Optimization of MRR and Overcut in Die Sinking Electro- Discharge Machining of EN45 Material Using Grey-Taguchi Technique

Optimization of MRR and Overcut in Die Sinking Electro- Discharge Machining of EN45 Material Using Grey-Taguchi Technique Sumit Raj and 2 Dr. Kaushik Kumar* Reaserch scholar - Department of Mechanical Engineering, Birla Institute of technology, Ranchi, Jharkhand, 2 Associate Professor Department of Mechanical Engineering,Birla Institute of technology, Ranchi, Jharkhand, Abstract The main focus of this paper is to investigate the effect of process parameters on material removing rate (MRR) and overcut (OC) in die sin king electro discharge machining (EDM) in EN 45 steel tool using Grey Taguchi optimization method. Experiments are conducted based on Taguchi's L27 orthogonal array (OA) with four process parameters viz. Peak current (Ip), Pulse on time (Ton), Pulse off time (Toff) and voltage (v). In this paper tried to find out the best optimal combination of input parameters for the maximum value of MRR and minimum value of overcut. Analysis of variance (ANOVA) is performed to get the contribution of each parameter and contribution of their interaction on the performance characteristics and it was observed that Peak current is the significant input parameter that affects the responses MRR and OC. A confirmation test was also carried out that concluded that there was a considerable improvement in this process. Keywords: Electro-Discharge Machining, EN 45 steel tool, Material Removal Rate, Over Cut, Grey- Taguchi, ANOVA. Nomenclature Used: MRR Material Removing Rate A Peak current ( Ip) B Pulse on time (Ton) C Pulse off time (Toff) D Voltage (V) EDM Electrical discharge machining ANOVA Analysis of variance μs Micro second S/N ratio Signal-to-noise ratio DOF Degree of freedom SS Sum of squares MS Mean square F Variance ratio % P Percentage contribution.introduction Electrical discharge machine (EDM) is among the wellrecognized non-traditional manufacturing processes in industry. EDM is very useful and demanding machining process for manufacturing geometrically complex and hard material parts that can t be machine by conventional machining processes. The principle of EDM process is to convert electrical energy into thermal energy. For machining electrically conductive parts regardless of its hardness EDM is one of the most prominent non-conventional machining process. It is mainly used in the manufacture of mould, die, automotive, aerospace and surgical components etc. In EDM work piece is cut without contact between tool and work piece and without mechanical force between them. Due to this there is no problem of development of mechanical stresses, chatter and vibration, as is prominent in traditional. In EDM melting and vaporization of the work material due to heat generation by sparks dominates the material removal process, leaving tiny craters on the surface of the work material. Main reason of producing overcut in EDM is due to uncontrolled side sparks during process and finishing of tool surfaces. The frontal sparks produces depth of blind hole. During EDM from both work piece and tool an appropriate amount of material is diffused depending upon polarity of work piece and tool. Positive charged materials have more material erosion because electron strikes at higher rate to negative charged material due to this more heat is produced at this. 2.Literature Review A number of experimental works has been carried out till date for the investigation of the process parameters in EDM. Raghuraman et al [] tried to optimize the EDM parameters using Taguchi method and grey relational analysis for mild steel IS 2026. They concluded that current and pulse on time are major factor. Vikas et al [2] studied the effect and optimization of various machine process parameters on the surface roughness in EDM for EN 4 material using Grey- Taguchi. They concluded that discharge current had a larger impact over the surface roughness parameters and effect of other parameters was less. ShaileshDewangan et al [3] investigated the optimization of the surface integrity characteristics of EDM process using PCA based Grey Relation investigation. They took four variables discharge current, pulse on time, duty factor and polarity. They found that discharge current was more significant factor then other. Bijo Mathew et al [4] tried to optimize multiple process parameter optimization of WEDM on AISI304 using Taguchi Grey Relational analysis. They took six variables pulse on time, pulse off time, servo voltage, feed rate, dielectric pressure and wire tension. They found that for MRR, surface roughness and dimension deviation, pulse on time was more significant factor. MohitTiwari et al [5] investigated the 3652

optimal combination of process parameters for EDM by using a Grey Relational analysis. They used four parameters pulse on time, peak current, gap voltage and duty cycle they observed that peak current contributed more significance toward MRR and tool wear rate. Zahid A. Khan et al [6] investigated multi response optimization of wire electrical discharge machining process parameters using Taguchi based Grey relational analysis. They concluded that Increase in the pulse ON time leads to the increase in both the surface roughness and the kerf width and increase in the pulse current leads to the increase in the surface roughness. MitaliS.Mhatre et al [7] examined Electro Discharge Machining characteristics of Ti-6Al-4V alloy using Grey Relational optimization method. They optimized against MRR, surface roughness and electrode wear rate. They observed that pulse current is the most significant factor for MRR, surface finish and electrode wear rate. PujariSrinivasaRao et al [8] tried to optimize of wire EDM parameters for MRR and surface roughness for aluminium alloy using Taguchi method. They found that the parameters pulse on time, current and gap voltage have more significant factor for MRR and surface roughness. Rajmohan. T. [9] et al conducted an experiment to optimize the machining parameters in EDM of 304 stainless steel by using Taguchi method. They found that current and pulse off time are the most significant parameters on MRR. A lot of work has been done by many researchers on EDM and optimization process by Grey Taguchi optimization method [0-5]. Grey relation analysis Taguchi technique (Taguchi 990) is a powerful tool for designing high quality system at minimum cost based on orthogonal array. Taguchi method is only suitable for single response optimization. But optimization of more than one performances i.e more than one output parameters characteristics is different from single performance characteristics. For optimization of multi- outputs Grey relation analysis coupled with Taguchi method of optimization. This Grey system theory has been first employed by Deng in 989. For calculation of Grey relation analysis some steps are done. At first calculation of Grey relational generation is done in which the set of experimental results are normalizes in between 0 and. After that obtain the Grey relational coefficient from the normalized data for represent the correlation between the desired and actual experimental data. Obtain the value of the Grey relation grade by averaging the input parameters with the Grey relation coefficient. Now Grey relational grade to be maximized, the S/N ratio is calculated for higher the better criteria. With Grey relational grade perform the statistical analysis of variance (ANOVA) for the input parameters and to find which parameters significantly effects on the process performance. From ANOVA table select the optimal levels of process parameters. And at last conformation test to be done verify optimal process parameters setting. 3.. Result Discussion Design of Experiment In this study four controllable input parameters Peak current (Ip), Pulse on time (Ton), Pulse off time (Toff) and Voltage (V) is considered at three levels as shown in Table. In this investigation total 27 experiments has been conducted by using an L27 array which is generated by using Taguchi method of optimization. Each experiment is done at different combination of input parameters. Table. Machining parameters and their levels. EDM Coding Machining Level Level2 Level3 Parameters A Peak current (Ip) 8 6 24 B Pulse on time (Ton) 200 300 400 C Pulse off time (Toff) 2300 2200 200 D Voltage (V) 40 60 80 Selection of work piece, tool material and dielectric medium Cylindrical block of diameter 25mm and thickness of 0mm of EN 45 steel tool material is used as a work piece for this experiment. EN 45teel tool is high carbon content material and it is widely used in the motor vehicle components like leaf spring, truncated conical spring and spring leaf etc. Tool is made of copper with cylindrical shape is selected which is 99.99% pure, due to its good response for thermal and electrical conductivity and good response for material removing it is selected as tool. Paraffin oil was used as dielectric medium and also for flushing purpose. Dielectric is also work as coolent. Flushing is done for removing the melt material between electrode and work piece if it will not done in proper than there may be chances of short circuit. Experimental procedure The experiments are conducted on the CNC EDM machine (EMT 43- Electronica machine Tools). EDM setup consist machining chamber, control unit and dielectric circulation system. The chemical composition of work piece and tool material was obtained using Energy Dispersive X-Ray Spectroscopy (EDX)(Specification: Resolution 3mm at 30 KV).The work piece is fixed on work table. Work piece and tool were polarized opposite in nature. Work piece and tool were positively and negatively charged respectively. Before experiment weight of each work piece was taken by digital weighting machine. A hole of mm depth produced in each work piece by EDM process. Flushing with constant pressure value (0.6kg/cm2) is done during experiment. For maintaining the sharp edge of tool and getting the sharp edge cavity on the work piece hole, after every experiment tool was grinding with fine emery paper. Initial and final time of each experiment were recorded. After experiment each work piece were washed and dried. Again final weight of each work piece were taken, weight taken on same electric weighting machine for maintaining the accuracy. Diameter of hole produced in each work piece was measured using a Verniercaliper with least count of 0.02mm. 3653

Material removing rate is calculated using equation : () Normalization formula using for lower the better criteria for Overcut (4) Over Cut is evaluated using equation 2: Experimental value of MRR and OC are in Table 2. Table 2. Experimental results for output parameters Sample MRR (A) (B) (C) (D) markin (gm/min Ip(A) Ton(µs) Toff(µs) V(V) g ) (2) Over Cut 8 200 8 40 0.263 0.08 2 8 200 2 60 0.385 0.0 3 8 200 6 80 0.423 0.2 4 8 300 8 60 0.026 0.4 5 8 300 2 80 0. 0.0 6 8 300 6 40 0.97 0. 7 8 400 8 80 0.0799 0.2 8 8 400 2 40 0.400 0.09 9 8 400 6 60 0.444 0.5 0 6 200 8 60 0.2722 0.2 6 200 2 80 0.304 0.7 2 6 200 6 40 0.4633 0.9 3 6 300 8 80 0.3685 0.9 4 6 300 2 40 0.439 0.23 5 6 300 6 60 0.3567 0. 6 6 400 8 40 0.307 0.22 7 6 400 2 60 0.380 0.22 8 6 400 6 80 0.3694 0.9 9 24 200 8 80 0.36 0.6 20 24 200 2 40 0.5667 0.20 2 24 200 6 60 0.5083 0.2 22 24 300 8 40 0.4250 0.24 23 24 300 2 60 0.5708 0.23 24 24 300 6 80 0.5625 0.2 25 24 400 8 60 0.4083 0.26 26 24 400 2 80 0.5250 0.25 27 24 400 6 40 0.667 0.27 Grey relational generation In Grey relational analysis first step is to perform the Grey relational generation in this stage the result of the experiments are is normalized in the range between 0 and. For normalized the MRR data higher the better (HB) criteria and for Overcut lower the better (LB) criteria is selected, because here MRR is to be maximized and Overcut is to be minimized. Normalization formula using for higher the better criteria for MRR (3) Here X_(i ) (k) represent the value after Grey relational generation, min y_i (k) stand for the smallest value of y_i (k) for the kth response, and max y_i (k) stand for the largest value of y_i (k) for the kth response. Where k=,2,3..27. The processed data after grey relational generation is shown in table (3).The value of larger normalized result correspond to the better performance and the best normalized result obtained. Grey relational coefficient: Grey relational coefficients are expressed the relationship between the ideal and actual experiment results for parameters. Grey relational coefficient calculated by the equation 5: Where _oi is the difference of absolute value between X0(k) and Xi (k). And _min and _max are stand for minimum and maximum values of the absolute differences ( _oi) of all comparing sequences. Here ψ is a distinguishing coefficient, its value lie between 0 and including both values, 0 ψ. The purpose of this is to weaken the effect of _max. The suggested value of ψ is took 0.5 for moderate distinguishing effect and good stability of outcomes. The value of Grey relation coefficient of each out-put parameters are shown in Table 3. Grey relational grade and grey relational ordering: The Grey relational grade is converted the multiple response of process like MRR and Overcut into a single response. The Grey relational grade is calculated by the following formula by the using grey relational coefficient as follows: (6) Where n is the number of process responses. Higher the value of Grey relational grade means stronger relational degree between the ideal sequence x0(k) and the given sequence xi(k). Grey relational grade for experimental result are calculated which are shown in Table 3. Here the multi response optimization problem is converted into single response optimization problem. Analysis of Signal-to-Noise (S/N) ratio: As Grey relational grade is maximized, the S/N ratio for overall Grey relational grade is calculated using higher the better (HB) criteria using Minitab 6 software by this formula 0log n n y i 2 i (5) 3654

Where y is number of observed data and n is the number of observation. The result may be expressed in either S/N ratio or the mean. Now the response table for the mean of Grey relational grade is obtained using Minitab 6 software where the rank of each of the input parameters is signified as shown in Table 4. The delta statistic is the value of highest average of each factor minus the lowest average of the same. Rank of input parameters are assigned on the basis of their delta value. First rank value are most significant parameter towards output parameters. Table3. Normalized of experimental data, Grey relational coefficient and Grade. Normalized data Grey relation coefficient Exp. Grade MRR Overcut MRR Overcut No. 0.08643.00000 0.3537.00000 0.67685 2 0.096 0.89472 0.35949 0.82608 0.59279 3 0.624 0.78947 0.3633 0.70370 0.5325 4 0.04228 0.6842 0.34300 0.6290 0.47795 5 0.0582 0.89473 0.34677 0.82608 0.58642 6 0.20827 0.8420 0.38707 0.76000 0.57353 7 0.00000 0.78947 0.33333 0.70370 0.585 8 0.95 0.94736 0.36022 0.90476 0.63249 9 0.205 0.6357 0.36235 0.57575 0.46905 0 0.35823 0.78947 0.4379 0.70370 0.5708 0.42939 0.5263 0.46702 0.535 0.49026 2 0.7423 0.4205 0.63632 0.4634 0.54986 3 0.53763 0.4205 0.5955 0.4634 0.4948 4 0.6222 0.2053 0.5696 0.38776 0.47868 5 0.5565 0.842 0.50795 0.76000 0.63397 6 0.42325 0.2636 0.46436 0.40426 0.4343 7 0.56092 0.2636 0.53243 0.40426 0.46834 8 0.5393 0.4205 0.52046 0.4634 0.4994 9 0.52385 0.57895 0.522 0.54286 0.52754 20 0.90686 0.36842 0.84296 0.4486 0.6424 2 0.79806 0.3579 0.723 0.42222 0.56727 22 0.64288 0.5789 0.58335 0.37255 0.47795 23 0.9449 0.2053 0.85396 0.38776 0.62086 24 0.89903 0.3579 0.8399 0.42222 0.627 25 0.677 0.05263 0.56292 0.34545 0.4549 26 0.8297 0.0526 0.74535 0.35849 0.5592 27.00000 0.00000.00000 0.33333 0.66667 Table 4. Response table for Grey relational grade Level A B C D 0.5622 0.5723 0.544 0.5703 2 0.522 0.5520 0.5627 0.5395 3 0.5707 0.5208 0.5680 0.5353 Delta 0.0585 0.054 0.0536 0.0350 Rank 3 2 4 parameters is near horizontal, then the parameters has no significant effects. On the other hand, a parameter for which the line has the highest inclination will have the most significant effect. From the main effect plot it is very clear that parameters (Peak current)a is the most significant parameters. ANOVA Result ANOVA (analysis of variance) is very useful for analyzing the level of significance of influence of factors and their interaction on a particular response. In the present study, ANOVA is performed using Minitab 6 software. Table 5 shows the ANOVA result for overall Grey relational grade of MRR and Overcut parameters. ANOVA calculations are based on F-ratio, it is the ratio between the regression mean square and the mean square error. The F ratio is also the ratio of variance due to the effect of a factor and variance due to the error term so it s also called variation ratio. In general, due to increases in the F-value the significance of the parameter also increases. ANOVA table also shows the percentage contribution of each parameter and also percentage contribution of their interaction. It is clear from ANOVA table that parameter A (Peak current) has got the most significant influence on MRR and Overcut. Table 5. Result for ANOVA for Grey relational grade Source DF Seq SS Adj SS Adj MS F %P A 2 0.080 0.080 0.0090 4.4 3.74 B 2 0.02 0.02 0.0060 2.96 9.23 C 2 0.057 0.057 0.0078 3.85.99 D 2 0.0066 0.0066 0.0033.6 5.03 A*B 4 0.0055 0.0055 0.004 0.68 4.2 A*C 4 0.0362 0.0362 0.0090 4.44 8.80 B*C 4 0.0246 0.0246 0.0062 3.02 0. Error 6 0.022 0.022 0.0020 Total 26 0.309 4. Conformation test After evaluating the optimal level of process parameters a conformation test was carried out in order to ensure the accuracy of the analysis. The comparison of the predicted S/N ratio and experimental S/N ratio for combination of optimal parameters and the S/N ratio of the mid-level combination of process parameters are shown in Table 6. From this test it was found the improvement in the S/N ratio from mean to the optimal process parameters was about.85 db. Based on the above results, it may be clearly observe that quality characteristics are greatly improved through this study. Fig.: Main effect plot for mean of Grey relational grade. Figure shows the main effect plots for the process parameters. In the main effects plot, if the line for a particular Table 6. Conformation test for Grey relation grade. Parameter Mean parameter for EN 45 A2B2C2D2 Optimal parameter for EN 45 A3BC3D Mean Predicted Experimental Grade 0.48840 0.602890 S/N ratio of grade -6.24898-4.32360-4.39524 Improvement in S/N ratio =.85 db 5. Conclusion In the present study the optimization of the process parameters is carried out in EDM of EN 45 steel tool for maximum material removing rate (MRR) and minimum Overcut (OC) 3655

value. There were four variable parameters Peak current, Pulse ON time, Pulse off time and Voltage at three levels. Grey relational analysis is successfully employed in conjunction with Taguchi design of experiment to convert multi response to a single response. The optimal parameter combination is obtained as A3BC3D i.e highest level of peak current, lowest level of pulse on time, highest level of pulse off time and highest level of voltage. The improvement in S/N ratio was.85 db, thus Grey based Taguchi method was best for optimize the process parameters. A conformation test is carried out to validate the analysis. ANOVA result show that Peak current was the maximum influenced on the metal removal rate and overcut characteristics. References [] Raghuraman, Thiruppathi, Panneerselvam, Santosh. Optimization of EDM parameters using Taguchi method and Grey relational analysis for mild steel IS 2026. International Journal of Innovative Research in Science, Engineering and Technology.Vol.2 pp3095-304. [2] Vikas, Apurba Kumar Roy, Kaushik Kumar. Effect and optimization of various machine process parameters on the surface roughness in EDM for an EN4 material using Grey-Taguchi. Procedia Materials Science 6 ( 204 ).pp 383 390. [3] ShaileshDewangana, Chandan Kumar Biswasb and SoumyaGangopadhyayc. Optimization of the surface integrity characteristics of EDM process using PCA based Grey relation investigation. 3rd International Conference on Materials Processing and Characterisation (ICMPC 204). pp 09-096. [4] Bijo Mathew, Benkim, J. Babu. Multiple process parameter optimization of WEDM on AISI304 using Taguchi Grey Relational analysis. International Conference on Advances in Manufacturing and Materials Engineering 204. Volume 5 pp 63-622. [5] MohitTiwari, KuwarMausam, Kamal Sharma, RavindraPratap Singh. Investigate the Optimal combination of process parameters for EDM by using a Grey Relational analysis.international Conference on Advances in Manufacturing and Materials Engineering 204, volume 5, pp 736 744. [6] Zahid A. Khan, Arshad N. Siddiquee, Noor Zaman Khan, Urfi Khan, G.A.Quadir. Multi response optimization of wire electrical discharge machining process parameters using Taguchi based Grey relational analysis. 3rd International Conference on Materials Processing and Characterisation (ICMPC 204) volume 6, pp 683 695. [7] MitaliS.Mhatre, SagarU.Sapkal, RajuS.Pawad.Electro Discharge Machining characteristics of Ti-6Al-4V alloy: A Grey Relational optimization. International Conference on Advances in Manufacturing and Materials Engineering, ICAMME 204. pp 204 2022. [8] PujariSrinivasaRao,KoonaRamji, BeelaSatyanarayana. Experimental investigation and optimization for wire EDM parameters for surface roughness, MRR white layer in machining of Aluminium alloy. Procedia Materials Science 5 ( 204 ) pp 297 2206. [9] Rajmohan.T, Prabhu.R, SubbaRao G, Palanikumar K. Optimiation of machining parameters in Electrical Discharge machining (EDM) of 304 stainless steel. SciVerse Science Direct Procedia Engineering 38 ( 202 ) pp 030 036. [0] H.Ramasawmy, L.Blunt.Effect of EDM process parameters on 3D surface topography.journal of Materials Processing Technology 48 (2004) pp 55 64. [] Pravin R. Kubade, V. S. Jadhav. An experimental investigation of Electrode Wear Rate (EWR), Material Removal Rate (MRR) and Radial Overcut (ROC) in EDM of high Carbon-high Chromium Steel (AISI D3) International Journal of Engineering and Advanced Technology (IJEAT). Volume-, Issue-5, June 202. ISSN: pp2249 8958. [2] Milan Kumar Das, Kaushik Kumar, Tapan Kr. Barmanaan, PrasantSahoo. Application of Artificial bee Colony algorithm for optimization of MRR and surface roughness in EDM of EN3 tool steel. Procedia Materials Science 6 ( 204 ) pp 74 75. [3] A. M. Nikalje& A. Kumar & K.V. SaiSrinadh. Influence of parameters and optimization of EDM performance measures on MDN 300 steel using Taguchimethod. Int J AdvManufTechnol (203) 69:. DOI 0.007/s0070-03-5008-8pp 4 49. [4] E. Aliakbari& H. Baseri. Optimization of machining parameters in rotary EDM process by using the Taguchi method. Int J AdvManufTechnol (202) 62: DOI 0.007/s0070-0-3862-9. pp 04 053. [5] S. Arvind Krishnan & G.L Samuel. Multi- objective optimization of material removing rate and surface roughness in wire electrical discharge turning. Int J AdvManufTechnol (203) 67: pp 202-2032, DOI 0.007/s0070-02-4628-8.. 3656