Experimental Investigations for Optimized High Speed Turning on Inconel 718 using Taguchi method based Grey Relational Analysis

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1 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 method based Grey Relational Analysis B. Satyanarayana 1, G. Ranga Janardhana 2 and D. Hanumantha Rao 3 1 Department of Mechanical Engineering, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India, sanbollu@gmail.com 2 College of Engineering, J N T U, Vizayanagaram, India, ranga.janardhana@gmail.com 3 Department of Mechanical Engineering, MVSR Engineering College, Hyderabad, India, dharwada.rao@gmail.com Abstract: This paper presents the experimental investigations for optimum conditions while turning Inconel 718, a Nickel based super alloy which is widely employed in the aerospace industry, in particular in the hot sections of gas turbine engines due to their high temperature strength and high corrosion resistance. It is known as being among the most difficult-to-cut materials. The Taguchi method based Grey Relational Analysis is used for determination of optimum turning process parameters (speed, feed and depth of cut) which minimize the Cutting force, Surface Roughness and Tool flank wear together in CNC High Speed turning of Inconel 718 Nickel based super alloy. The study involved nine experiments based on an orthogonal array, and the results indicate those optimal process parameters as 90 m/min for speed, 0.18 mm/min for feed and 0.5 mm for depth of cut. Also the significant process parameters have been found out for the above process optimization by performing an ANOVA. Confirmation tests with the optimal levels of cutting parameters are carried out in order to illustrate the effectiveness of the method. Validations of the modeled equations are proved to be well within the agreement with the experimental data. Keywords: Inconel 718, High Speed Machining, Grey relational analysis, ANOVA 1. INTRODUCTION 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 (γ ) precipitates and also to a minor extent to the effects of gamma prime (γ ) 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 [5-10]. In almost all of this research the machining has been examined from the manufacturing technology point of view for the improvement of tool materials, 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]. A study indicates that uncoated cemented carbide tools perform better than coated [8]. 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 defense, aerospace, aircraft and automotive. Research on HSM involves a wide variety of work materials ranging from easy-to-cut aluminum alloys (Schulz et al., 2001; Siems et al., 2000) to difficultto-cut hardened steels (Quan et al., 2004; Behrens et al., 2004) and advanced aerospace materials (Ezugwu and Bonney, 2003).

2 74 B. Satyanarayana, G. Ranga Janardhana and D. Hanumantha Rao The machining forces have an important share in the generation of stresses and temperature in the machined surfaces. They further influence the stresses 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 and geometry, and tool coating. Hence, much attention should be paid to surface characteristics of components, that was pointed first by Field and Khales (1971) and then by Arunachalam et al. (2004) 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 of machined Inconel 718. Also, most of these studies include random experiments. Thus, in this experimental work, 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 of Inconel 718. The following sections of this paper describe methodology, plan of the experiments and their execution followed by analysis of the results. 2. AN OVERVIEW OF 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 [13]. 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 in 1982 [14] 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 inter-relationships among multiple performance characteristics effectively. However, the first step of the grey relational analysis is the grey relational generation [15]. 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. 3. EXPERIMENTAL WORK 3.1 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 presented in Table 1. The work material used was Inconel 718 (Ni = %, Cr = 17.5%, Nb = 4.9%, Al = 0.66 %, Ti = 0.96% balance are Fe and other).

3 Experimental Investigations for Optimized High Speed Turning on Inconel 718 using Taguchi Method based Grey.. 75 of 0.3 mm and /or after reaching depth of cut notch wear (V N ) of 0.6mm. Figure 1: CNC Lathe Used for the Experiments Surface Roughness (SR) The other response variable measured was surface roughness (SR). The machined surface roughness was measured by a Mitutoyo make Surftest SJ201(Fig. 3) surface roughness tester of sampling length 0.8mm and least count of 0.01µm. 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. Table 1 Working Geometry of the Tool used in Experiments S. No. Parameter Values 1 Inclination angle 6 2 Orthogonal rake angle 6 3 Orthogonal clearance angle 6 4 Auxiliary cutting edge angle 15 5 Principle cutting edge angle 75 6 Included angle 90 7 Nose radius 0.8 mm Table 2 The Machining Parameters and their Levels Figure 2: Tool Maker s Microscope Parameters Levels Cutting Speed Vc (m/min) Feed, f (mm/rev) Depth of Cut, d(mm) The initial cutting parameters were as follows: Cutting speed 70m/min, a feed rate of 0.18mm/rev, and a depth of cut of 0.5mm. 3.2 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). The tool wear criteria was used as per ISO 3685 i.e. the tools were discarded after reaching average flank wear (VB avg ) Figure 3: Surface Roughness Tester Figure 4: Machined Work Piece 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

4 76 B. Satyanarayana, G. Ranga Janardhana and D. Hanumantha Rao 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. 4.1 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. Table 3 Experimental Layout using an L 9 Orthogonal Array Experiment number Cutting Parameter Level A B C D Cutting Feed Depth Error Speed Rate of cut 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 [16]. Grey Relational Analysis (GRA), a normalized evaluation technique, is extended to solve the complicated multi-performance 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 & surface roughness were performed in the range between zero and one, which is also called the grey relational generation. The normalized data processing for Fc, SR and TFW corresponding to lower-the-better criterion can be expressed as X i (k) = max()() yi k yi k max() yi kmin() yi k 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 kth response, and the max y i (k) is the largest value of y i (k) for the kth response. The ideal sequence is X 0 (k) (k = 1, 2, 3 for F c, SR and TFW respectively). The Grey relational generation is shown in the 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 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 ξ i (k) = min+ ψ max 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 ψ i can be calculated as follows: γ i = 1/() n n k = 1 Wkξi k 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,

5 Experimental Investigations for Optimized High Speed Turning on Inconel 718 using Taguchi Method based Grey.. 77 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 [17-19]. The higher grey relational grade represents that the corresponding experimental result is closer to the ideally normalized value. In other words, optimization of the complicated multiple performance characteristics can 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. Table 4 Grey Relational Coefficients and Grades Exot. Cutting speed Feed Depth of Cutting SR TFW Normalized decision matrix Grey Relational Grey Relational No (m/min) (mm/red) cut force (F c ) (µm) (mm) Coefficient ( = 0.5) Grade (V c ) (f) (d) (N) Fc SR TFW Fc SR TFW Value Rank (N) (µm) (mm) (N) (µm) (mm) From the 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. Table 5 Grey Relational Grades at Different Levels Factor Level 1 Level 2 Level 3 Max-Min Rank Speed Feed DoC Figure 5: GRG of Multiple Performance Characteristic 4.3 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 [20] 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 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.

6 78 B. Satyanarayana, G. Ranga Janardhana and D. Hanumantha Rao Table 6 Results of ANOVA for Multiple Performance Characteristics Parameter SS DoF MS F Contribution % Cutting speed Feed Depth of cut Error Total Confirmation Test After the optimal level of machining parameters has been identified, a verification test needs to be carrying 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: γ* = o γ () m + γi γ i= 1 γ m = total mean grey relational grade, γ i = 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 good agreement between estimated value (0.7389) and experimental values ( ). The increase of grey relational grade from initial factor setting to optimal process parameter setting is of Hence, it may conclude 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. Table 7 Results of the Confirmation Experiment Initial cutting Optimal cutting parameters parameters Prediction Experiment Setting level V 1 F 1 D 1 V 3 F 1 D 1 V 3 F 1 D 1 Cutting Force Surface Roughness Tool Flank Wear Grey relational grade Improvement of grey relational grade = = CORRELATIONS AND CONFIRMATION TESTS The correlation between the factors (cutting speed, feed and depth of cut) and the measured multiple performance m characteristics (F c, SR, TFW) to minimize them to perform machining of Inconel 718 at high cutting speed using lower the better characteristic is obtained by multiple linear regression. The equations obtained are as follows: SR (µm) = V c f d(1) [R 2 = ] TFW (mm) = V c f d (2) [R 2 = ] F C (N) = VC f d (3) [R 2 = ] The multiple linear regression equation based on overall Grey Relational Grade (GRG), as it is a measure of ideally normalized solution, for combined effect of F c, SR and TFW is as follows: F c, SR and TFW (GRG) = V c f d (4) [R 2 = ] Where V c = cutting speed (m/min), f = feed (m/rev) and d = depth of cut (mm) Table 8 shows the cutting conditions and confirmation test results respectively. It was observed that the calculated error for Surface Roughness with maximum value of 12.36% and minimum value of 6.88%, Tool Flank Wear with maximum value of 9.288% and minimum value of 5.58% whereas the cutting force with maximum value of 7.7% and minimum value of 7.2%. Thus, it is proved that the equations (1), (2) and (3) respectively correlate the evaluation of the Surface Roughness, Tool Flank Wear and Cutting Force to perform the machining operation at higher cutting speed with the cutting conditions (cutting speed, feed and depth of cut) with a reasonable degree of approximation. 5.1 Effect of Feed on Overall Grey Relational Grade (F c, SR and TFW) at constant speed (90 m/min) Figure 6 shows the variation of overall grey relational grade with various feed rates at different depth of cuts and at constant speed. As the feed and depth of cut increasing the overall grey relational grade is decreasing means the performance of the process is decreasing (the experimental result is away from the idealized normal value). The higher grey relational grade represents that the corresponding experimental result is closer to the ideally normalized value. Hence, it is a good agreement with the metal cutting theory that at low feed rates and depth of cuts the cutting force, surface roughness and tool flank wear are less.

7 Experimental Investigations for Optimized High Speed Turning on Inconel 718 using Taguchi Method based Grey.. 79 Table 8 Machining Parameters, Experimental Plan and Confirmation Results for High Speed Turning of Inconel 718 Test Cutting speed Feed Depth of Surface Roughness (SR) (µm) Tool Flank Wear (TFW) (mm) Cutting Force (Fc) (N) (m/min) (mm/rev) cut (mm) Experimental Model Error Experimental Model Error Experimental Model Error value value % value value % Value Value % Figure 6: Overall grey Relational Grade V s Feed Rate 6. CONCLUSIONS In this experimental investigations, super alloy Inconel 718 is used, which is a costly material and has got peculiar characteristics which makes 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 showed that the optimal cutting parameters are high cutting speed 90 m/min, lower feed 0.18 mm/rev and lower depth of cut 0.5mm, 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 showed that there is an increase in grey relational grade from the initial factor setting to the optimal process parameter setting is of Thus it may be conclude 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. The confirmation tests shows that the error associated to tool flank wear (maximum value of 9.288% and minimum value of 5.58%) is lower than the error associated with surface roughness (maximum value of 12.36% and minimum value of 6.88%) to perform the machining operation. The error associated with cutting force is in between the Tool flank wear and surface roughness error values. Thus, the effectiveness of the Taguchi-method based grey relational analysis was confirmed by confirmation test results. This research demonstrates how to use Taguchi-method based grey relational analysis for optimizing multiple machining performances with minimum cost and time to industrial readers during high speed machining of Inconel 718 super alloy. References [1] R.C. Reed, The Superalloys, Cambridge University Press, Cambridge, [2] C.T. Sims, N.S. Stoloff, W.C. Hagel, Superalloys II, Wiley, New York, [3] J.F. Radavich, The Physical Metallurgy of Cast and Wrought Alloy 718, in: Superalloy 718- Metallurgy and Applications, [4] N. Saunders, et al., Modelling the Material Properties and Behaviour of Ni-based Superalloys, 2004, pp

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