Modeling and optimization of cutting parameters in high speed dry machini

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Modeling and optimization of cutting parameters in high speed dry machini

  1. 1. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 4, May – June (2013), © IAEME 242 MODELING AND OPTIMIZATION OF CUTTING PARAMETERS IN HIGH-SPEED DRY MACHINING OF INCONEL 718 ALLOY B.Satyanarayanaa , G. Ranga Janardhanab and D. Hanumantha Raoc a Department of Mechanical Engineering, VNR Vignana Jyothi Institute of Engineering & Technology, Hyderabad, India, b Director, Foreign University Relations, J N T U, Kakinada, India, c Principal, Matrusri Engineering College, Hyderabad, India, ABSTRACT The aim of this research paper is to develop mathematical model and optimize the machining conditions during high speed dry turning of nickel based super alloy Inconel 718 with coated tungsten-carbide tool insert. Surface roughness(SR) is considered as a performance measure. The effectiveness of experimental values and the effect of input process parameters such as cutting speed, feed rate and depth of cut on the performance measure were determined by multiple regression analysis with the use of analysis of variance (ANOVA). Based on the experimental results, second order surface roughness mathematical model was developed in terms of input process parameters. The developed mathematical model is used as an objective function in Genetic Algorithm (GA) to determine the optimal parameter values that gives minimum SR. The GA predicted value is confirmed with that of experimental value. Keywords: Inconel 718, High Speed Machining, Multiple Regression Analysis, Genetic Algorithm. 1. INTRODUCTION Among the nickel based heat resistant super alloys (HRSA), Inconel 718 is the most extensively used alloy [1], principally because it maintains excellent mechanical properties and is corrosion resistant over a wide temperature range (−250 to 705 °C) [2]. This alloy exhibit high strength to weight ratio, high resistance to corrosion, erosion, and wear and are INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET) ISSN 0976 - 6480 (Print) ISSN 0976 - 6499 (Online) Volume 4, Issue 4, May – June 2013, pp. 242-252 © IAEME: www.iaeme.com/ijaret.asp Journal Impact Factor (2013): 5.8376 (Calculated by GISI) www.jifactor.com IJARET © I A E M E
  2. 2. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 4, May – June (2013), © IAEME 243 also capable of retaining their mechanical properties such as hardness at elevated temperatures relative to steel and stainless steel alloys [3]. In any machining operation, it is an important task to select cutting parameter values for achieving high-quality cutting performance [4]. Increase in productivity and reduction in costs could be achieved with optimum selection of cutting conditions. [5]. A good understanding of the behaviour and the relationship between the work piece material, cutting tool material, cutting conditions and the process parameters is an essential requirement for the optimization of the cutting process [6]. Recently, the use of higher cutting speeds has shown promise in the reduction in cutting forces and less use of coolant and thus improving the machined surface quality to some extent [7, 8]. Thus, high- speed dry machining is expected to provide a suitable technology for the bulk material removal in machining of the aerospace components of this material. In this context, it is important to understand the cutting temperature, tool wear and the mechanics at higher cutting speeds, which govern the quality and integrity of machined surfaces [9–11]. 2. LITERATURE SURVEY D.G. Thakur et. al. [12] measured the surface roughness in high speed dry machining of Inconel 718 using carbide tool inserts(K20). They found that the surface finish was found to be optimum in the cutting range of 45–55 m/min for the feed rate of 0.08 mm/rev and 0.5 mm and also observed the grain deformation and refinement after machining. Taguchi S/N ratio was used to find the optimal cutting conditions. M.Z.A.Yazid et. al. [13] reported the results of an experimental works on surface integrity during finish turning of Inconel 718, under three cutting conditions (DRY, MQL 50 mL/h and MQL 100 mL/h). The parameter ranges are: cutting speed (90-150 m/min), feed rate (0.1-0.15 mm/rev) and cutting depth (0.3 to 0.5 mm. The results of this study show that MQL possibly improve the surface integrity characteristics. A. Devillez et. al. [14] focused on the effect of dry machining of Inconel 718 on surface integrity with semi-finished cutting conditions using a triangular shaped coated carbide tool insert. The observation is the surface quality is effected by the deposition of parts of built up edge to the machined surface; this is due to the higher temperatures generated. V. Bushlya et.al. [15] presented the results of superalloy machinability study on surface integrity of Inconel 718 with uncoated and coated PCBN round tools under wet cutting environment aiming on increased speed and efficiency. The parameters are: speed (250-350m/min), feed rate (0.1-0.2 mm/rev) and depth of cut constant at 0.3mm. The finding was the surface roughness has higher values for coated tools as a result of increased edge radius. Sahoo.P, [16] has successfully applied Response Surface Methodology (RSM) and Genetic Algorithm (GA) to optimize turning parameters for surface roughness during machining of AISI 1040 mild steel. The developed models through RSM shows good agreement with the experimental results. The optimal parameters obtained through GA matches with the confirmatory test results. The literature reveals that, there is a scope for optimizing the cutting conditions during high speed dry turning of Inconel 718 with coated carbide tool using regression analysis and Genetic Algorithm. Hence, in the present study, second order mathematical model was developed using multiple regression analysis for surface roughness and the
  3. 3. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 4, May – June (2013), © IAEME 244 developed model is used to find the optimal cutting parameters using GA during high speed dry machining of Inconel 718 using coated carbide tool. The surface roughness obtained with the optimal conditions obtained from GA, was verified with that of measured values from experiments for the feasibility of optimization process. 3. METHODOLOGY A multiple regression analysis using analysis of variance (ANOVA) is conducted to determine the performance of experimental measurements and to show the effect of cutting parameters on the response. By using the experimental results, the second-order mathematical model in terms of cutting parameters are developed for the response with the help of Response Surface Methodology (RSM). The developed mathematical model was used as an objective function and the optimization was carried out with the help of Genetic Algorithm. 3.1 Mathematical formulation Response Surface methodology (RSM) is a collection of mathematical and statistical techniques useful for developing, improving and optimizing processes [17]. This is extensively used in the environment where a number of input variables influence the response, with the aim to establish the relationship between response and independent variables (input variables). In general the relationship is represented by: y = f (VC, f ,d) + ε (1) where y is the machining response, f is the response function and VC, f and d are turning variables and ε is the statistical error which is often assumed to be normally distributed about the observed response y with mean zero. The relationship between the response variable surface roughness Ra, and the independent variables can be represented as: Ra = C (VC)a f b dc (2) where C represents constant and a, b and c represents exponents. To assist the determination of exponents and constants, the mathematical model has to be linearized by applying logarithmic transformation. This can be represented as: ln( Ra) = lnC + a ln VC +b ln f +c ln d (3) The second order polynomial model developed from the equation (3) using method of least squares, can be represented as: y2 =y - ε= b0 x0 + b1 x1 + b2 x2 + b3 x3 + b12 x1 x2 + b23 x2 x3 + b31 x3 x1 + b11 x1 x1 + b22 x2 x2 +b33 x3 x3 (4) where y2 is the estimated response based on the second order polynomial equation. y is the measured response on a logarithmic scale, x0 =1, x1, x2 and x3 are logarithmic transformations of cutting speed, feed rate and depth of cut respectively, ε is the experimental error and 'b' values are to be estimated by the method of least squares.
  4. 4. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 4, May – June (2013), © IAEME 245 3.2 Optimization by Genetic Algorithm (GA) Optimization problems can be effectively solved by a powerful and robust tool, Genetic Algorithm (GA) [18]. GA performs a multi directional search by maintaining a population of potential solutions and encourages information formation and exchange between these directions. The population undergoes a simulated evolution: at each generation the relatively “good "solutions reproduce, while the relatively “bad” solutions die. To distinguish between different solutions it has been used an objective (evaluation) function which plays the role of an environment [19]. 4. EXPERIMENTAL DETAILS 4.1 Design of experiment The design of experiments technique is a vital tool, which allow us to carry out the modeling and analysis of the influence of input process variables on the response. In the present study, the controllable turning parameters such as cutting speed, feed rate and depth of cut are considered as design factors. The range of values of each factor was set at three different levels based on the available machining data on Inconel 718 from hand books, literature and from preliminary experimentation as shown in Table 1. A full factorial design (L27 orthogonal array) is used to design factors to increase the accuracy of the developed model. Table. 1 Process parameters and their levels Parameters Levels 1 2 3 Cutting speed, VC (m/min) 50 65 80 Feed, f (mm/rev) 0.05 0.125 0.2 Depth of cut, d (mm) 0.2 0.4 0.6 4.2 Work material, equipment and cutting tool used The work material used was Inconel 718 rod (Ni = 54.48 %, Cr = 17.5%, Nb = 4.9%, Al = 0.66 %, Ti = 0.96% balance are Fe and other). The machine used for turning tests is a ACE make CNC Super jobber 500 turning centre with 12 kW motor. For generating the turned surfaces, CNC part programs for tool paths were created with specific commands. The surface roughness of a machined surface was recorded using Mitutoyo make standard profilometer SJ 301. As per the ISO 3685 standards, surface roughness of arithmetic average roughness, Ra (µm) was recorded. The sampling length used was 0.8mm. Sandvik make SNMG 120408 coated carbide tool was used in the experimentation. The working tool geometry in combination with tool holder and insert is given in Table 2. For each machining experiment, a new cutting edge was used. Readings are taken at least twice and the average values are considered. Table 2: Working tool geometry Orthogonal rake angle (γ) Orthogonal Clearance angle (α) Inclination angle (λ) Auxiliary cutting edge angle Approach angle (ψ) Included angle (β) Nose radius (r) mm -6 ° 6° -6° 15° 75° 90° 0.8
  5. 5. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 4, May – June (2013), © IAEME 246 5. RESULTS AND DISCUSSION From the experimental results, the unknown regression coefficients were determined through Design expert software and empirical equation has been obtained to estimate surface roughness (Ra) with the input process parameters considered i.e. cutting speed, feed rate and depth of cut. The second-order polynomial equations for the response variable surface roughness (Ra) is expressed as 222 0556.09383.120012.0 5.10053.00089.07056.01383.01677.09062.5 )( dfV dfdVfVdfV RRoughnessSurface c ccc a −+ ++−++−−+ = (5) 5.1 Model accuracy and significance of the individual model coefficients Analysis of variance(ANOVA) is conducted to check the model accuracy and to know the significance of individual coefficients shown in Table 3. The Table 3 shows that the regression model, cutting speed, feed, depth of cut, higher order terms of speed and feed are significant which influence the surface roughness (Ra) as their p-values are less than that of a significance level of α = 0.05. Multiple regression correlation coefficients R2 for the second order polynomial model was quite satisfactorily as R2 = 97.5% R2 (pred) = 93.2% R2 (adj) = 96.24% Table 3 Analysis of Variance (ANOVA) for surface roughness (Ra) After eliminating the insignificant coefficient terms, the equation (5) is modified as: 22 9383.120012.07056.01383.01677.09062.5 )( fVdfV RRoughnessSurface cc a +++−−+ = (6) The normal probability plot of the residuals for the surface roughness is shown in Fig 1. It shows that the residuals are placed on a straight line, indicates that the errors are Source Sum of Squares DF Mean square F Value p-value Prob > F Model 3.51 9 0.39 75.79 < 0.0001 Significant Vc 1.02 1 1.02 197.94 < 0.0001 F 1.84 1 1.84 357.27 < 0.0001 D 0.19 1 0.19 36.58 < 0.0001 Vc x f 8.333E-004 1 8.333E-004 0.16 0.6923 Vc x d 2.408E-003 1 2.408E-003 0.47 0.5029 f x d 5.208E-003 1 5.208E-003 1.01 0.3283 Vc x Vc 0.42 1 0.42 82.30 < 0.0001 f x f 0.033 1 0.033 6.37 0.0218 d x d 7.407E-006 1 7.407E-006 1.441E- 003 0.9702 Residual 0.087 17 5.141E-003 Total 3.59 26
  6. 6. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 4, May – June (2013), © IAEME 247 distributed normally and the developed model is fairly well fitted with the experimental values. Fig. 1 Probability plot of the residuals The variation of experimental surface roughness (Ra) values with the predicted values obtained from equation (6) is shown in the Fig. 2. It was seen that predicted and experimental values follows the close path which shows the adequacy of developed model. Fig. 2. Comparison of experimental results and predicted values for surface roughness 5.2 Validation of the model Additional experiments were conducted to check the accuracy of the developed model with the different input process parameter values selected randomly are shown in Table 4. The experimental and model values with percentage error is shown in Table 5. The percentage error associated with each experiment is observed to be less than 3%. The experimental values and predicted values are close together, which indicates the suitability of the developed model.
  7. 7. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 4, May – June (2013), © IAEME 248 Table 4 Cutting conditions for validation of model Test No Cutting Speed (Vc) (m/min) Feed (f) (mm/rev) Depth of cut (d) (mm) 1 60 0.08 0.25 2 75 0.15 0.45 3 60 0.1 0.5 4 75 0.175 0.3 Table 5 Validation test results Test No surface roughness (Ra) Model value Experiment value % Error 1 0.311012 0.32 2.808 2 0.538038 0.53 1.513 3 0.498248 0.51 2.304 4 0.592528 0.61 2.864 Mean 2.37 5.3 Effect of Cutting parameters on surface roughness Surface plots have been drawn using Design expert software for the convenience of understanding the effect of cutting parameters on the response and selecting the best combinations of cutting parameters. The surface roughness variation for different combinations of cutting parameters are shown in Fig. 3. The surface roughness variation with cutting speed and feed rate at 0.4mm depth of cut is shown in Fig. 3 (a). It shows that, the surface roughness is increasing greatly with increase in feed than decrease in cutting speed. It was also observed that feed has more effect on surface roughness than other control factors. The combination of low feed rate and high speed gives better surface finish. The effect of feed rate and depth of cut at a speed of 50m/min on surface roughness is shown in Fig. 3 (b). It was observed that, surface roughness is better at low values of feed rate and depth of cut. Surface roughness is increasing with the increase of feed and depth of cut. The effect of cutting speed and depth of cut at a feed rate of 0.125 mm/rev on surface roughness is shown in Fig. 3 (c). It was found that, at lower value of depth of cut and higher value of cutting speed, the surface roughness is minimum.
  8. 8. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 4, May – June (2013), © IAEME 249 (a) (b) (c) Fig. 3 Surface plots of speed, feed and depth of cut 6. OPTIMIZATION THROUGH GA The model would be optimized using GA optimization tool in MATLAB software [20]. In this context an effort has been made to optimize the process variables that produce the best possible output parameter value within the assumed variable bounds. The optimization problem consists of a minimization function defined by the second order equation given by (6) and the following variable bounds. 50 m/min ≤ x1 ≤ 80 m/min 0.05 mm/rev ≤ x2 ≤ 0.2 mm/rev 0.2 mm ≤ x3 ≤ 0.6 mm xil ≤ xi ≤ xiu where xil and xiu are the lower and upper bounds of process variables xi and x1, x2, x3 are logarithmic transformation of cutting speed, feed rate and depth of cut. The main parameters of the GA are the mutation, population size, number of generations and cross over function. In the present study, population size 100, adaptive feasible mutation, two point crossover function and number of generations 500 are judiciously taken. The convergence of GA to the minimum objective function value for the optimization problem is shown in Fig. 4. The results found by GA are compared with those obtained from conformation experiments and given in Table 6. They show fairly good agreement with each other. Table 6. Optimization results Output parameters Cutting parameters Predicted value (GA) Experiment al value Vc (m/min) f (mm/rev) d (mm) Surface roughness (µm) 70 0.05 0.2 0.133 0.13
  9. 9. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 4, May – June (2013), © IAEME 250 Fig. 4. Convergence graphs of GA for the optimization problems It can be justified that, the surface roughness is directly proportional to the square of feed rate [21]. At higher speeds the formation of built up edge (BUE), if any, disappears and observed improved surface finish. Therefore, the surface roughness is low at higher speeds and lower feed rates and depth of cuts. The same phenomenon was observed in high speed dry turning of Inconel 718 with coated cemented carbide tools. 7. CONCLUSIONS A mathematical model based on experimental results was developed for obtaining a surface roughness using the response surface methodology. The predicted values of the surface roughness from the model are compared with the values obtained experimentally and found a good closeness between them. The surface roughness is increasing greatly with increase in feed than decrease in cutting speed. Feed shows more effect on surface roughness than other control factors. The combination of low feed rate and high speed are giving better surface finish. Surface roughness is better at low values of feed rate and depth of cut. At lower value of depth of cut and higher value of cutting speed, the surface roughness was observed minimum. The optimal cutting conditions were obtained for the best possible values of surface roughness during high speed turning of Inconel 718 through GA. Confirmation experiment results, when machining with optimal cutting conditions, shows good agreement with the predicted values obtained from GA. 0 20 40 60 80 100 0.1 0.2 0.3 0.4 0.5 0.6 Generation Surfaceroughnes Best: 0.13351 Mean: 0.13357 Best fitness Mean fitness
  10. 10. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 4, May – June (2013), © IAEME 251 8. REFERENCES 1. F Klocke, W Konig, K Gerschwiler, “Advanced machining of titanium- and nickel- based alloys” Advanced Manufacturing Systems and Technology, Springer Wien, New York (1997) 2. “INCONEL alloy 718”, Inco Alloys International Inc, Publication No. IAI-19/4M/1994, 1985. 3. “Turning difficulty-to-cut alloys”., Sandvik technical guide. 4. Zuperl U, Cus F, Mursec B, Ploj T “A hybrid analytical neural network approach to the determination of optimal cutting conditions.” J Mater Process Technol 157–158:82– 90(2004) 5. Yang WH, Tarng YS “Design optimization of cutting parameters for turning operations based on the Taguchi method.” J Mater Process Technol 84:122–1299 , (1998) 6. E.O. Ezugwu, K.A. Olajire, J. Bonney, “Modelling of tool wear based on component forces,” Tribol. Lett. 11 (1) (2001). 7. R.S. Pawade, S.S. Joshi, P.K. Brahmankar, (2008). Effect of cutting edge geometry and machining parameters on surface integrity of high-speed turned Inconel 718, International Journal of Machine Tools and Manufacture 48 (1) 15–28. 8. R.T. Coelho, L.R. Silva, A. Braghini Jr., A.A. Bezerra, Some effects of cutting edge preparation and geometric modifications when turning Inconel 718 at high cutting speeds, Journal of Materials Processing Technology 148 (1) (2004) 147–153. 9. R.S. Pawade, S.S. Joshi, P.K. Brahmankar, M. Rahman, (2007). An investigation of cutting forces and surface damage in high-speed turning of Inconel 718, Journal of Materials Processing Technology 192–193 139–146. 10. R.S. Pawade, S.S. Joshi, P.K. Brahmankar, M. Rahman, (2004). Some investigations of high-speed turned Inconel 718, in: V.S. Raja, Kuppan (Eds.), Proceedings of the of the 21st AIMTDR Conference, VIT, Vellore, pp. 41–47, (December 21–23). 11. Muammer Nalbant, Abdullah Alt y´n, Hasan Gokkaya, (2007). The effect of coating material and geometry of cutting tool and cutting speed on machinability properties of Inconel 718 super alloys, Materials and Design 28 (5) 1719–1724. 12. D. G. Thakur, B. Ramamoorthy, and L. Vijayaraghavan (2009) “A Study on the parameters in High-speed Turning of superalloy Inconel 718”, Materials and Manufacturing processes, pp 497-503. 13. M.Z.A.Yazid, G.A. Ibrahim, A.Y.M. Said, C.H. CheHaron, J.A. Ghani, " Surface integrity of Inconel 718 when finish turning with PVD coated carbide tool under MQL", 1st CIRP Conference on Surface Integrity (CSI), Procedia Engineering 19 (2011) 396 – 401. 14. A. Devillez, G. Le Coz, S. Dominiak, D. Dudzinski, " Dry machining of Inconel 718, workpiece surface integrity", Journal of Materials Processing Technology 211 (2011) 1590– 1598. 15. V. Bushlya,, J. Zhou, J.E. Ståhl " Effect of Cutting Conditions on Machinability of Superalloy Inconel 718 During High Speed Turning with Coated and Uncoated PCBN Tools", 45th CIRP Conference on Manufacturing Systems 2012, Procedia CIRP 3 ( 2012 ) 370 – 375 16. Sahoo. P , " Optimization of turning parameters for surface roughness using RSM and GA", Advances in Production Engineering & Management vol. 6 (3), 2011, pp; 197-208.
  11. 11. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 4, May – June (2013), © IAEME 252 17. Oktem, H., “An integrated study of surface roughness for modeling and optimization of cutting parameters during end milling operation,” Int. J. Adv. Manuf. Technol., Vol. 43, No. 9-10, pp. 852-861, 2009. 18. Michalewicz, Z., “Genetic Algorithms - Data Structures-Evolution Programs,” Springer, p. 17, 1999. 19. Deb, K., " An efficient constraint handling method for Genetic Algorithms," Computer methods in Applied mechanics and engineering, Vol.186, No. 2-4, pp. 311-338, 2000. 20. The MathWorks, Inc., MATLAB R2008a, 2008. 21. Trent. E.M, Metal cutting principles, Butterworths, London 2000. 22. M Manohar, Jomy Joseph, T Selvaraj and D Sivakumar, “Development of Models using Genetic Programming for Turning Inconel 718 With Coated Carbide Tools”, International Journal of Design and Manufacturing Technology (IJDMT), Volume 4, Issue 1, 2013, pp. 1 - 13, ISSN Print: 0976 – 6995, ISSN Online: 0976 – 7002. 23. Satyanarayana.B, Ranga Janardhana.G, Kalyan.R.R and Hanumantha Rao.D, “Prediction of Optimal Cutting Parameters for High Speed Dry Turning of Inconel 718 using Gonns”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 3, Issue 3, 2012, pp. 294 - 305, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.

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