Optimized high speed turning on Inconel 718 using Taguchi method based Grey relational analysis
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1 Indian Journal of Engineering & Materials Sciences Vol. 20, August 2013, pp Optimized high speed turning on Inconel 718 using Taguchi method based Grey relational analysis B Satyanarayana a *, G Ranga Janardhana b & D Hanumantha Rao c a Department of Mechanical Engineering, VNR Vignana Jyothi Institute of Engineering & Technology, Hyderabad , India b Department of Mechanical Engineering, College of Engineering, J N T U, Vizayanagaram , India c Department of Mechanical Engineering, MVSR Engineering College, Hyderabad , India Received 25 July 2011; accepted 1 April 2013 Inconel 718, a Nickel based super alloy which has wide applications in aerospace industry particularly in the hot sections of gas turbine engines due to their high temperature strength and corrosion resistance. It is known as being among the most difficult-to-cut materials. This paper presents an optimum process parameters (speed, feed and depth of cut) to minimize the cutting force, surface roughness and tool flank wear together in CNC high speed dry turning of Inconel 718 using Taguchi method based Grey relational analysis. The study involved nine experiments based on Taguchi orthogonal array and the result indicates that the optimal process parameters are 60 m/min for speed, 0.05 mm/rev for feed and 0.2 mm for depth of cut from the selected range. Also the significant process parameters have been found out for the above process optimization by performing ANOVA. Confirmation tests with the optimal levels of cutting parameters are carried out in order to illustrate the effectiveness of the method. Keywords: Inconel 718, High speed machining, Grey relational analysis, ANOVA Alloy 718, developed in the late 1950s, is today by far the most used superalloy, mainly applied in the hot section of turbine machinery 1,2 either as vacuum investment castings or in the various wrought shapes supplied. It is a high strength superalloy capable for long time service at 650 C the upper temperature limit for this alloy. Alloy 718 is a precipitation hardening nickel-iron base superalloy. The high temperature strength is mainly due to the hardening effects of submicron gamma double prime (γʺ) precipitates and also to a minor extent to the effects of gamma prime (γʹ) precipitates 3,4. The alloy also contains a delta (δ) phase which does not confer any strength but is important during the metallurgical processing for grain size refinement and control. Molybdenum adds strength by solid solution hardening. MC-carbides (NbC, TiC) and nitrides (TiN), are distributed in the matrix, although limited in both size and number they are still undesirable not the least from a machinability point of view. Since the machining of alloy 718 has been found to be a challenging task it has attracted considerable research In almost all of this research the machining has been examined from the manufacturing technology point of view for the improvement of tool materials, *Corresponding author ( sanbollu@gmail.com) geometries and machining parameters as, e.g., the cutting speed and feed rate. The main reasons for the machining difficulties are due to its high strength and ductility at high temperatures, the strong work hardening of its austenitic matrix and its low thermal conductivity 11,12. The material can also adhere and weld onto the rake face of the insert which causes severe notching 11. Among the most effective and efficient modern manufacturing technologies, high speed machining (HSM) is employed to increase productivity while simultaneously improving product quality and reducing manufacturing costs. Depending on work and tool materials as well as tool life requirements, the cutting speed used in HSM is often 2-50 times higher than those employed in traditional (relatively low speed) machining. Due to its high material removal rate and short product cycle time, HSM has received steadily growing applications in recent years in many industrial sectors, such as defence, aerospace, aircraft and automotive. Research on HSM involves a wide variety of work materials ranging from easy-tocut aluminium alloys 13,14 to difficult-to-cut hardened steels 15,16 and advanced aerospace materials 17. The machining forces have an important share in the generation of stresses and temperature in the machined surfaces. They further influence the stresses
2 270 INDIAN J ENG. MATER. SCI., AUGUST 2013 and temperature along tool-chip and tool-work interfaces. All these effects finally lead to poor surface integrity if the working conditions are not properly selected. Therefore, it is important to know the machining parameters, which reduce the cutting forces. The low thermal conductivity and diffusivity of Inconel 718 alloy cause steep temperature gradient at the tool edge and the shift the location of the maximum temperature towards the tool tip. As a result, excessive tool wear, premature cracking and built-up edge formation are observed. Due to the low machinability of this material, the worked surface and subsurface are easily affected or damaged during the machining operations. To ensure the better surface integrity, a special care must be taken when choosing cutting conditions, tool material, geometry and tool coating. Hence, much attention should be paid to surface characteristics of components, that was pointed first by Field and Khales 18 and then by Arunachalam 19 about machining Inconel 718. It appears that most of the work has been done at reasonably lower cutting speeds, whereas the increasing use of Inconel 718 in aerospace and automobile industries necessitates the knowledge of their machinability at higher cutting speeds, which is not adequate at the present. Further, less attention has been paid to optimize the process conditions to improve machinability in terms of cutting forces, tool wear and surface roughness together in machining of Inconel 718. Also, most of these studies include random experiments. Thus, in this experimental study, keeping in view the extensive applications of turned components in the critical aerospace engine, turning process is selected to assess the effect of machining parameters on cutting forces, tool flank wear and surface roughness together in high speed dry machining of Inconel 718. Taguchi Method based Grey Relational Analysis The optimization of multiple performance characteristics is different from that of a single performance characteristic. The higher S/N ratio for one performance characteristic may correspond to a lower S/N ratio for another. Therefore, the overall evaluation of the S/N ratio is required for the optimization of multiple performance characteristics. The usual recommendation for the optimization of a process with multiple performance characteristics is left to the engineering judgment and verified by confirmation experiment 20. Normally, the problem is tackled by using desirability function and/or weighting method. In weighting method, a suitable weighting factor (in percent) is assigned to the normalized measure of a performance characteristic indicating the importance or desirability of that performance characteristic for a particular application. Taguchi method based grey relational analysis (GRA) is one such method of optimization of multiple performance characteristics using weighting method. The grey system theory proposed by Deng 21 has been proven to be useful for dealing with poor, incomplete and uncertain information. The grey relational analysis is based on the grey system theory and can be used to solve complicated interrelationships among multiple performance characteristics effectively. However, the first step of the grey relational analysis is the grey relational generation 22. During this step, all the performance characteristics are normalized in the range between zero and one. Next, the grey relational coefficient is calculated from the normalized data to express the relationship between the desired and actual normalized performance values. Then, the grey relational grade is computed by assigning a suitable weighting factor (in percent) to the grey relational coefficient corresponding to each performance characteristic. Overall evaluation of the multiple performance characteristics is, thus, based on the grey relational grade. As a result, optimization of the complicated multiple performance characteristics can be converted into optimization of a single grey relational grade. The optimal level of the process parameters is the level with the highest grey relational grade. Furthermore, a statistical analysis of variance (ANOVA) is performed to find which process parameters are statistically significant. With the grey relational analysis and statistical ANOVA, the optimal combination of the process parameters can be predicted. Finally, a confirmation experiment is conducted to verify the optimal process parameters obtained from the analysis. Experimental Procedure Work material and cutting tools The cutting experiments were carried out on a CNC Lathe (Fig. 1) using uncoated cemented carbide tool insert Sandvik make SNMG H13A for the machining of Inconel 718 bars. The tool signature of the same is given in Table 1. The work material used was Inconel 718 (Ni = %, Cr = 17.5%, Nb =
3 SATYANARAYANA et al.: OPTIMIZED HIGH SPEED TURNING ON INCONEL %, Al = 0.66 %, Ti = 0.96% balance are Fe and other). The parameters and their associated levels based on preliminary experiments and the available data for machining of superalloy Inconel 718 in literature are given in Table 2. Machining performance measure Cutting force (F c ) Cutting force was measured online during turning of Inconel 718 with three-component cantilever type strain gauge dynamometer. Tool flank wear (TFW) Tool flank wear (TFW) measurements were carried out using high resolution Tool maker s microscope (Fig. 2) at the end of each experiment. Surface roughness (SR) The other response variable measured was surface roughness (R a ) value. The machined surface roughness was measured by a Mitutoyo make Surftest SJ301 (Fig. 3) surface roughness tester of sampling length 0.8 mm and least count of 0.01 µm. The result of the surface roughness depends on the stylus path direction. For this reason the roughness were measured several times and averaged and expressed in microns (µm). The machined workpiece is shown in Fig. 4. Determination of Optimal Cutting Parameters In this section, the use of an orthogonal array to reduce the number of cutting experiments for determining the optimal cutting parameters is reported. Results of the cutting experiments are studied by using the GRA and ANOVA analysis. Based on the results of the GRA and ANOVA analysis, optimal cutting parameters with considerations of the multiple performance characteristics including cutting force, surface roughness and tool flank wear are obtained and verified. Table 2 The machining parameters and their levels Parameters Levels Cutting speed (V c ), m/min Feed ( f ),mm/rev Depth of cut (d ), mm Table 1 Working geometry of the tool used in experiments S. No Parameter Value 1 Inclination angle -6 2 Orthogonal rake angle -6 3 Orthogonal clearance angle 6 4 Auxiliary cutting edge angle 15 5 Principal cutting edge angle 75 6 Included angle 90 7 Nose radius 0.8 mm Fig. 2 Tool maker s microscope Fig. 3 Surface roughness tester Fig. 1 CNC Lathe used for the experiments Fig. 4 Machined work piece
4 272 INDIAN J ENG. MATER. SCI., AUGUST 2013 Orthogonal array experiment In this study, an L 9 orthogonal array with four columns and nine rows was used. This array has eight degrees of freedom and it can handle three-level process parameters. Each cutting parameter is assigned to a column and nine cutting parameter combinations are available. Therefore, only nine experiments are required to study the entire parameter space using the L 9 orthogonal array. The experimental layout for the three cutting parameters using the L 9 orthogonal array is shown in Table 3. Since the L 9 orthogonal array has four columns, one column of the array is left empty for the error of experiments. Orthogonality is not lost by letting one column of the array remain empty. Grey relational analysis The grey relational analysis (GRA) associated with the Taguchi method represents a rather new approach to optimization. The grey theory is based on the random uncertainty of small samples which developed into an evaluation technique to solve certain problems of system that are complex and having incomplete information. A system for which the relevant information is completely known is a white system, while a system for which the relevant information is completely unknown is a black system. Any system between these limits is a grey system having poor and limited information. 22 Grey relational analysis (GRA), a normalized evaluation technique, is extended to solve the complicated multiperformance characteristics optimization effectively. Data pre-processing Data pre-processing is normally required, since the range and unit in one data sequence may differ from others. It is also necessary when the sequence scatter range is too large, or when the directions of the target in the sequences are different. In this study, a linear normalization of the experimental results for cutting force, tool flank wear and surface roughness were performed in the range between zero and one, which is also called the grey relational generation. The normalized data processing for F c, SR and TFW corresponding to lower-the-better criterion can be expressed as: Table 3 Experimental layout using an L 9 orthogonal array Experiment Cutting parameter level No. A B C D Cutting speed Feed rate Depth of cut Error max yi( k) yi( k) X i ( k) = max yi( k) min yi( k) Table 4 Grey relational coefficients and grades Where X i (k) is the value after the grey relational generation, min y i (k) is the smallest value of y i (k) for the k th response, and the max y i (k) is the largest value of y i (k) for the k th response. The ideal sequence is X 0 (k) (k=1, 2, 3 for F c, SR and TFW respectively). The Grey relational generation is given in Table 4. Basically, the larger normalized results correspond to the better performance and the best-normalized result should be equal to one. Next, the grey relational Expt. Cutting Feed (f), Depth of Cutting SR, TFW, Normalized decision matrix Grey relational coefficient Grey relational No. speed mm/rev cut (d), force µm mm (ψ=0.5) grade (V c ), mm (F c ), N F c, N SR, µm TFW, F c, N SR, µm TFW, m/min mm mm value rank
5 SATYANARAYANA et al.: OPTIMIZED HIGH SPEED TURNING ON INCONEL coefficient is calculated to express the relationship between the ideal (best) and actual normalized experimental results. The grey relational coefficient ξ i (k) can be calculated as: min max ξ i ( k) = + ψ oi + ψ max Where oi = xo( k) xi( k) = difference of the absolute value between x 0 (k) and x i (k), min and max are respectively the minimum and maximum values of the absolute differences of all comparing sequences. Ψ is a distinguishing coefficient, 0 ψ 1, the purpose of which is weaken the effect of max when it gets too big and thus enlarges the difference significance of the relational coefficient. In the present case, ψ = 0.5 is used. After averaging the grey relational coefficients (Table 4), the grey relational grade γ i can be calculated as follows: γ 1/ n = n Wkξi ( k ) i k = 1 Here W k denotes the normalized weight factor and taken as 1. The grey relational grade γ i represents the level of correlation between the reference sequence and the comparability sequence. If the two sequences are identical, then the value of grey relational grade is equal to 1. The high relational grade implies that the corresponding parameter combination is closer to the optimal. The grey relational grade also indicates the degree of influence that the comparability sequence could explain over the reference sequence The higher grey relational grade represents that the corresponding experimental result (Table 4, Experiment number 8) is closer to the ideally normalized value. In other words, optimization of the complicated multiple performance characteristics can Fig. 5 GRG of multiple performances characteristic be converted into optimization of a single grey relational grade. Since the experimental design is orthogonal, it is then possible to separate out the effect of each machining parameter on the grey relational grade at different levels (Table 5). Basically, the larger the grey relational grade the better is the multiple performance characteristics. However, the relative importance among the machining parameters for the multiple performance characteristics still needs to be known so that the optimal combinations of the machining parameter levels can be determined more accurately. From Table 5 and Fig. 5, it is clearly known that third level of speed, first level of feed and first level of depth of cut are the optimal combination of process parameters for multiple performance characteristics. Analysis of variance (ANOVA) The purpose of the ANOVA is to investigate which of the process parameters significantly affect the performance characteristics. This is accomplished by separating the total variability of the grey relational grades, which is measured by the sum of the squared deviations from the total mean of the grey relational grade, into contributions by each machining parameter and the error. Statistically, there is a tool called the F-test named after Fisher 26 to see which process parameter have a significant effect on the performance characteristic. Usually the larger the F-value, the greater the effect on the performance characteristic due to the change of the process parameter. Table 6 shows that the results of ANOVA for multiple performance characteristics F c, SR and TFW. It can be found that the feed is the Table 5 Grey relational grades at different levels Factor Speed Feed DoC Level Level Level Max-Min Rank Table 6 Results of ANOVA for multiple performance characteristics Parameter SS DoF MS F Contribution % Vc F Doc Error Total
6 274 INDIAN J ENG. MATER. SCI., AUGUST 2013 Table 7 Results of the confirmation experiment Starting cutting Optimal cutting conditions conditions Prediction Experiment Setting V 1 F 1 D 1 V 3 F 1 D 1 V 3 F 1 D 1 level CF SR TFW GRG Improvement of grey relational grade = = significant parameter followed by depth of cut and speed, for effecting the F c, SR and TFW. Therefore, based on the grey relational grade and ANOVA analysis, the optimal cutting parameters for multiple performance characteristics are cutting speed at level 3, the feed at level 1 and depth of cut at level 1. Confirmation test After the optimal level of machining parameters has been identified, a verification test needs to be carried out in order to check the accuracy of analysis. The estimated grey relational grade γ *, using the optimal level of the process parameters can be calculated as: * 0 γ γ m (γi-γ 1 m ) i= = + γ m is total mean grey relational grade, γ i is mean grey relational grade at the optimal level, o is the number of main design parameters that significantly affect the roughness characteristics of ground surfaces. Table 7 shows the comparison of the estimated grey relational grade with the actual grey relational grade obtained in experiment using the optimal cutting parameters. From Table 7, it may be noted that there is a good agreement between estimated value ( ) and experimental value ( ). The increase in grey relational grade from initial cutting conditions to optimum cutting conditions is of Hence, it may be concluded that the multiple performance characteristics of turning Inconel 718 super alloy such as F c, SR and TFW are improved together by using this approach. Conclusions In this study, superalloy Inconel 718 is used, which is a costly material and has got peculiar characteristics which make it difficult to machine. Therefore, the selections of optimal parameters are important to minimize the higher unit cost per machined part and service life. In this work, Taguchi-method based grey relational analysis has been used to provide an efficient design of experiment technique to obtain simple, systematic and efficient methodology for the optimization of the process parameters at high speed machining. The application of Taguchi-method based grey relational analysis directly integrates the multiple quality characteristics (F c, TFW and SR) into a single performance characteristic called grey relational grade. The grade obtained for each experiment can immediately reflect the actual turning results in terms of quality of surface, cutting force and tool wear. The experimental results show that the optimal cutting parameters are high cutting speed 60 m/min, lower feed 0.05 mm/rev and lower depth of cut 0.2 mm, gives the lower cutting force (F c ), surface roughness (SR) and tool flank wear (TFW) together within the range of experiments based on the average grey relational grade. The ANOVA analysis for F c, TFW and SR shows that the cutting parameter, feed is more significant at 95% confidence level compared to depth of cut and speed. Confirmation experiment shows that there is an increase in grey relational grade from the initial cutting conditions to the optimum cutting conditions is of Thus it may be concluded that the multiple performance characteristics of the Inconel 718 turning process such as cutting force, tool flank wear and surface finish are improved together by using this approach. References 1 Reed R C, The Superalloys, (Cambridge University Press, Cambridge), Sims C T, Stoloff N S & Hagel W C, Superalloys II, (Wiley, New York), Radavich J F, in Superalloy 718 Metallurgy and Applications, edited by Loria E A, (TMS, The Minerals, Metals and Materials Society, Pittsburgh), 1989, pp Saunders N, Guo Z, Li X, Miodownik & Schile J P, in Superalloys 2004, edited by Green K A, (TMS, The Minerals, Metals and Materials Society, Pittsburgh), 2004, pp Nalbant M, Altin A & Gökkaya H, Mater Des, 28 (4) (2007) Liu G, Int J Mach Mach Mater, 1 (3) (2006). 7 Rahman M, Seah W K H, Teo T T, J Mater Process Technol, 63 (1-3) (1997) Choudhury I A & El-Baradie M A, J Mater Process Technol, 77 (1-3) (1998)
7 SATYANARAYANA et al.: OPTIMIZED HIGH SPEED TURNING ON INCONEL Choudhury I A & El-Baradie M A, Proc Inst Mech Eng, Pt B: J Eng Manuf, 212 (B3) (1998) Graham D, Mod Mach Shop, 75 (2002) Ezugwu E O, Int J Mach Tools Manuf, 45(12-13) (2005) Schirra J J, in Superalloys 718, 625, 706 and Various Derivatives, (TMS, The Minerals, Metals and Materials Society, Pittsburgh),1997, pp Schulz H, Abele E & Sahm A, Ann CIRP, 50 (2001) Siems S, Dollmeier R & Warnecke G, Trans NAMRI/SME, 28 (2000) Quan Y M, Wang C Y & He Z W, Adv Mater Manuf Sci Technol Mater Sci Forum, (2004) Behrens A, Kalisch K & Wulfsberg J P, Possibilities and problems of finite element simulation of high speed cutting mechanics, Proc ASME International Mechanical engineering Congress and R&D Exposition, Anaheim, CA, 2004, pp Ezugwu E O & Bonney J, J Mater Process Technol, 134 (2003) Field M & Khales J F, Ann CIRP, 20 (2) (1971) Arunachalam R M, Mannan M A & Spowage A C, Int J Mach Tools Manuf, 44 (2004) Phadke M S, Quality engineering using robust design, (Prentice Hall, Englewood Cliffs, New Jersey), Caydas Ulas, Opt Laser Technol, 40 (2008) Huang J T & Liao Y S, Int J Prod Res, 41 (8) (2003) Guo H, Fuzzy Math, 5 (2) (1985) Kuo C F J & Wu Y S, Int J Adv Manuf Technol, 27 (2006) Lin C L & Lin J L, J Mater Process Technol, 142 (2002) Fisher R A, Statistical methods for research worker, (Oliver & Boyd, London), 1925.
Experimental Investigations for Optimized High Speed Turning on Inconel 718 using Taguchi method based Grey Relational Analysis
Experimental Investigations for Optimized High Speed Turning on Inconel 718 using Taguchi Method based Grey.. 73 Experimental Investigations for Optimized High Speed Turning on Inconel 718 using Taguchi
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