Performance Evaluation of Ordinary and Wiper Inserts in Dry Turning of Inconel 718 Super Alloy using Grey-Fuzzy Algorithm A Hybrid Approach

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1 5 th International & 26 th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12 th 14 th, 2014, IIT Guwahati, Assam, India Performance Evaluation of Ordinary and Wiper Inserts in Dry Turning of Inconel 718 Super Alloy using GreyFuzzy Algorithm A Hybrid K.Venkatesan 1, R.Ramanujam 1*, Vimal Saxena 1, Rachit Pandey 1 School of Mechanical and Building Sciences, VIT University, Vellore, India * Corresponding author: ramge2k@yahoo.com Abstract The superior thermomechanical properties of Inconel 718 super alloy make it suitable for high end temperature applications such as aerospace, nuclear, chemical and petrochemical industries. Even though these properties are desirable from design perspective but they put constraints on machining while achieving end products. Characteristics such as retaining of high strength at elevated temperature, work hardening during machining and its ability to weld and form builtup edge are few of the challenges and hence these alloys are designated as difficulttocut materials. In the past decade, many researches have been conducted to analyze the machinability of Inconel 718 alloy by varying the cutting conditions (speed, feed and depth of cut), cutting tool materials and tool geometry. Still there is need for continuing research for achieving improved machinability with new generation wiper cutting inserts at optimal setting of cutting conditions. The present work focuses on the performance evaluation of ordinary and wiper inserts on machinability of Inconel 718 by employing hybrid GreyFuzzy approach. The experiments were conducted according to Taguchi s L9 experimental design for three different levels of cutting parameters viz cutting speed, feed rate and depth of cut. Surface roughness and cutting force are chosen as performance characteristics. The obtained results indicated that coated inserts with wiper geometry performed better in terms of surface roughness within the designed cutting parameters using GreyFuzzy hybrid approach. Keywords: Inconel 718, Taguchi method, Grey Analysis, Fuzzy inference system 1 Introduction Nickel based alloys form a superior class of metallic materials which possess advanced properties to withstand stringent working conditions. These super alloys, with excellent thermomechanical properties have found widespread applications in aerospace, nuclear, chemical and petrochemical industriesbyd. Dudzinskiet. al (2004). Inconel 718, a nickel based super alloy, is the most dominant alloy in production which finds its use in high temperature environment such as engine parts of aircraft, steam turbine power plant, space vehicles, medical application etc. It is a precipitation hardened superalloy with superior thermomechanical properties that makes it suitable for high end temperature application as well as highly corrosive and oxidizing environment by Ezugwu (2005). During the machining process, the tool workpiece interface undergoes a severe plastic deformation due to excessive heat generation. This effect in conjugation with work hardening results in loss of tool substrate which leads to series of flaws, such as high surface roughness, excessive tool wear leading to shorter tool life, low productivity, and excessive power consumption etc.by Braghini et. al. (2004) and Chao Xueet. al (2011).Improper selection of machining parameters is one of the major causes leading to these problems. Researches have been carried out to determine the optimum machining parameters to minimize tool wear. Various statistical and hybrid based approaches have been mentioned in the literature. Literature highlights that Taguchi based experimental design proves to be highly productive as it reduces the number of experiments thereby reducing the cost as well as time for the experiment but it fails to optimize the problems with multiple objectives. In order to overcome this problem various researchers have employed various theories along with Taguchi method to achieve multi response optimization byissam Hanafiet.al (2012),Rajmohan et.al (2013) and Krishnamoorthy et.al. (2012).The purpose is to conglomerate the multiple objectives into single objective (multiresponse into equivalent quality index) which can further be optimized using Taguchi s method by YihfongTzeng et al. (2007). The surface quality of the machined part not only depends on the machining condition but also the type of cutting insert used. The behavior of the tool can be attributed to the type of coating used. Coatings increase the wear resistant properties of the tool and may also reduce cutting forces andtemperatures.jindal et al (1999) did a comparative investigation of PVD TiN, TiCN, and TiAlN coating on cemented carbides at a cutting speed of 46m/min and 76m/min. It was found out that TiAlN have superior properties than TiCN or TiN as it possesses higher hardness at elevated temperature (T 750ºC). This is due to the fact that 4491

2 Performance Evaluation of Ordinary and Wiper Inserts in Dry Turning of Inconel 718 Super Alloy using GreyFuzzy Algorithm A Hybrid TiAlN forms a protective layer of Al and an intermediate layer comprising of Ti, Al, N and O 2 leading to higher oxidation resistance. Similar results were obtained by Prengel et al (2001) who concluded that multilayer coated carbide tool performed better than monolayer coated carbide at the higher speed of 76m/min. In this present work, performance of two different cutting tools has been discussed. The work focuses on finding the optimal machining and tool dependent parameters setting for machining of Inconel 718 to achieve better surface finish and lower cutting force under dry machining conditions using coated carbide tools of different geometries. 2 Experimental Methods The details of test specimen, cutting conditions, measurements and methodology adopted for the present study are described in the following sections. 2.1 Cutting insert and Measurements Dry turning operations based on Taguchi s L9 experimental array were carried out on Inconel 718 bar of diameter 35mm and length 600mm in a medium duty lathe (7.5kW, 1600rpm). The cutting tools used were PVD coated carbide inserts, designatedd as KC5525MS and KC5010FW manufactured by Kennametal. Both grades are advanced PVD coated finegrain tungsten carbide insert specifically designed for machining high 10% cobalt for temperature alloys. There substrate has excellent toughness and deformation resistance. The inserts used were of ISO coding CNMG MS (Grade KC5525)and CNMG120408FW (Grade KC5010) were mounted on a tool holder designated by ISO coding PCLNR 2525M12. Surface roughness measures (Ra, Rt) and Cutting force (Fz) were considered as performance characteristics and measured by Mahr surf test and Kistler dynamometer respectively. 2.2 Methodology The experimental design was based on Taguchi s orthogonal array which involves selecting response variables, independent variables, and their interactions. The process flow chart of the experimental steps and optimization procedure are shown in Figure 1. The levels of the process parameters were selected in order to cover a sufficiently wide range of possible cutting conditions.the observations were recorded and tabulated in Table 1. Further, grey relational analysis is used for optimization of input parameters of the turning process to achieve better quality of surface finish and lower cutting force. The grey system to adequate for studies which involves poor, incomplete, and uncertain data which are designated as Grey data. It is used for solving the complicated interrelationship among the multiple responses. Concept of fuzzy logic is then incorporated into this multivariate system so as to get an improved greyfuzzy grade. The obtained grey relational coefficients are shown in Table2. The applied fuzzy rules and Rule viewer are mentioned in Table 3 and Figure Results and Discussion This section presents the results obtained by greyfuzzy reasoning analysis, ANOVAA for identifying the most contributing turning parameters, the optimum condition obtained from the mean effect plot. 3.1 GreyFuzzy Reasoning Analyses The grey fuzzy grades weree calculated using MATLAB The fuzzy inference system sodeveloped incorporated triangular membership function and a set of 27 IF THEN rules were formulated to fuzzify the grey relational coefficient of each response. Each input had three numbers of membership function and the outpu had 6 numbers of membership functions as shown in Figure 3. The grey fuzzy inference fuzzygrades so obtained from the system for both inserts are shown in Table 2. It is clear from values that trial 3 for ordinary geometry insert and trial 7 for wiper geometry inserts has theoptimal input parameters for the turning operation. From the analysis, the optimum conditions arrived for w/o wiper insert are:(i) Cutting speed (50 m/min),iii) Depth of cut (0.2 mm) and (iv) Feed rate (0.103 mm/rev) and for wiper insert are:(i) Cutting speed (70 m/min),ii) Depth of cut (0.2 m) and (iii) Feed rate (0.103 mm/rev)shown in Table 4 & ANOVA Analysis: Grey Fuzzy Assessing the factors and its effects on surface roughness (Ra and Rz) and cutting force (Fz) of Inconel 718 has been carried out through response table and analysis of variance (ANOVA) technique.response tables are used to simplify the calculations needed to analyze the GreyFuzzy data (MPC) for both inserts. The complete response table for fuzzy data in orthogonal array is shown in Table 4 & 5. From the maxmin range method, the effect of each control factor on the MPC can be determined. From Table 4 it is clear that control factor feed rate has the most significant effect on the conventional insert (KC5525MS), followed by the factors depth of cut and cutting speed. The larger the grey relational grade or the fuzzy reasoning grade, the better is the multiple process response. Therefore, the optimal machining parameters are cutting speed at level v2, feed rate at level f1and depth of cut at level d2. Similarly, for wiper insert (KC5010FW), the optimal machining parameters 4492

3 5 th International & 26 th All India Manufacturing Guwahati, Assam, India Technology, Design and Research Conference (AIMTDR 2014) December 12 th 14 th, 2014, IIT are cutting speed at level v3, feed rate at level f1and depth of cut at level d1. START Orthogonal array experiments design (L9 orthogonal array is Experiment progression Defining the machining performances evaluation Normalizing the experimental results. Generation of grey relational coefficients Grey Relational Coefficient ( Grey Relational Analysis Grey relational generating, x*(k) Fuzzy inference system START (FUZZY LOGIC) Fuzzifier Inference engine Fuzzy Rules Defuzzifier GreyFuzzy Reasoning Grade FMPI Small Ra Small Rt Small S Medium M S Large M Ra Medium Rt Small MS Medium M Large L M Ra Large Rt Small M Medium L M Large LM Cutting Force Medium Large M S M M M L M M M L M M L M L M L M LM LM L M L L L Fuzzy Rules Matrix Determine grey grades fuzzy relational Get optimal control factor level with effect plots. STOP Selection of optimal machining parameters (Confirming Test) Proceed with ANOVA validation 4493

4 Performance Evaluation of Ordinary and Wiper Inserts in Dry Turning of Inconel 718 Super Alloy using GreyFuzzy Algorithm A Hybrid Figure 1 Process Conceptualization Table 1Experimental value: Cutting force and Surface roughness components Exp. No. speed (m/min) Cutting Parameters KC5525MS KC5010FW feed (mm/rev) doc (mm) Ra Rt Fz (kn) Ra Rt Fz (kn) Table 2Fuzzy values: Fuzzy Multi performance index (MPI) Exp.No KC5525MS Grey Relational Grey Relational Coefficient Grey Grey Rank coefficient Grade Fuzzy (Ra) (Rt) (Fz) (Ra) (Rt) (Fz) KC5010FW Grey Grade Grey Fuzzy Rank Membership function for Ra Membership function forrt 4494

5 5 th International & 26 th All India Manufacturing Technology, Design and Research Conference (AIMTDR 2014) December 12 th 14 th, 2014, IIT Guwahati, Assam, India Membership function forfz Membership function formpc Figure 2 Membership for input Ra, Rt, Fz and MPC variable Table 4 Response Table for KC5524MS Table 5 Response Table for KC5010FW Level Delta Rank v * f 2.749* d * Level Delta Rank v f 1.598* d 3.925* * Table 6 ANOVA results for KC5525MS(without wiper) Factor DF SS MS F P% v f d Error Total R 2 =93.6%, R 2 (adj)=93.04% Analysis of Variance (ANOVA) was employed to verify the fitnessofthe statistical model (Table 6& 7). Regression coefficient of the fit is 93.6% for KC5525MS and 96.2% for KC5010FW,signifying that the regression model provides an excellent description of thecorrelation between the factors and the response. Contribution of various variable and theirinteractions are shown in Figure 4 for KC5525MS and Figure 5 for KC5010FW.Of the various variables feed rate contributes to the maximum effect of 68.36% on machinability characteristics as linear and another 20.8% as depth of cut, followed by cutting speed with 6.78% respectively for KC5525MS. Similarly, for Table 7 ANOVA results for KC5010FW(with wiper) Factor DF SS MS F P% v f d Error Total R 2 =96.2%, R 2 (adj)=87.67% KC5010FW feed rate contributes to the maximum effect of 61.85% on machinability characteristics as linear and another 32.78% as cutting speed, followed by depth of cut 2.98% respectively. From the present study, coated inserts with wiper geometry KC5010FW has the better performance when compared to conventional coated cutting inserts KC55525MS, in terms the surface roughness and cutting force of the workpiece. In particulate, the mean ratios value (L9) for roughness and force parameters were (Ra, Rt and Fz) is of (1.7, 1.7and 1.57) respectively. Moreover, the study reveals that the estimated GreyFuzzy reasoning grade accomplished effectively, in particular the optimization of surface roughness and cutting force in the dry turning operation at multiple quality characteristics 4495

6 Performance Evaluation of Ordinary and Wiper Inserts in Dry Turning of Inconel 718 Super Alloy using GreyFuzzy Algorithm A Hybrid 4. Conclusion The paper present a study about machining of Inconel 718 with wiper and conventional coated carbide insertsbasedongreyfuzzy logic analysis coupled with Taguchi method.themajor conclusions drawn fromthe present investigation are: The analyses are carried out with the assist of the statistical analysis software package. It can be seen that the ANOVA model for Geryfuzzy reasoning grade is suitable in order to predict the contribution variables of the surface roughness and cutting force close to those readings recorded experimentally with a 95% confident level. Feed rate is the most variablefactorin this study.the contribution for KC5525MS has been found equal to 68 % (feed rate),21% (depth of cut) and 4% (cutting speed) and for KC5010FW the contribution factor are 61%, 32% and 16% for feed rate, cutting speed and depth of cut respectivelyby applying Geryfuzzy logic analysis. The optimum parameter values for KC5525MSas cutting speed of 50 m/min, feed rate of mm/min and depth of cut of 0.4 mm for and as cutting speed of 70 m/min, feed rate of mm/min and depth of cut of 0.2 mm for KC5010FW. KC5010FW has the better performance compared than KC5525MS in terms of roughness Parameters.The mean ratios value (L9) for roughness and force parameters were (Ra, Rt and Fz) is of (1.7, 1.7 and 1.57) %. It is observed that simultaneous improvement in multiple responses of machining parameter is greatly improved by GreyFuzzy Logic Analysis. Figure 4 Contribution of Process variables for KC5525 and KC5010FW References Dudzinski,D.,Devillez,A.,Moufki, A.., Larrouquere,D.,Zerrouki, V., and Vigneau,J., (2004), A review of developments towards dry and high speed machining of Inconel 718 alloy,international Journal of Machine Tools & Manufacturing, Vol. 44(4), pp Ezugwu, E.O., (2005), Key improvements in the machining of difficulttocut aerospace super alloys, International Journal of Machine Tools Manufacturing, Vol. 45, pp Braghini Jr., A., Bezerra, A.A., Coelho, R.T., and Silva, L.R., (2004), Some effects of cutting edge preparation and geometric modifications when turning Inconel 718 at high cutting speeds,journal of Material Processing Technology, Vol.148 (1), pp Chao Xue., Wuyi Chen, (2011), Adhering layer formation and its effect on the wear of coated carbidetools during turning of a nickel based alloy,wear, 270, pp IssamHanafi., AbdellatifKhamlichi., Francisco Mata Cabrera., Pedro J. Nunez Lopez.,andAbdullahJabbouri., (2012), Fuzzy rule based predictive model for cutting force in turning ofreinforced PEEK composite,measurement, Vol. 45, pp Krishnamoorthy A.,RajendraBoopathyS., Palanikumar, K., and Paulo DavimJ., (2012),Application of grey fuzzy logic for the optimization of drilling parametersfor CFRP composites with multiple performance characteristics, Measurement 45 (2012) Rajmohan,T.,Palanikumar, K., and Prakash, S., (2013 ),Greyfuzzy algorithm to optimise machining parameters in drilling ofhybrid metal matrix composites, Composites: Part B, Vol. 50, pp YihfongTzeng., and Fuchen Chen., (2007), Multiobjective optimisation of highspeed electricaldischarge machining process using a Taguchi fuzzybased approach, Materials and Design, Vol. 28, pp Jindal, P.C.,Santham, A.T.,Schleinkofer and A.F. Shuster., (1999), Performance of PVD TiN, TiCN and TiAlN coated cemented carbide tools in turning, International Journal of Refractory Metals & Hard Materials, Vol. 17, pp Prengel., Jindal, Wendt, Santhanam, Hedge and R.M. Penich., (2001), A new class of high performance PVD coating for carbide cutting tools, Surface, Vol.139, pp