Optimization of process parameters in drilling of aisi 1015 steel for exit burr u

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Optimization of process parameters in drilling of aisi 1015 steel for exit burr u

  1. 1. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 327 OPTIMIZATION OF PROCESS PARAMETERS IN DRILLING OF AISI 1015 STEEL FOR EXIT BURR USING RSM AND TAGUCHI Mr. Dhanke V. D.(1) Prof. Phafat N.G.(2) Prof. Dr. Deshmukh R. R.(3) (1) Student, M.E. Manufacturing, Mechanical Engineering Department, J.N.E.C. Aurangabad, Maharashtra, India (2)(3) Associate Professor, Mechanical Engineering Department, J.N.E.C. Aurangabad, Maharashtra, India ABSTRACT This paper concentrates on the analysis and optimization of input parameters for minimum burr size using Response surface methodology and Taguchi method. Speed, feed, drill diameter and point angle were taken as input machining parameters while two response variables namely burr height and burr thickness were chosen for the given study. Central composite design (CCD) was used for the experimental design. Optimum machining parameters were determined using S/N ratio obtained from Taguchi optimization method for multi response characteristics i.e. burr height and burr thickness. With this technique minimum burr height were obtained at cutting speed 28 m/min, feed rate 0.1 mm/rev, drill diameter 20 mm and point angle 135° and minimum burr thickness were obtained at cutting speed 16 m/min, feed rate 0.05 mm/rev, drill diameter 4 mm and point angle 142°. Keywords: Burr height, Burr thickness, Grey relational analysis, Drilling, AISI 1015. 1. INTRODUCTION Drilling is one of the important manufacturing operations that can be carried out on number of parts for assembly work. Drilling operation is essential for manufacturing industries like automobile industry, aerospace industry, medical and electrical related industries etc. Formation of exit burr after drilling operation is one of the important problems that have to be minimized or control in order to avoid its adverse effect during manufacturing stage or assembly work. Burr is defined as the projection of material at the end of the hole due to plastic deformation of the material. Burr has two types’ viz. entrance burr and exit burr. The exit burr is important because these are larger in size than entrance burr. Exit burr creates several problems like decreasing quality INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING AND TECHNOLOGY (IJMET) ISSN 0976 – 6340 (Print) ISSN 0976 – 6359 (Online) Volume 4, Issue 4, July - August (2013), pp. 327-337 © IAEME: www.iaeme.com/ijmet.asp Journal Impact Factor (2013): 5.7731 (Calculated by GISI) www.jifactor.com IJMET © I A E M E
  2. 2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 328 of the products, improper seating between mating parts during assembly, jamming and misalignment of parts etc. Burrs are injurious to workers during machining operations. Especially in precision industries, burrs are quiet undesirable. Due to all these problems deburring operation should be carried out on the work piece to remove the burr formed during drilling operation. This will result in waste of time and money. Almost 30% of the total manufacturing cost will be spending on deburring and edge finishing of components. Most of the research work on the burr minimization focused on optimization of parameters for machining various metals and alloys. Gaitonde and Karnik [1] introduce an optimal parameter model using artificial neural network and particle swarm optimization. Gaitonde et al [2] used ANN and genetic algorithm for multi performance optimization. Again Gaitonde et al [3] used Taguchi method for process optimization for drilling burr. Nihal Tosun [4] investgated the effect of drilling parameters on burr height and surface roughness. Erol Kilckap [5] developed a RSM based model in the drilling of Al-7075 for the minimization of burr height and surface roughness. Erol kilckap et al [6] again produce a model for burr height and surface roughness in the drilling of AISI 1045 steel. L. Ken Lauderbaugh [7] use the combined experimental and simulation tool for process optimization. As drilling burr formation is a very complicated phenomenon affected by many parameters such as drill geometry, material property and process conditions. Therefore it is imperative to develop a reliable model that predicts the burr size to reduce deburring cost as burrs cannot be completely eliminated. “However, no generally accepted analytical model is available in drilling up to now”. This investigation work differs from other studies carried out on burr formation as it attempts to develop a prediction model, which determines whether a drill bit consisting of important process parameters yields a minimum burr or not. The important process parameters are determined based on the literature review of previous studies carried out on burr formation. An response surface methodology (RSM) model is developed that predicts burr height and thickness. RSM is selected because of its capability to learn and simplify from examples and adjust to changing conditions. In addition they can be applied in manufacturing area as they are an effective tool to model non linear systems. 2. RESPONSE SURFACE METHODOLOGY RSM consists of the experimental strategy for exploring the space of the process or input factors, empirical statistical modeling to develop an appropriate approximating relationship between the output and the process variables, and optimization methods for finding the levels or values of the process variables that produce desirable values of the response outputs. Response surface method designs also help in quantifying the relationships between one or more measured responses and the vital input factors (MINITAB SOFTWARE version 14). Process modeling by response surface methodology (RSM) using statistical design of experiment based on central composite design is proved to be an efficient modeling tool. The methodology reduces cost of experimentation and provides the information about the main and interaction effects. The first step of RSM is to define the limits of the experimental domain to be explored. These limits are made as wide as possible to obtain a clear response from the model. The cutting speed, feed rate, drill diameter and point angle are the drilling variables selected for present investigation. The different levels retained for this study are depicted in Table 1. In the next step is the planning to accomplish the experiments by means of RSM using a Central Composite Design. In many engineering fields, there is a relationship between an output
  3. 3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 329 variable of interest (y) and a set of controllable variables ),........,( 21 nxxx . The relationship between the drilling control parameters and the responses is given as: ε+= ),........,( 21 nxxxfy (1) where ε represents the noise or error observed in the response (y). If we denote the expected response to be η== ),........,()( 21 nxxxfyF , then the surface represented by ),........,( 21 nxxxf=η (2) is called a response surface. The variables nxxx ,........, 21 in Eq. 2 are called natural variables because they are expressed in natural units of measurement. In most RSM problems, the form of the relationship between the independent variables and the response is unknown; it is approximated. Thus, the first step in RSM is to find an appropriate approximation for the true functional relationship between response and the set of independent variables. Usually, a low-order polynomial in some region of the independent variables is employed. If the response is well modeled by a linear function of the independent variables, then the approximating function is the first order model: εββββ +++++= kkxxxy ..............22110 (3) If there is curvature in the system, then a polynomial of higher degree must be used, such as the second-order model: εββββ +∑∑+∑+∑+= − == jiij k j k i jjj k j jj k j xxxxy 1 2 11 0 (4) Where 1,.....2,1 −= ki and kj ,.........2,1= also ji < 3. EXPERIMENTAL WORK AISI 1015 steel plates with 10 mm thickness were used as a work piece material for the given study. AISI 1015 steel is widely used in the applications like Rivets, Screws, panels, ship plates, boiler plates, fan blades, gear, valves, cam shafts, crank shafts, connecting rods, railway axles, tubes of bicycles and automobiles, small forgings etc. The composition of AISI Steel is 0.178% C, 0.60% Mn, 0.04% Cr, 0.03% Ni, 0.002% Mo, 0.019% S, 0.020% P and 0.19% Si. The Hardness is 170 BHN. Experimental studies were performed on CNC Machining centre HASS VF2SS with Maximum speed, 12000 r.p.m. In the present study four process parameters (speed, feed, drill diameter and point angle) were considered. A five level central composite design was used to study linear, quadratic and two factor interaction effect between the four process variable and responses (Table 1). The upper limit of a factor was coded as +2 and the lower limit as -2; coded values for intermediate levels were calculated from the following relationship: minmax minmax )](2[2 XX XXX Xi − +− = (5) Where Xi is the required coded values of a variable X, X is any value of the variable from Xmin to Xmax. Xmin the lower level of the variable and Xmax is the upper level of the variable.
  4. 4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 330 Table 1 Factors and their levels Factors Levels -2 -1 0 +1 +2 Speed (V), m/min 12 16 20 24 28 Feed (f), mm/rev 0.05 0.1 0.15 0.2 0.25 Drill dia. (D), mm 04 08 12 16 20 Point angle(PA), degree 110 118 126 134 142 4. RESULTS AND DISCUSSION 4.1 Experimental results and Taguchi analysis A series of drilling experiments were conducted to investigate the influence of process parameters on burr height and burr thickness in drilling AISI 1015 steel. Experimental results and S/N ratio for burr height and burr thickness for drilling AISI 1015 steel with various drilling parameters are shown in table 1. The S/N ratios for each experiment were calculated by using equation: Lower is the best, )(log10 2 10 ijy N S −= Where, ijy is the response value at the th i run Now, by utilizing calculated values of S/N ratios (Table 1), average S/N response ratios were calculated for burr height and burr thickness. Table 2 shows S/N response table for burr height and Table 3 shows S/N response table for burr thickness. The S/N ratio response graphs for both responses are shown in Fig 1 and 2. Table 2 S/N response table for burr height Levels S/N ratio for Burr height V f D θ 1 10.43 9.9 10.49 9.92 2 9.85 11.03 9.85 9.87 3 9.46 9.83 9.48 9.71 4 10.03 8.85 10.03 10.01 5 11.54 7.29 11.21 8.83 Table 3 S/N response table for burr thickness Regardless of quality characteristics, a greater S/N ratio corresponds to a better performance. The level of factor with the highest signal-to-noise ratio is the optimum level. Levels S/N ratio for Burr thickness V f D θ 1 20.45 22.5 21.94 19.58 2 21.44 22.1 20.59 20.34 3 20.07 20.08 19.88 19.98 4 20.19 19.52 21.03 21.29 5 20.26 18.06 21.21 22.27
  5. 5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 331 D rilling param eter lev els 11 .5 11 .0 10 .5 10 .0 9 .5 54321 11 10 9 8 7 54321 11 .0 10 .5 10 .0 9 .5 1 0. 00 9. 75 9. 50 9. 25 9. 00 V f D ? MeanS/Nratio D rilling pa ra m e te r le v e l 2 1 .6 2 1 .2 2 0 .8 2 0 .4 2 0 .0 54321 2 2 2 1 2 0 1 9 1 8 54321 2 2 .0 2 1 .5 2 1 .0 2 0 .5 2 0 .0 2 2 2 1 2 0 V f D ? MeanS/Nratio Optimal parameter combination for Burr height: The optimal parameter combination level for the present research work in the drilling process is speed at level 5, feed rate at level 2, drill diameter at level 5 and point angle at level 4 for burr height. That means, the optimal cutting parameters for burr height (Bh) were obtained at 28 m/min cutting speed (level 5), 0.1 mm/rev feed rate (level 2), 20 mm drill diameter (level 5), and 1340 point angle (level 4). Optimal parameter combination for Burr thickness: The optimal parameter combination level for the present work in the drilling process is speed at level 2, feed rate at level 1, drill diameter at level 1 and point angle at level 5 for burr thickness. That means, the optimal cutting parameters for burr thickness (Bt) were obtained at 16 m/min cutting speed (level 2), 0.05 mm/rev feed rate (level 1), 4 mm drill diameter (level 1), and 1420 point angle (level 5). Fig.1 The effect of drilling parameters on burr height Fig. 2 The effect of drilling parameters on burr thickness
  6. 6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 332 Table 4 Experimental design and responses Run Levels of factors Expt. results S/N ratio V F D θ Bh Bt Bh Bt 1 0 0 0 0 0.362 0.110 8.83 19.17 2 1 -1 1 1 0.355 0.097 9.00 20.26 3 0 0 0 0 0.352 0.115 9.07 18.79 4 0 -2 0 0 0.32 0.075 9.90 22.50 5 1 -1 -1 1 0.230 0.077 12.77 22.27 6 -1 1 1 1 0.395 0.092 8.07 20.72 7 1 -1 -1 -1 0.270 0.102 11.37 19.83 8 0 0 0 0 0.362 0.120 8.83 18.42 9 0 2 0 0 0.432 0.125 7.29 18.06 10 0 0 0 -2 0.319 0.105 9.92 19.58 11 -1 1 1 -1 0.300 0.100 10.46 20.00 12 -1 -1 1 -1 0.205 0.075 13.76 22.50 13 0 0 0 0 0.325 0.110 9.76 19.17 14 -1 1 -1 1 0.385 0.090 8.29 20.92 15 1 1 1 -1 0.357 0.097 8.95 20.26 16 0 0 0 0 0.347 0.099 9.19 20.09 17 1 1 -1 -1 0.335 0.125 9.50 18.06 18 1 1 1 1 0.370 0.110 8.64 19.17 19 -2 0 0 0 0.301 0.095 10.43 20.45 20 -1 -1 -1 1 0.270 0.052 11.37 25.68 21 1 1 -1 1 0.305 0.115 10.31 18.79 22 0 0 2 0 0.275 0.087 11.21 21.21 23 1 -1 1 -1 0.327 0.072 9.71 22.85 24 0 0 0 0 0.343 0.098 9.29 20.18 25 0 0 0 2 0.362 0.077 8.83 22.27 26 0 0 -2 0 0.299 0.080 10.49 21.94 27 0 0 0 0 0.302 0.105 10.40 19.58 28 -1 -1 -1 -1 0.370 0.090 8.64 20.92 29 2 0 0 0 0.265 0.097 11.54 20.26 30 -1 1 -1 -1 0.470 0.122 6.56 18.27 31 -1 -1 1 1 0.262 0.075 11.63 22.50
  7. 7. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 333 Table 5 Regression table for burr height Term Coef SE Coef T P Constant 0.341857 0.008979 38.073 0.000 V -0.007500 0.004849 -1.547 0.141 f 0.035500 0.004849 7.321 0.000 D -0.004667 0.004849 -0.962 0.350 PA 0.001000 0.004849 0.206 0.839 V*V -0.014089 0.004442 -3.171 0.006 f*f 0.009161 0.004442 2.062 0.056 D*D -0.013089 0.004442 -2.946 0.009 PA*PA 0.000286 0.004442 0.064 0.950 V*f -0.016125 0.005939 -2.715 0.015 V*D 0.037625 0.005939 6.335 0.000 V*PA 0.000250 0.005939 0.042 0.967 f*D -0.005125 0.005939 -0.863 0.401 f*PA 0.003000 0.005939 0.505 0.620 D*PA 0.028000 0.005939 4.715 0.000 S = 0.02376 R-Sq = 90.5% R-Sq(adj) = 82.1% Table 6 Regression table for burr thickness Term Coef SE Coef T P Constant 0.108143 0.002781 38.892 0.000 V 0.004292 0.001502 2.858 0.011 f 0.012958 0.001502 8.629 0.000 D -0.001708 0.001502 -1.138 0.272 PA -0.005458 0.001502 -3.635 0.002 V*V -0.002942 0.001376 -2.138 0.048 f*f -0.001942 0.001376 -1.412 0.177 D*D -0.006067 0.001376 -4.410 0.000 PA*PA -0.004192 0.001376 -3.047 0.008 V*f -0.000812 0.001839 -0.442 0.665 V*D -0.001938 0.001839 -1.053 0.308 V*PA 0.005063 0.001839 2.753 0.014 f*D -0.003188 0.001839 -1.733 0.102 f*PA 0.000063 0.001839 0.034 0.973 D*PA 0.008438 0.001839 4.588 0.000 S = 0.007357 R-Sq = 90.8% R-Sq(adj) = 82.8%
  8. 8. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 334 Table 7 RSM model adequacy check Run Order V f D PA Experimental values (mm) Predicted values from RSM model (mm) Error % Bh Bt Bh Bt Bh Bt 1 20 0.15 12 126 0.362 0.11 0.342 0.108 5.564 1.688 2 24 0.10 16 134 0.355 0.097 0.362 0.093 1.855 4.466 3 20 0.15 12 126 0.352 0.115 0.342 0.108 2.882 5.963 4 20 0.05 12 126 0.32 0.075 0.308 0.074 3.906 0.721 5 24 0.10 8 134 0.23 0.077 0.229 0.077 0.470 0.379 6 16 0.20 16 134 0.395 0.092 0.368 0.098 6.814 5.978 7 24 0.10 8 118 0.27 0.102 0.289 0.095 7.192 7.353 8 20 0.15 12 126 0.362 0.12 0.342 0.108 5.564 9.881 9 20 0.25 12 126 0.432 0.125 0.450 0.126 4.051 1.033 10 20 0.15 12 110 0.319 0.105 0.341 0.102 6.897 2.580 11 16 0.20 16 118 0.3 0.1 0.304 0.102 1.195 1.540 12 16 0.10 16 118 0.205 0.075 0.217 0.081 5.651 7.336 13 20 0.15 12 126 0.325 0.11 0.342 0.108 5.187 1.688 14 16 0.20 8 134 0.385 0.09 0.406 0.087 5.563 3.844 15 24 0.20 16 118 0.357 0.097 0.332 0.094 7.119 2.579 16 20 0.15 12 126 0.347 0.099 0.342 0.108 1.482 9.235 17 24 0.20 8 118 0.335 0.125 0.332 0.125 0.771 0.034 18 24 0.20 16 134 0.37 0.11 0.396 0.111 7.050 0.645 19 12 0.15 12 126 0.301 0.095 0.301 0.088 0.166 7.588 20 16 0.10 8 134 0.27 0.052 0.287 0.052 6.266 0.958 21 24 0.20 8 134 0.305 0.115 0.284 0.108 6.912 6.520 22 20 0.15 20 126 0.275 0.087 0.280 0.080 1.879 7.518 23 24 0.10 16 118 0.327 0.072 0.309 0.077 5.479 6.539 24 20 0.15 12 126 0.343 0.098 0.342 0.108 0.333 10.350 25 20 0.15 12 142 0.362 0.077 0.345 0.080 4.696 4.492 26 20 0.15 4 126 0.299 0.08 0.299 0.087 0.055 9.114 27 20 0.15 12 126 0.302 0.105 0.342 0.108 13.198 2.993 28 16 0.10 8 118 0.37 0.09 0.347 0.091 6.103 0.602 29 28 0.15 12 126 0.265 0.097 0.271 0.105 2.076 8.205 30 16 0.20 8 118 0.47 0.122 0.455 0.124 3.209 1.911 31 16 0.10 16 134 0.262 0.075 0.269 0.076 2.704 1.613
  9. 9. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 335 4.2 Response surface analysis For the burr height data, the regression table shows the following: • Linear effects: The p-value of 0.141, 0.350 and 0.839 for speed, drill diameter and point angle is not less than 0.05. Therefore, there is no significant effect of these three factors on the model. The p-value of 0.000 for feed is less than 0.05. Therefore drill diameter has significant effect. • Squared effect: The p-value of squared effect V*V = 0.006 and D*D = 0.009 which is less than 0.05. Hence these two squared effects are significant. • Interaction effect: The p-values of V*f= 0.015, V*D = 0.000 and D*θ = 0.000 are less than 0.05. Therefore, their effect on the model is significant. That means the effect of speed on burr height depends on the drill diameter and feed rate. The effect of diameter on burr height depends on the point angle. For the burr thickness data, the regression table shows the following: • Linear effects: The p-value of 0.011, 0.000 and 0.002 for speed, feed and point angle is less than 0.05. Therefore, there is significant effect of these three factors on the model. • Squared effect: The p-value of squared effect V*V = 0.048, D*D = 0.000 and θ*θ = 0.008 is less than 0.05. Hence these three squared effects are significant. • Interaction effect: the p-values of V*θ = 0.014 and D*θ = 0.000 are less than 0.05. Therefore, their effect on the model is significant. That means the effect of speed on burr height depends on the point angle and the effect of diameter on burr height depends on the point angle. For each term in the model, there is a coefficient. Using these coefficients we have construct an equation representing the relationship between the response and the factors. For the burr height data, regression equation is: Burr height (Bh) = 0.341857 - 0.007500(V) +0.035500(f) - 0.004667(D) + 0.001000(θ) - 0.014089(V2 ) + 0.009161(f2 ) - 0.013089(D2 ) + 0.000286(θ2 ) - 0.016125(V*f) + 0.037625(V*D) + 0.000250(V*θ) - 0.005125(f*D) + 0.003000(f*θ) + 0.028000(D*θ) (5) For the burr thickness data, regression equation is: Burr thickness (Bt) = 0.108143 + 0.004292(V) + 0.012958(f) - 0.001708(D) - 0.005458(θ) - 0.002942(V2 ) - 0.001942(f2 ) - 0.006067(D2 ) - 0.004192(θ2 ) - 0.000812(V*f) - 0.001938(V*D) + 0.005063(V*θ) - 0.003188(f*D) + 0.000063(f*θ) + 0.008438(D*θ) (6) The normal probability plots of the residuals versus the predicted response for burr height and burr thickness are shown in Figs. 3 and 4, respectively. Figures 3 and 4 revealed that the residuals generally fall on a straight line, implying that the errors are normally distributed. This implies that the models proposed are adequate, and there is no reason to suspect any violation of the independence or constant variance assumption.
  10. 10. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 336 R e s id u a l Percent 0 .0 40 .0 30 .0 20 .0 10 .0 0- 0 .0 1- 0 .0 2- 0 .0 3- 0 .0 4- 0 .0 5 99 95 90 80 70 60 50 40 30 20 10 5 1 N o r m a l P r o b a b i l i ty P l o t o f th e R e s i d u a l s (r e s p o n s e is Bh ) R e s id u a l Percent 0 .01 50 .01 00 .00 50.00 0- 0 .0 05- 0 .0 1 0 99 95 90 80 70 60 50 40 30 20 10 5 1 N o r m a l P r o b a b ility P lo t o f th e R e s id u a ls (re s po n se is Bt) Fig. 3 Normal plot of the residuals for burr height Fig. 4 Normal plot of residuals for burr thickness 5. CONCLUSION This paper has used Taguchi method and response surface methodology for selecting the optimum combination values of process parameters affecting the burr height and burr thickness in dry drilling of AISI 1015 steel. The conclusions of this present study were drawn as follows: • The optimal levels of the controllable factors were cutting speed 28 m/min, feed rate 0.1 mm/rev, drill diameter 20 mm and point angle 135° for burr height. • The optimal levels of the controllable factors were cutting speed 16 m/min, feed rate 0.05 mm/rev, drill diameter 4 mm and point angle 142° for burr thickness. • RSM has been used to determine the burr height and burr thickness attained by various drilling parameters. The quadratic modes developed using RSM were reasonably accurate and can be used for prediction within the limits of the factors investigated. • From RSM model and experiment results, the predicted and measured values are quite close, which indicates that the developed model can be effectively used to predict the burr height and thickness. The given models can be utilized to select the level of drilling parameters.
  11. 11. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME 337 REFERENCES [1] Gaitonde, Karnik, “Minimization of burr size in drilling using ANN-PSO approach”, Springer, (2010). [2] Gaitonde et al, “Genetic algorithm-based burr size minimization in drilling of AISI 316L stainless steel”, Journal of material processing technology, (2008), 197:225-236. [3] V.N.Gaitonde et al, “Taguchi optimization in drilling of AISI 316L stainless steel to minimize burr size using multi-performance objective based on membership function”, Journal of material processing technology, (2008), 202:374-379 [4] Nihat Tosun, “Determination of optimum parameters for multi-performance characteristics in drilling by using grey relational analysis”, IJAMT, (2006), 28:455-55. [5] Erol Kilickap, “Modeling and optimization of burr height in drilling of Al-7075 using Taguchi method and response surface methodology”, IJAMT, (2010), 49:911-923 [6] Erol Kilickap et al, “Optimization of drilling parameters on surface roughness in drilling of AISI 1045 using response surface methodology and genetic algorithm”, IJAMT, (2011) 52:79-88. [7] L. Ken Lauderbaugh, “Analysis of the effects of process parameters on exit burrs in drilling using a combined simulation and experimental approach”, Journal of material processing technology, (2009), 209:1909-1919. [8] Noorul Haq et al, “Multi response optimization of machining parameters of drilling Al/SiC metal matrix composite using grey relational analysis in the Taguchi method” IJAMT, (2008), 37:250- 255 [9] Karnik,Gaitonde, “Integrating Taguchi principle with GA to minimize burr size in drilling of AISI 316L Stainless steel with ANN model”, Springer, (2008) [10] Hossein Hasani, “Grey Relational Analysis to Determine the Optimum Process Parameters for Open-End Spinning Yarns”, Journal of Engineered Fibers and Fabrics, (2012), 7:81-86 [11] Reddy Sreenivasulu, Dr.Ch.Srinivasa Rao, “Application of grey relational analysis for surface roughness and roundness error in drilling of Al 6061 alloy”, International journal of lean thinking, (2012), 3:67-78. [12] V.N.Gaitonde et al, “Nihat Tosun, “Taguchi approach with multiple performance characteristics for burr size minimization in drilling”, Journal of scientific and industrial research, (2006), 65:977-981. [13] V.N.Gaitonde et al, “GA applications to RSM based models for burr size reduction in drilling”, (2005), 64:347-353. [14] Eder Silva Costa, “Burr Produced on the Drilling Process as a Function of Tool Wear and Lubricant-Coolant Conditions”, J. of the Braz. Soc. of Mech. Sci. & Engg., (2009), 31:57-63. [15] Sumedh. S. Pathak and Dr. M. S. Kadam, “Development of RSM Based Model for Machining of T105cr En31 Steel By Tialn Coated Twist Drill”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 3, Issue 1, 2012, pp. 300 - 309, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. [16] Nagaraja, Mervin A Herbert, Divakara Shetty, Raviraj Shetty and Murthy Brn, “Investigation into the Effects of Process Parameters on Delamination During Drilling of Bd-Cfrp Composite using Taguchi Design of Experiments and Response Surface Methodology”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 4, Issue 3, 2013, pp. 136 - 148, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359. [17] Vijaya Kumar Gurram and Venkataramaiah patti, “Selection of Optimum Parameters to Develop an Aluminium Metal Matrix Composite with Respect to Mechanical Properties by using Grey Relational Analysis”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 3, Issue 2, 2012, pp. 462 - 469, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.

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