Optimization of process parameters of wire EDM for material removal rate using Taguchi technique
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1 Indian Journal of Engineering & Materials Sciences Vol. 23, August 2016, pp Optimization of process parameters of wire EDM for material removal rate using Taguchi technique Mandeep Kumar & Hari Singh Department of Mechanical Engineering, National Institute of Technology, Kurukshetra , India Received 10 July 2015; accepted 14 July 2016 In this paper, the material removal rate (MRR) of Inconel X750 (Nickel based alloy) subjected to wire electrical discharge machining (WEDM) is studied and optimized by using the Taguchi technique. A 0.25 mm diameter plain brass wire is used as the tool electrode for cutting the material. Experiments are designed and conducted using Taguchi s L-27 orthogonal array. Six input process parameters of WEDM such as spark gap voltage (SV), pulse-on time (T ON ), pulse-off time (T OFF ), wire feed rate (WF), peak current (IP) and wire tension (WT) and three interactions (T ON *T OFF, T OFF *SV, T ON *SV) are chosen as variables to study the process performance. Three trials are conducted and the average is chosen as response at that particular experimental condition. The analysis of variance (ANOVA) is carried out to determine the level of importance of the machining parameters on the MRR. Confirmation experiments are carried out to check the accuracy of the optimized results. The Investigation indicated that material removal rate increases with increase in pulse-on time and decreases with increase in pulse-off time. Keywords: Wire electrical discharge machining, Spark gap voltage, Material removal rate, Wire feed rate, Taguchi technique Inconel X-750 is a precipitation-hardenable nickelchromium alloy used for its corrosion and oxidation resistance and high strength at temperatures up to 1300 F. Although much of the effect of precipitation hardening is lost with increasing temperature over 1300 F, heat-treated material has useful strength up to 1800 F. Alloy X-750 also has excellent properties down to cryogenic temperatures. Inconel alloys are oxidation and corrosion resistant materials well suited for service in extreme environments subjected to pressure and heat. This alloy has a high hardness value ranging in between BHN. So, it is difficult to machine this material with conventional machining. WEDM (wire electrical discharge machining) can be one option to machine this material. The WEDM has become an important nontraditional machining process, widely used in the aerospace, nuclear and automotive industries. This is because the WEDM process provides an effective solution for machining hard materials with intricate shapes, which are difficult to machine by conventional machining methods. Figure 1 represents the schematic diagram of WEDM 1. In general, WEDM is perceived to be an extremely accurate process and there are various reasons for this perception. Firstly, in WEDM, there is no direct contact between tool and work-piece; as a result, the adverse effects such as mechanical stresses, chatter and vibration normally present in conventional machining are eliminated. Secondly, the wire used as a cutting tool has high mechanical properties and small diameter producing very fine, precise and accurate cuts. Finally, the movements of the workpiece are controlled by a highly accurate CNC (Computer Numeric Controlled) system; as a result the effects of positioning errors present in traditional machining are eliminated. There is a lot of work done on WEDM in past. Miller et al. 2 studied the effect of two input factors, i.e., spark on-time duration and spark on-time ratio in *Corresponding author ( mandeep_257@rediffmail.com) Fig. 1 Schematic diagram of WEDM 14
2 224 INDIAN J. ENG. MATER. SCI., AUGUST 2016 WEDM. The MRR (material removal rate) and surface integrity were selected as the responses of interest. Singh and Garg 3 investigated the effects of various process parameters of WEDM on material removal rate of hot die steel H-11 using one factor at a time approach. They found that the material removal rate directly increases with increase in pulse on time and peak current while decreases with increase in servo voltage and pulse off time. Hascalyk and Caydas 4 investigated the machining characteristics of AISI D5 tool steel in wire electrical discharge machining process. During experiments, parameters such as open circuit voltage, pulse duration, wire speed and dielectric fluid pressure were changed to explore their effect on the surface roughness and metallurgical structure. Optical and scanning electron microscopy, surface roughness and micro hardness tests were used to study the characteristics of the machined specimens. Hewidy et al. 5 highlighted the development of mathematical models for correlating various WEDM machining parameters of Inconel 601 material-- peak current, duty factor, wire tension and water pressure--with the metal removal rate, wear ratio and surface roughness. The material was successfully machined by WEDM with a volumetric material removal rate of 8 mm 3 /min and surface finish of less than 1 µm. The response surface methodology (RSM) was applied for analysis of the data. Kanlayasiri and Boonmung 6 investigated the effects of machining variables on the surface roughness of wire-edmed DC53 die steel. The machining variables investigated were pulse-peak current, pulseon time, pulse-off time, and wire tension. Analysis of variance (ANOVA) technique was used to find out the variables affecting the surface roughness. Goswami and Kumar 7 proposed a multi response optimization method using utility concept for wire electrical discharge machining of Nimonic 80-A alloy. The material removal rate and surface roughness along with surface topography of the machined surface were the investigated machining characteristics. Kuriakose and Shunmugam 8 investigated multi-objective optimization of wire EDM process. Titanium alloy was chosen as the work material and work-piece thickness was kept as 60 mm. The applied voltage, ignition pulse current, pulse-off time, pulse duration, servo-control reference mean voltage, servo-speed variation, wire speed, wire tension and injection pressure were taken as the input parameters. Zinc-coated brass wire of 0.25 mm diameter was used for all the experiments. Experiments were planned using a factorial design based on Taguchi s L-18 orthogonal array. Non- Dominated Sorting Genetic Algorithm (NSGA) was used to optimize the cutting speed and surface roughness. Khanna and Singh 9 identified process parameters that affect metal removal rate and surface roughness in machining of cryogenic treated D-3 material on wire-cut electric discharge machine tool. Central composite design was used to plan and conduct the experiments. Puri and Bhattacharyya 10 carried out an extensive study of the wire lag phenomenon in Wire-cut Electrical Discharge Machining and established the trend of variation of the geometrical inaccuracy caused due to wire lag with various machine control parameters. All the machine control parameters were considered simultaneously for the machining operation which comprised a rough cut followed by a trim cut. Taguchi s L 27 (3 13 ) orthogonal array was selected as an experimental design. Goswami and Kumar 11 investigated the surface integrity, material removal rate and wire wear ratio of Nimonic 80A using WEDM. Taguchi's design of experiments methodology was used for planning and designing the experiments. Scanned electron microscopy (SEM) was also performed on the machined samples to investigate the effect and microstructure of the samples after machining. Khanna and Singh 12 optimized the effects of various process parametrs of WEDM like pulse width, time between two pulses, maximum feed rate, servo reference mean voltage, short pulse time and wire mechanical tension to get their maximum impact on MRR while machining D-3 material. Liao et al. 13 proposed an approach to determine parameters setting of WEDM. Based on the Taguchi quality design method and the analysis of variance, the significant factors affecting the machining performance such as metal removal rate, gap width, surface roughness, etc. were determined. Mathematical models relating the machining performance and various machining parameters were established through regression analysis. Muthuraman and Ramakrishnan 14 studied the material removal rate and surface roughness of the WC-Co composite material subjected to wire electric discharge machining. Using desirability analysis, the surface roughness was improved from 2.52 µm to 1.90 µm and MRR was increased from mm 3 per min to mm 3 per min. Jangra et al. 15 optimized the multi
3 KUMAR & SINGH: EDM FOR MATERIAL REMOVAL RATE 225 machining characteristics in wire electrical discharge machining of WC-5.3%Co composite. They used the Taguchi s design of experiment. The four characteristics optimized simultaneously were material removal rate, surface roughness, angular error and radial overcut. Chalisgaonkar and Kumar 16 optimized the material removal rate, surface roughness and wire weight consumption simultaneously using grey-fuzzy logic and dimensional analysis. The reported literature reflects a complete dearth of research on optimization of process parameters while machining Inconel X-750 on WEDM. The current article is thus targeted to analyse the effect of input factors of WEDM on MRR while machining Inconel X-750. The selected material has numerous applications in advanced industries. Materials and Methods The material used in this study was Inconel X750 which is a nickel based alloy. The chemical composition of the work material is shown in Table 1. The work material in the form of plate with dimensions 150*100*23 mm has been taken for the experimental work. The experiments were performed on the four axis CNC type (Electronica Sprintcut) WEDM machine installed at Advanced Manufacturing Lab, NIT Kurukshetra, India. The experimental setup is shown in Fig. 2. Based on the literature review and a pilot study, six input parameters--spark gap voltage (SV), pulse-on time (T ON ), pulse-off time (T OFF ), wire feed rate (WF), peak current (IP) and wire tension (WT)-- were varied to see their effect on the output variable MRR. Table 2 represents the factors and their levels. The levels of the input parameters were decided on the basis of pilot experiments using one factor at-a-time approach. The degree of freedom (DOF) is the number of independent components which are free to vary. For a parameter, it is calculated as the number of levels minus one. There are six input parameters in this study each with 3 levels. So each parameter has 2 DOF making total DOF 12. Now we have to select an orthogonal array which is a 3 level design and has DOF more than 12. So, Taguchi s L 27 orthogonal array with six input variables and 3 interactions (Table 3) was selected for final experimentation. The specimens were prepared in cylindrical shape of base diameter 5 mm. The deionized water was used as the dielectric fluid. Three trials were conducted for each experiment planned and their mean was taken as the final reading for MRR. The material removal rate was calculated by Eq. (1). MRR= CS*H mm 2 /min (1) where CS is the cutting speed (mm/min) and H is the height of work-piece in mm. Design of Experiments by Taguchi Method The statistical design of experiments (DOE) is an efficient procedure for planning experiments so that the data obtained can be analyzed to yield valid and objective conclusions. DOE begins with determining the objectives of an experiment and selecting the process factors for the study. Taguchi s L 27 orthogonal array was used for this study. Table 4 Fig. 2 Experimental setup Table 2 Input parameters and their levels Symbols Factors Levels Unit I II III A T on µs B T off µs C SV Volt D IP Ampere E WF m/min F WT Gram Table 1 Chemical composition of Inconel X750 (wt%) Ni Cr Fe Nb Co Mn Cu Al Ti Si C S
4 226 INDIAN J. ENG. MATER. SCI., AUGUST 2016 Table 3 Taguchi s L 27 orthogonal array Run A B A*B A*B C A*C A*C B*C D E B*C F Table 4 Experimental readings of MRR Run Trial1 Trial2 Trial3 S/N Ratio Mean
5 KUMAR & SINGH: EDM FOR MATERIAL REMOVAL RATE 227 shows the experimental results of the MRR for different settings of parameters. Three trials were conducted for each setting in order to minimize the chances of error. MINITAB-17 software was used for the design and analysis purpose in this study. Results and Discussion The cutting speed increases with the increase in pulse on time and peak current and vice versa. This is because when we increase the pulse on time and peak current, the number of electrons striking the work surface increases which results in erosion of more material from the work-surface. Similarly, CS decreases with the decreased value of SV and T OFF. In this work, six input parameters (T ON, T OFF, IP, SV, WF, WT) and three interactions (T ON *T OFF, T OFF *SV, T ON *SV) have been studied. Signal to noise ratio was obtained by using MINITAB-17. The selected characteristic, MRR, is of the type Higher the Better. The S/N ratio is calculated by the logarithmic transformation of loss function 17 as in Eq. (2) and values are reported in Table 4 along with the raw data. S/N ratio= -10 log 10 (2) The Taguchi approach for predicting the mean performance characteristic and determination of confidence intervals for the predicted mean has been applied. Figure 3 represents the main effects plot of data means on MRR and Fig. 4 shows the interaction effect of means. Figures 5 and 6 show the main effects plot and interaction plot for S/N ratios Fig. 3 Main effects plot of data means for MRR Fig. 4 Interaction plot of data means for MRR
6 228 INDIAN J. ENG. MATER. SCI., AUGUST 2016 Fig. 5 Main effects plot of S/N ratios for MRR Fig. 6 Interaction plot of S/N ratios for MRR respectively. The ANOVA was carried out in MINITAB-17 software. Table 5 represents the analysis of variance results for mean MRR and Table 6 represents the ANOVA results for S/N ratios. For 95% confidence interval, an input factor should have a P value less than 0.05 in order to have significant effect on the output. From ANOVA results it can be observed that the pulse on time (T ON ), pulse off time (T OFF ), spark gap voltage (SV) and peak current (IP) have P value less than 0.05 and hence have significant effect on material removal rate whereas wire feed (WF) and wire tension (WT) have no effect on MRR. Therefore WF and WT can be neglected in future study. Interaction simply means that the effect of a factor depends upon the level(s) of the other factor(s). The presence of interaction, particularly extreme interaction, can easily result in completely erroneous conclusions being drawn if an experimenter is not careful. From Figs 4 and 6, it can be seen that no line is crossing other and also it can be observed from Tables 5 and 6 that the three interactions considered in this study have P value more than 0.05 and hence do not show any significant effect on MRR and can thus be dispensed with in future work. The optimized condition for MRR (Figs 3 and 5) is A3, B1, C1 and D2. Table 7 gives the optimization results. The predicted optimum value of MRR is calculated as 17 µmrr= (µa3+ µb1+ µc1+ µd2) -3µ (3) The overall mean of the population is: µ= mm 2 /min.
7 KUMAR & SINGH: EDM FOR MATERIAL REMOVAL RATE 229 Table 7 Optimization results for MRR Method Characteristic Optimal setting Predicted optimal value Taguchi s technique Response (units) MRR (mm 2 /min) Material removal rate A3B1C1D mm 2 /min Table 8 Confirmatory Experimental results Predicted value CI CE < µmrr < Experimental value By putting values, the predicted value of MRR is: µmrr= mm 2 /min For calculation of confidence interval, Eq. (4) has been used 17. CI CE = 1,. + (4) Here f e (error degree of freedom)= 2 F 0.05 (1,2)= (tabulated value at 95% confidence level) V e (error variance)= 4.47 = 1+ Table 5 Analysis of variance for means Source DF Seq SS Adj SS Adj MS F P A B C D E F A*B A*C B*C Residual error Total S = R-Sq = 99.8% R-Sq(adj) = 97.5% Table 6 Analysis of Variance for S/N ratios Source DF Seq SS Adj SS Adj MS F P A B C D E F A*B A*C B*C Residual error Total S = R-Sq = 99.8% R-Sq(adj) = 96.8% N= total number of experiments = 81 Hence, n eff = 81/(1+8) = 9 R = sample size = 3 Putting all values in Eq. (4) CI CE = The 95% confidence interval for µmrr is < µmrr < The confirmation experiments were conducted three times at the optimized settings of parameters. Table 8 shows the confirmatory experimental results. The mean value of MRR has been found to be within confidence intervals. Conclusions The following conclusions can be drawn from this study: (i) WEDM is an adequate process to machine Inconel X750 with a good material removal rate. (ii) The pulse on time (T ON ), pulse off time (T OFF ), spark gap voltage (SV) and peak current (IP) are found to be significant factors in both the ANOVAs, hence affecting mean and variance of MRR at 95% confidence level.
8 230 INDIAN J. ENG. MATER. SCI., AUGUST 2016 (iii) During WEDM of Inconel X750, wire feed (WF) and wire tension (WT) are found to be insignificant to the response MRR. (iv) Analysis of results leads to conclusion that the factors at levels A3, B1, C1, and D2 can be set for the maximization of MRR. (v) The optimal predicted value comes out to be mm 2 /min. and the confidence interval is < µmrr < (vi) The confirmation experiments are performed at the optimal levels of parameters. Three set of experiments are performed and their average is taken. The average comes out to be mm 2 /min. and it is well contained within the confidence intervals. Hence the predicted optimal setting may be implemented. References 1 Hassan El-Hofy, Advanced Machining Processes, Miller S F, Shih A J & Qu J, Int J Mach Tools Manuf, 44(4) (2003) Singh H & Garg R, J Achieve Mater Manuf Eng, 32(1) (2009) Hascalyk A & Caydas U, J Mater Process Technol, 148 (3) (2004) Hewidy M S, El-Taweel T A & El-Safty M F, J Mater Process Technol, 169 (2) (2005) Kanlayasiri K & Boonmung S, J Mater Process Technol, (2007) Goswami A & Kumar J, Int J Eng Sci Technol, 17 (2014) Kuriakose S & Shunmugam M S, J Mater Process Technol, 170 (1 2) (2005), pg Khanna R & Singh H, Int J Mach Mater, 14 (1) (2013) Puri A B & Bhattacharyya B, Int J Mach Tools Manuf, 43 (2) (2003) Goswami A & Kumar J, Int J Eng Sci Technol, 17 (2014) Khanna R & Singh H, Parametric optimization for material removal rate in Wire Electrical Discharge Machining, Jalandhar, India, Liao Y S, Huang J T & Su H C, J Mater Process Technol, 71 (3) (1997) Muthuraman V & Ramakrishnan R, Procedia Eng, 38 (2012) Jangra K, Grover S & Aggarwal A, Front Mech Eng, 7(3) (2012) Chalisgaonkar R & Kumar J, Cogent Eng, 1 (2014) Ross P J, Taguchi Techniques for Quality Engineering, 2 nd ed, (Tata McGraw-Hill Publishing Company Limited, New Delhi), 2005.
research work. In Secondly, the result, the effects
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