turning UD-GFRP composite materials is presented in Fig. 2 (b). The figure indicated that the decrease of depth of cut reduces the surface roughness. The roughness observed at 0.2 mm is more than the surface roughness observed at 1.4 mm. The effect of cutting parameters (cutting environment: wet) and S/N ratio on surface roughness is presented in Fig. 2 (c). The figures indicated that the cutting environment (wet) to reduces the surface roughness. The influence of turning parameters on surface roughness in turning UD-GFRP composite materials is presented in Fig. 2 (d). The results also indicate that the surface roughness increased linearly at higher feed rates. The reason being, the increase in feed rate increases the surface roughness and fracture of the composite materials. The response graph shows the change in the response when the factor goes from its level 1 to level 2. Based on the response graph and response table, the optimal machining parameters for the UD-GFRP machining process is achieved for the minimum value of surface roughness. The optimal conditions are: (1) cutting speed at level 2 (25.33 m/min), (2) depth of cut at level 1 (0.2 mm), (3) cutting environment (wet) at level 2, (4) feed rate at level 1 (0.1 mm/rev). For analysing the results, statistical analysis of variance is used. The purpose of the analysis of variance (ANOVA) is to analyse which machining parameters significantly affect the performance characteristic. Table 7(A) and 7(B) shows the mean pooled ANOVA and S/N ratio ANOVA pooled version respectively It is clear from ANOVAs that the parameters A, C and D (cutting speed, cutting environment (dry and wet) and feed rate respectively) significantly affect both the mean value as well as the variation of the surface roughness because these are significant in both the ANOVAs. The interaction (A * D) is significant in S/N ANOVA for raw data only and hence affects mean value of surface roughness only. The percent contributions of parameters as quantified under column P of Table 7(A) and 7(B) reveal that the influence of feed rate in affecting surface roughness is significantly larger than the cutting speed and environment. The percent contributions of cutting speed (11.69 %), environment (17.13 %) and feed rate (62.08 %) in affecting the variation of surface roughness are significantly larger (95 % confidence level) as compared to the contribution of the depth of cut as shown by Table 7 (A). For analyzing the significant effect of the parameters on the quality characteristics, F and P test is used. This analysis was carried out for a level of significance of 5%, i.e. for a level of confidence of 95%. From the ANOVA result, it is concluded that A Cutting speed, C environment, D feed rate have significant effect on surface roughness. Depth of cut (B) and interactions (AB, AC, BD, AD, BC, CD has no effect at 95% confidence level. It is found that feed rate is more significant factor than other parameters, whilst cutting speed is the least significant parameter. The surface roughness produced on the UD-GFRP workpiece is mainly due to the feed rate. Insignificant parameters are not taken into consideration this regression modelling. Table 7(A) Pooled ANOVA (raw data: Surface roughness)

5 Table 7(B) S/N Pooled ANOVA (raw data: Surface roughness) Figure 2 Response graphs for machining parameters (a) Response and S/N ratio effects for cutting speed, (b) Response and S/N ratio effects for depth of cut, (c) Response and S/N ratio effects for dry and wet and (d) Response and S/N ratio effects for feed rate. 3.1 Development of empirical expression by regression Empirical expression for the unidirectional glass fiber reinforced plastics process is developed to evaluate the relationship between the input parameter with the surface roughness. The functional relationship between dependent output parameter with the independent variables under investigation could be postulated by Equation 2. Y = A (X 1 ) a (X 2 ) b (X 3 ) c Equation (2) Where, Y is dependent output variable such as surface roughness. X₁, X₂ and X₃ are independent variables such as cutting speed, cutting environment (dry and wet) and feed rate. The constants a, b and c are the exponents of independent variables. To convert the above non linear equation into linear form, a logarithmic transformation is applied into the above equation and written as Equation 3. Log y = log A + a. log(x₁) + b. log(x₂) + c.log (X₃) Equation (3) This is one of the most popularly used data transformation methods for empirical model building. Now the above equation is written as Equation 4. η = β 0 + β₁.x₁ + β₂.x₂ + β₃.x₃ Equation (4) Where, η is the true value of dependent surface roughness on a logarithmic scale, x₁, x₂ and x₃ are logarithmic transformation of the different parameters respectively, while β 0, β₁, β₂ and β₃ are the corresponding parameters to be estimated. Due to the experimental error, the true response η = y-ε, where y is the logarithmic transformation of the measured surface roughness parameters and the ε is the experimental error. For simplicity the equation is rewritten as Ŷ = b 0 + b₁x₁ + b₂x₂ + b₃x₃ Equation (5) Where Ŷ is the predicted surface roughness value after logarithmic transformation and b₀, b₁, b₂ and b₃ are the estimates of the parameters, β₁, β₂ and β₃ respectively. The values of b 0, b₁, b₂ and b₃ are found out by linear regression analysis, (second order model) which is conducted with MINITAB standard version software (MINITAB 15.0 for windows), using the experimental data. The first order model for surface roughness reveals lack of fitness due to high prediction errors for surface roughness. As a result, the below mentioned second order model has been developed and its form is given below. Ŷ = b 0 + b 1 x 1 + b 2 x 2 + b 3 x 3 + b 12 x 1 x 2 + b 13 x 1 x 3 + b 23 x 2 x b 11 x 1 + b 22 x 2 + b 33 x 3 Equation (6) x 2 1, x 2 2 2, x 3 are highly correlated with other x variables so x 2 1, x 2 2 and 2 x 3 are removed from Equation. Insignificant parameters are not taken into consideration as shown in Table 8. The developed empirical model by regression analysis for surface roughness is given below Ŷ = x 1 + ( ) x x x 1 x x 1 x 3 + ( ) x 2 x 3

6 Predicted output values for surface roughness are calculated with the help of above equation and the given coefficients as shown in Table 8. It has been seen that relative error of surface roughness are well within limits. Thus, it can be stated that empirical equation build by using second -order model can be used. Relative error between predicted and measured observed values for surface roughness is calculated and presented in Table 9. Table 8 Empirical expressions developed by second order model six variables Table 9 Comparison between observed and predicted values of surface roughness Table 10 Validation between experimental and predicted results 4. CONFIRMATION EXPERIMENTS The experimental study is carried out to validate the earlier developed empirical expressions for surface roughness. Cutting speed is least significant for surface roughness as observed for ANOVA Table 7 (A). So cutting speed remained constant at m/min respectively for validation and other parameter put the same level are shown in Table 3. Figure 3 Comparison between actual and predicted values of surface roughness To verify the goodness of the predicted model, the observed values and their predictive values of the surface roughness are given in the Table 10. Table 10 also shows the prediction error of output parameters i.e. surface roughness. It has been seen that the maximum and minimum error percentage for surface roughness is % and %, which is very much satisfactory. Comparison between actual and predicted values of surface roughness is shown in Figure CONCLUSIONS Experiments were conducted on a lathe on unidirectional glass fiber reinforced plastics (UD-GFRP) specimens with polycrystalline diamond tool material. The data for surface roughness was collected under different cutting conditions. The percent contributions of cutting speed (13.96 %), environment (14.09 %) and feed rate (63.32 %), in affecting the variation of surface roughness are significantly large (95 % confidence level) as compared to the contribution of the depth of cut. Feed rate is the factor, which has great influence on surface roughness, followed by environment. From the ANOVA result, it is concluded that A Cutting speed, C environment, D feed rate, have significant effect on surface roughness while depth of cut (B) and interactions (AB, AC, BD, AD, BC, CD) has no effect at 95% confidence level. It is found that feed rate is more significant factor than other parameter, whilst cutting speed is the least significant parameter. Multiple regression equations are formulated for estimated predicted value of surface roughness for a specified range. REFERENCES 1. Bhatnagar N., Ramakrishnan N., Naik, NK., Komandurai R.; On the machining of fiber Reinforced plastics (FRP) composite laminates, Int. J. Machine Tool Manuf.; vol.35, No5, 1995, PP Evestine GC., Rogers TG.; A Theory of machining of fiber reinforced materials, J. Comp. Mater.; vol. 5, 1971, PP Santhanakrishnan G., Krishnamurthy R., Malhotra SK.; High speed steel tool wear studies in machining of glass fibre-reinforced plastics, wear, vol. 132, 1989, PP Santhanakrishnan G., Krishnamurthy R., Malhotra SK., Machinability characteristics of fibre reinforced plastics composites, Journal of Mechanical Working Technology, vol. 17, 1988, PP Konig W., Wulf Ch., Grab P., Willerscheid H.; Machining of fibre reinforced plastics, CIRP Ann, 34, 1985, PP Fereirra.J.R, Coppini N.L, Levy Neto, Characteristics of carbon carbon composite turning, Journal of Materials Processing Technology, 109 (2001) Lee ES.; Precision machining of glass fibre reinforced plastics with respect to tool characteristics, Int. J. Adv. Manuf. Technol.; vol.17, 2001, PP Palanikumar K., Davim JP.; Assessment of some factors influencing tool wear on the machining of glass fibre-reinforced plastics by coated cemented carbide tools, journal of materials processing technology, vol. 209, 2009, PP Sakuma By Keizo., Seto Masafumi.; Tool wear in cutting glass fiber reinforced plastics (the relation between fiber orientation and tool wear), Bulletin of the JSME, vol. 26, No. 218, 1983, PP

7 10. Isik B., Kentli A.; Multicriteria optimization of cutting parameters in turning of UD-GFRP materials considering sensitivity, Int. J. Adv. Manuf. Technol.; vol.44, 2009, PP Hussain SA, Pandurangadu V., Palanikumar K.; Surface roughness analysis in machining of GFRP composite by carbide tool (K20), European Journal of Scientific Research, vol.41, No.1, 2010, PP Kini MV., Chincholkar AM.; Effect of machining parameters on surface roughness and material removal rate in finish turning of ± 30 glass fibre reinforced polymer pipes, Material and Design, vol. 31, 2010, PP Palanikumar K.; Modeling and analysis for surface roughness in machining glass fibre reinforced plastics using response surface methodology, Materials and Design, vol. 28, 2007, PP Palanikumar K.; Application of Taguchi and response surface methodologies for surface roughness in machining glass fiber reinforced plastics by PCD tooling, International Journal of Advanced Manufacturing Technology, vol. 36, 2008, PP Rajasekaran T., Palanikumar K., Vinayagam BK.; Application of fuzzy logic for modeling surface roughness in turning CFRP composites using CBN tool, Prod. Eng. Res. Deve.; vol. 5 (2011) Harvey K., Ansell Martin P.; Improved timber connections using bonded-in GFRP rods, Department of Materials Science and Engineering, University of Bath, Bath, UK, Ross P.J., Taguchi Techniques for Quality Engineering, McGraw-Hills Book Company, New York, (1988) 18. Roy R. K., A Primer on Taguchi Method, Van Nostrand Reinhold, New York, (1990)