OPTIMIZATION OF MACHINING PARAMETERS OF WCEDM FOR TITANIUM ALLOY 6242 USING MULTI OBJECTIVE TECHNIQUES

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1 Volume 118 No , ISSN: (printed version); ISSN: (on-line version) url: ijpam.eu OPTIMIZATION OF MACHINING PARAMETERS OF WCEDM FOR TITANIUM ALLOY 6242 USING MULTI OBJECTIVE TECHNIQUES R Prasanna *, a, K.R. Mathevanan b,v. RA. Hareharasuthanraj c, M. Arunraj c, K.Kalaiyarasan d, V. Srimaan d a Assistant Professor, Department of Mechanical Engineering, Karpagam University, Coimbatore , Tamilnadu, India b Assistant Professor, Department of Mechanical Engineering, Karpagam College of Engineering, Coimbatore , Tamilnadu, India c Department of Mechanical Engineering, Karpagam University, Coimbatore , Tamilnadu, India d Department of Mechanical Engineering, Karpagam College of Engineering, Coimbatore , Tamilnadu, India 1 prasannaravi.g@gmail.com, krmathidurai@gmail.com Abstract: Titanium alloy are difficult to machine in traditional machining method. Wire cut electrical discharge machining hybrid manufacturing technology which enables machining of all engineering materials. This article deals with the investigation on optimization of process parameter of WCEDM of Titanium alloy (Ti- 6Al- 2Sn- 4Zr- 2Mo). Material removal rate, surface roughness, were studied against the wire cut EDM process parameters, such as pulse on, pulse off, voltage, wire feed rate. Contribution of this parameter on responses was determined by analysis of variance. Regression model were obtained for material removal rate, surface roughness, kerf angle. Values get predicted by using RSM technique and Grey relation technique. Keyword-Ti-6242, wire cut EDM, DOE, ANOVA, RSM, GRE, material removal rate, surface roughness. 1. Introduction WCEDM is an unconventional machining process it has high accuracy than AJM & AWJM. The titanium 6242 alloy is most widely used in automobile industries, aerospace industries, and in bio-medical industries. In this paper you could known about the machining parameters for high MRR and SF. Titanium aluminides represent an important class of alloys with a unique set of mechanical properties and can lead to substantial payoffs in the automotive industry, power plant turbines, and aircraft engines. Around 30 years of intensive research has achieved the maturity level of TiAl-based alloys that is sufficient to consider this class of materials for critical rotating components in commercial jet engines[1].one explanation behind for thermal deformation is constantly ignored is due to non-macro cutting force during the WEDM process. However, as a matter of fact, thermal effects play the dominant role in WEDM except for ultrashort discharges. The residual stress leads to thermal deformation[2].advance it is discovered that it is to a great degree hard to machine by regular strategy due to its excellent strength property. Different aspects of machining have been investigated by several researchers[3,4]. Huang et al investigated experimentally and optimized machining parameters on the surface roughness, gap width and the depth of white layer on the machined work piece.[5] Tarang et al utilized a feedforward neural network to associate cutting parameters with ouput response. A simulated annealing (SA) algorithm calculation was then connected to the neural network to solve for the optimal cutting parameters.[6].sf quality is as fine as lra. In addition, WEDM uses deionised water instead of hydrocarbon oil as the dielectric fluid and contains it within the sparking zone. The deionised water is not suitable for conventional EDM as it causes rapid electrode wear, but its low viscosity and rapid cooling rate make it ideal for WEDM[7].Sanchez et al provided a literature survey on the EDM of advanced ceramics, which have been commonly machined by diamond grinding and lapping. In the same paper, they studied the feasibility of machining boron carbide (B4C) and silicon infiltrated silicon carbide (SiSiC) using EDM and WEDM.[8]. Dielectric flow rate is the rate at which the dielectric fluid is circulated. Flushing play an important 925

2 role for machining process. High flow rate of dielectric is necessary for machining with higher values of pulse power and to cut the workpiece which have more thickness. Low input pressure is used for thin work piece [9]. Machining of titanium alloy using conventional and unconventional technique like WJM, AWJM with precise technology is difficult. In order to overcome the bove mentioned problem wire cut EDM are used for the machining of titanium alloy (Ti- 6Al- 2Sn- 4Zr- 2Mo) which are mainly used for biomedical implant and aerospace industry.this paper which shows clearly about machining of titanium alloy through wire cut EDMby using RSMand GRE method with the response parameter. 2. Materials and Experimental Details Table 1. Material composition COMPOSITION ELEMENT PERCENTAGE Titanium, Ti Aluminum, Al Tin, Si Zirconium, Zr Molybdenum, Mo Iron, Fe 0.25(max) Oxygen, O 0.15(max) Silicon, Si Carbon, C 0.050(max) Nitrogen, N 0.050(max) Hydrogen, H 0.015(max) Other each 0.10(max) 2.1 Work Material In the present work, titanium 6242 of 5mm thickness was used as the work material. Material composition of the workpiece explained in table 1. Titanium alloy are metals containing a blend of titanium and other chemical composition. Grade 6Al 2Sn 4Zr 2Mo titanium alloy is a near alpha alloy. It was developed to handle operations in high temperature application upto C. It is excellent strength and corrosion resistance with comparatively good weldability and fabricability. 2.2 Experimental Setup Machining of titanium alloy is done by CNC Electro Discharge machine. Job size acceptable of this machine are mm, max taper cutting angle are ±30º on 50 mm job, wire which is made up of brass and its diameter are 0.25, maximum cutting speed are 120 mm/min, axis of this machine are X,Y direction, Deionized water are used as dielectric medium. The process parameters such as Pulse on, Pulse off, Voltage, Wire feed rate were selected by earlier work of titanium alloy. Table 2, 3 shows the process parameter and their levels, Orthogonal Array. After machining the sample piece it was taken to analyse MRR, Surface roughness. The material removal rate (MRR) for Wire cut EDM is calculated by using the equation MRR = F D w H Where F is the machine feed rate(mm/min); D w Dwis wire diameter(mm) H- thickness of the work piece (mm). The average surface roughness (Ra) of WCEDM machined sample was measured by using Mitutoyo SJ- 310 surface roughness measurement device. The measurement was taken at a distance of 5 mm from top, middle, bottom of the cut surface. Each test was carried out trice and the averages of the result are taken for the study. The level and set of designed orthogonal array shown in table 2 and 3 respectively S no Table 2. Parameters and their levels PARAMETER UNITS LEVEL 1 1 PULSE ON (A) 2 PULSE OFF (B) 3 VOLTAGE (C) 4 WIRE FEED RATE(D) LEVEL 2 LEVEL 3 µs µs V m/min Table 3. L9 Orthogonal Array Run PULSE ON(µs) PULSE OFF(µs) VOLTA GE (V) WIRE FEED RATE (m/min)

3 3. Results and Discussion 3.1 Responses for the Experiment Sets The machinability of titanium alloy are prepared and the test are conducted. The results of responses are reported here. This includes material removal rate, surface roughness. Table 4 show responses for the experiment sets material removal rate and surface roughness. Table 5 show the variance and contribution percentage value for Material removal rate. Voltage having more contribution than other process parameter. Wire feed rate having least contribution for material removal rate. Table 6. Delta rank for material removal rate Table 4. Responses for the experiment sets Run MRR (mm 3 /min) Surface roughness (µ µm) Response Surface Experimental Analysis Taguchi experimental analysis is made using the popular software specifically used for design of experiment application known as MINITAB 17. It is used to study the effect of machining parameter such aspulse on, Pulse off, Voltage, Wire feed rate and with two output parameter such as Material removal rate, surface roughness. Table 6 shows delta rank for material removal rate, delta rank mainly for the hire the preference and give the rank based upon the performance or contribution of the factor. Table 7. ANOVA for Surface roughness Table 5. ANOVA for (MRR) Table 7 show the variance and contribution percentage value for Surface roughness (Ra), Voltage having more contribution than other process parameter. Pulse on time having zero contribution for surface roughness. 927

4 Table 8. Delta rank for Surface roughness Table 8 shows delta rank for Surface roughness, delta rank mainly for the hire the preference and give the rank based upon the performance or contributionn of the factor. 3.5 Effect of Signal Noise Ratio Figure 1. Main effects Plot for Data Means MRR vs Parameters This paper deals maximise the material removal rate and to minimise surface roughness. The experimental data are further transformed into signal to noise (S/N) ratio. There are several ratios available depend upon the type of responses. Lower the better (LB), Nominal the best (NB), higher the better (HB). Therefore HB is for MRR and LB is for surface roughness. Table 9. S/N ratio for output response Run S/N ratio of MRR in db S/N ratio of Ra in db Table 9 show S/N ratio for outpu response of material removal rate, Surface roughness. This signal to noise ratio which used to analysis the main effects plots for MRR in db. Figure 2. Main effects Plot for S/N ratio MRR vs Parameters The main effect plot for data means, S/N ratio of MRR for wire cut EDM is represented in the Figure-1,2. Optimal conditions for maximum material removal rate are achieved by as Pulse on time 6(µs), Pulse off time 6(µs), Voltage 50(V), Wire feed rate 2(mm/min).The material removal rate which controlled mainly by pulse on, pulse off, voltage, wire feed rate. Clear to see that material removal get increased by increasing pulse on time and decreasing the pulse off time, voltage and wire feed rate. 928

5 Surface roughness = A B C D 3.8 Grey Relation Optimization Figure 3. Main effects Plot for Data Means MRR vs Parameters In this paper multiple performance characteristic are analysed using grey relation. In this method multiple performance characteristic can be converted into single grey relation grade Steps in grey relation analysis: Step 1: Transform the original response data into S/N ratio (Y ij ) using the appropriate formula depending on the type of quality characteristic. Step 2 : Normalize Y ij as Z ij (0<Z ij 1) by the following formula to avoid the effect of using different units and to reduce variability. Normalization is a transformation performed on a single input to disturb the data evenly and scale it into acceptable range for the further analysis. Z ij = Normalized value for ith experimental/ trial jth dependent variable/ response Y ij min (y ij, i= 1,2,, n) Z ij = (1) Max(y ij, i= 1,2,.,n)- min (y ij, i= 1,2,., n) (to be used for S/N ratio with larger the better case) Figure 4. Main effects Plot for S/N ratio Ra vs Parameters The main effect plot of S/N ratio of Ra for wire cut EDM is represented in the Figure-3,4. Optimal conditions for minimum surface roughness are achieved by as Pulse on time 5(µs), Pulse off time 8(µs), Voltage 80(V), Wire feed rate 6(mm/min). Surface roughness specifies the state of machined surface. Observation of figure 4 indicates a surface roughness minimized by increasing pulse voltage and wire feed rate. 3.7 Regression Equations The regression equation of the following responses are listed below MRR = A B C D max (y ij, i= 1,2,, n)-y ij Z ij = Max (y ij, i= 1,2,.,n)- min (y ij, i= 1,2,., n) (to be used for S/N ratio with smaller the better case) Step 3: Compute the grey relational coefficient (GC) for the normalised S/N ratio values. λ GC ij = (3) λ i = 1, 2, 3, n experiments. j = 1, 2, 3., m responses. GC ij = grey relation coefficient for the ith experiment/ trial and jth dependent variable/ response. = absolute difference between Y oj and Y ij which is deviation from target value and can be treated as quality loss. (2) 929

6 Y oj = optimum performance value or the ideal normalized value of jth responses. Y ij = the ith normalized value of the jth response/depend variable. min = minimum value of max = maximum value of λ = is the distinguishing coefficient which is defined in the range 0 λ 1( the value may be adjusted on the practical needs of the system) Step 4 : The grey relational grade was determined by averaging the grey relational coefficient corresponding to each performance characteristic. It is given in the Table 11. The overall performance characteristic of the multiple response process depends on the calculated grey relational grade. The grey relational grade can be expressed as G i = (1/m )[ GC ij ] (4) Where m is the number of responses Table 10, shows that grey relation coefficient and grey relation grade, here normalized S/N ratio for MRR and Ra are calculated by using appropriate formula depending on the type of quality characteristic. MRR, Ra are calculated using the equation 1, 2 respectively. GC MRR, GC Ra are calculated by using the equation 3. Finally grey relation grade are calculated by using the equation 4. The main effect of MRPI (mean of MRPI ) are tabulated in table 11. Table 10. Grey relation coefficient and grey relation grade Normalized S/N ratio MR R Ra GC M RR GC R MRR Ra a Gi Table 11. Mean for MRPI values for illustration Factors Level1 Level2 Level 3 PULSE ON (µs) PULSE OFF(µs) VOLTAGE (V) WIREFEED RATE (m/min) Confirmation Test After identifying the optimal process parameters, the confirmation test is to be conducted to validate the analysis. In the confirmation test, an experiment has been conducted using the optimal process parameters settings The optimum level of parameter shown in Table 12. Table 12. Optimum level of parameter with response result Optimal process parameter Predicted Experimental Level A3B3C1D1 A3B3C1D1 MRR(mm 3 /min) Ra(µm) Conclusion In this paper optimum parameters of Material removal rate, surface roughness, of titanium alloy is studied. Experiments were conducted with different process parameter for determination of optimum condition using multi objective technique. This paper proves that voltage play an important role for material removal rate and surface roughness. The contribution of voltage for material removal rate are 65% and surface roughness are 85% The increases pulse on time with minimum pulse off time, voltage and wire feed rate leads to increases in material removal rate and minimize Surface roughness. The optimum value area3b3c1d1. This paper shows that pulse on time had zero contribution for surface roughness. 930

7 Acknowledgement The authors wish to thanks for establishing the centre for Advanced Machining at KLN COLLEGE OF ENGINEERING AND TECHNOLOGY. References [1] Leyens C, Peters M (2005) Titanium and titanium alloys fundamentals and applications. Wiley- VCH Verlag GmbH & Co. KGaA, Weinheim [2] Kawakami T, Kunieda M. Study on factors determining limits of minimum machinable size in micro EDM. CIRP Ann. Manuf. Technology 2005; 54(1): [3] Dhanapal. P, Mohammed Nazirudeen. SS, Multiresponse optimization of carbidic austempered ductile iron production parameters using Taguchi technique, Journal of Scientific and Industrial research 70, [4] A.R.C. Sharman, D.K. Aspinwall, R.C. Dewes, D. Clifton, P. Bowen, The effects of machined workpiece surface integrity on the fatigue life of titanium aluminide, Int. J. Mach. Tools Manuf. 41 (2001) [5] N. Zaltin, M. Field, Procedures and precautions in machining titanium alloys, Titanium Sci. Technol. 1 (1973) [6] Huang JT, Liao YS, Hsue WJ (1999) Determination of finish cutting operation number and machining parameters setting in wire electrical discharge machining. J Mater Process Technology 87, [7] Tarng YS, Ma SC, Chung LK (1995) Determination of optimal cutting parameters in wire electrical discharge machining. Int J Mach Tools Manuf 35: [8] E.A. Huntress, Electrical discharge machining, Am. Machinist 122 (8) (1978) [9] J.A. Sanchez, I. Cabanes, L.N. Lopez de Lacalle, A. Lamikiz, Development of optimum electro discharge machining technology for advanced ceramics, Inter. J. Adv. Manuf. Technol. 18 (12) (2001) [10] MohdSyafiq Bin Dzulkapli Study the Effect of Wire-EDM Parameters on Surface Roughness for Machining Die-Steel Faculty of Manufacturing Engineering March

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