Parametric Optimization of Wire Electrical Discharge Machining by Taguchi Technique on Composite Material

Parametric Optimization of Wire Electrical Discharge Machining by Taguchi Technique on Composite Material

Kishore. G. C 1*

1 Post Graduate Student, Dept. of Mechanical Engineering,

Dayananda Sagar College of engineering, Bangalore-560078, Karnataka, INDIA.

Aruna Devi. M 2

2 Assistant Professor,

Dept. of Mechanical Engineering, Dayananda Sagar College of engineering, Bangalore-560078, Karnataka, INDIA.

1. P. S Prakash 3

   3 Principal,

   Dayananda Sagar College of engineering, Bangalore-560078, Karnataka, INDIA.

   Abstract In this present study, Al7075+10%Al2O3 metal matrix composite (MMC) were fabricated using stir casting method. The five parameter namely voltage, pulse-on, pulse-off, current, bed speed were chosen as factors to study the output responses in terms of material removal rate (MRR) and surface roughness (Ra) while machining Al7075+10%Al2O3 metal matrix composite (MMC) in wire Electrical Discharge Machining (WEDM). Experimentation has been carried out using Taguchis L18 orthogonal array. Evaluation of output responses has been done by Signal to Noise (S/N) ratio analysis and to determine the significant effect of each parameter Analysis of Variance (ANOVA) was carried out. Optimal value of parameters which maximize material removal rate (MRR) and minimize surface roughness (Ra) were determined based on experimental result, In addition mathematical model have developed for output responses.

   KeywordsWEDM, MRR, Ra, Taguchi orthogonal array, minitab-17 software.

   EDM material removal process were wire is used as cutting tool as shown in Fig-1. The discrete sparking between work piece and wire, erodes the material from the work piece in the presence of dielectric fluid which is flushed continuously to working zone and also flush the eroded particles and acts as coolant. Taguchi orthogonal array is used to conduct the experiment with less number of experiment and get better result. The experimental result is transformed to S/N ratio and ANOVA are used to determine optimum value and relative contribution of each factor on output responses.

   1. INTRODUCTION

      A composite is a material which is comprised of two or more materials. The main part of the composite is called as matrix material and the material mixed is called as reinforcement. The composite are mainly classified in to three groups Polymer matrix composites (PMCs), Metal matrix composites (MMCs), Ceramic matrix composites (CMCs). To produce Polymer matrix composites (PMCs), Metal matrix composites (MMCs), Ceramic matrix composites (CMCs) reinforcement are added. The advantage of using composite are light weight, high strength, corrosion resistance, high impact strength, design flexibility, dimension stability and so on. Aluminium matrix composite (AMC) exhibit outstanding combination properties such as high strength, high stiffness, thermal and electric conductivities from these magnificent property AMCs are used in aerospace, automobile, defense and in many other sectors. These composite can be fabricated by using stir casting method. In which reinforcement material can be mixed with molten metal by mechanical stirring process. This method can reduce the final cost and produce large quantity. Wire electrical discharge machining (WEDM) is a thermo-electrical process which is classified under non- traditional machining process. WEDM is effectively and successfully used to machine MMC. WEDM is similar to

      Fig- 1 Wire electric discharge machining (WEDM) model

   2. EXPERIMENTAL STUDY

      1. Material Selection

         1. Matrix Material

            Aluminium Al7075 alloy was considered as the matrix material based on mechanical properties like machinability, fatigue strength, corrosion resistance are excellent compared to other Aluminium alloy.

            Table – I Chemical composition of Al7075

            Contents	Zn	Mg	Cu	Cr	Fe	Si	Mn	Ti	Al
            Composition (%)	6	3	2	0.3	0.6	0.5	0.4	0.3	Bal

         2. Reinforcement

         Al2O3 (Alumina) of size 50 – 100 microns was used as reinforcing particles with the proportions of 90% and 10%. In engineering ceramic family Alumina is widely used and it is cost effective material which have excellent combination of properties. With the fine grain alumina has wide range of application.

         selection of process parameter were based on machine capability. The parameters like Voltage, pulse-on, pulse-off, current, bed speed were chosen to carry out the experiments

         Table- III Machining parameter used in experiments

         parameters		Level
         		I	II	III	Units
         A	Voltage	75	100	—-	Volts
         B	Pulse-ON	40	30	20	sec
         C	Pulse-OFF	9	12	15	sec
         D	Current	2	4	6	Amps
         E	Bed speed	50	150	250	m/sec

         Table – II Chemical composition of AL2O3

         Contents	SiO2	Fe2O3	TiO2	Na2O	AL2O3
         Composition (%)	0.15	0.05	0.15	0.45	Bal

      2. Composite Preparation

         The present study was conducted by taking Aluminium Al7075 as a base matrix and Al2O3 of size 50 – 100 microns was used as reinforcing particles with the proportions of 90% and 10% wt. respectively. Aluminium Al7075 alloys were melted using 6kw melting furnace (silicon element heating) at a temperature of 7400c for 30 min. Then the mixture was stirred using a ceramic coated metallic stirrer rotating at 300 rpm for 15 minutes and then it was poured in to a metallic mould which was preheated.

         1) Metallographic Study

         Fig- 2 Microstructure

         To investigate the distribution of discontinuous reinforcement matrix in the fabricated specimen, the quality of specimen was checked by metallographic study using optical microscope connected to computer imaging system and scanning electron microscope. Microstructure of composite specimen was observed at 200X. From the Fig-2 uniformly distributed reinforcement was revealed.

      3. Wire Electrical Discharge Machining (WEDM)

         The experiments was conducted by using DK-7732 WEDM machine tool which is manufactured by CONCORD United Products Ltd. Based on the thickness, material the operator can select the input parameter from the manual provided by the manufacturing company. Molybdenum wire of Diameter 0.18mm was used and demineralized water plus JR3A gel is used as a dielectric fluid to carry out experiments. The

         Material removal rate (MRR) can be calculated using

         Eq. (1).

         MRR= (2*Wg+D) * t * L Eq. (1)

         T

         Where,

         Wg = spark gap – 0.02mm

         D = Diameter of ire – 0.18mm T = Time taken to cut min

         L = Distance travelled by tool 60mm t = Thickness of work piece 10mm

         Surface roughness was measured using Mitutoyo Surftest SJ- 210 portable surface measuring unit.

      4. Experimental Design

      Dr.Genichi Taguchi developed Taguchi method which was built on traditional concepts of Design of Experiment (DOE).

      R.A. Fisher introduced the DOE technique to study the multiple variables simultaneously. Orthogonal array (OA) is a specially constructed table based on DOE technique to reduce the number of experiments. The L18 (2*3) orthogonal array was chosen to conduct the experiment as shown in Table- IV

      Table- IIV L18 (2*3) orthogonal array

      L18 (2*3) Orthogonal array
      Exp	Voltage	Pulse ON	Pulse OFF	Current	Bed speed
      No.	volts	(µs)	(µs)	Amps	(µm/s)
      1	75	40	9	2	50
      2	75	40	12	4	150
      3	75	40	15	6	250
      4	75	30	9	2	150
      5	75	30	12	4	250
      6	75	30	15	6	50

      12	17.483	24.853	1.595	-4.055
      13	28.884	29.213	1.759	-4.905
      14	5.880	15.387	1.617	-4.174
      15	11.681	21.350	1.768	-4.950
      16	17.862	25.039	1.458	-3.275
      17	14.270	23.089	1.729	-4.756
      18	5.877	15.383	1.501	-3.528

      7	75	20	9	4	50
      8	75	20	12	6	150
      9	75	20	15	2	250
      10	100	40	9	6	250
      11	100	40	12	2	50
      12	100	40	15	4	150
      13	100	30	9	4	250
      14	100	30	12	6	50
      15	100	30	15	2	150
      16	100	20	9	6	150
      17	100	20	12	2	250
      18	100	20	15	4	50

      After conducting the experiments results were evaluated by using, S/N ratio and ANOVA to find the optimal value and relative parameter influence on output responses i.e. (MRR, Ra).

      The S/N ration is classified as Larger the better, Nominal the better and Smaller the better. The S/N ratio for MRR and Ra was calculated by logarithmic transformation function as shown in Eq. (2). Larger the better and Eq. (3) Smaller the better respectively S/N ratio and tabulate in Table- V

      MRR = -10 log ( (1/y2)/n) Eq. (2)

      RA = -10 log (y2/n) Eq. (3)

   3. RESULT AND DISCUSSION

      The analysis of experimental results were carried out by using minitab-17. The results were transformed to signal to noise ratio of MRR and Ra for Al7075+10%Al2O3 metal matrix composite (MMC).

      L18 (2*3) Orthogonal array
      Exp. No.	MRR	S/N ratio MRR	Ra	S/N ratio Ra
      	mm3/min	(dB)	(µm)	(dB)
      1	5.641	15.027	1.784	-5.028
      2	16.500	24.350	1.528	-3.682
      3	17.984	25.098	1.572	-3.929
      4	13.895	22.857	1.737	-4.796
      5	17.671	24.945	1.739	-4.806
      6	5.906	15.426	1.387	-2.842
      7	5.901	15.418	1.645	-4.323
      8	17.886	25.050	1.567	-3.901
      9	8.800	18.890	1.749	-4.856
      10	30.137	29.582	1.405	-2.954
      11	5.911	15.434	1.576	-3.951

      Table- V Eexperimental results

      1. Signal to Noise (S/N) ratio

         The S/N ratio graph for material removal rate (MRR) is shown in fig 3. from the graph the optimum value obtained are Voltage 100 volts, Pulse-ON 40(µs), Pulse-OFF 9(µs), Current 6 Amps, Bed speed 250 (µm/s). This optimum value gives maximum MRR. The S/N ratio graph for surface roughness (Ra) is shown in fig 4. from the graph the optimum value obtained are Voltage 100 volts, Pulse-ON 40 (µs), Pulse-OFF 15 (µs), Current 6 Amps, Bed speed 50 (µm/s). This optimum value gives minimize Ra.

         Fig. 3. Signal to noise ratio graph for material removal rate (MRR)

         Fig. 4. Signal to noise ratio graph for surface roughness (Ra)

         The Response Table for S/N ratio for Material Removal Rate (MRR) and surface roughness (Ra) is shown in Table-VI and Table-VII

         Table-VI Taguchi Analysis: MRR v/s Voltage, Pulse On, Pulse Off, Current, Bed Speed

         3.16

         Response Table For Signal To Noise Ratios Larger Is Better
         Level	Voltage	Pulse-On	Pulse-Off	Current	Bed Speed
         1	20.78	20.48	22.86	19.44	15.35
         2	22.15	21.53	21.38	22.36	23.92
         3	——-	22.39	20.17	22.60	25.14
         Delta	1.36	1.91	2.69	9.79
         Rank	5	4	3	2	1

         Table-VII Taguchi Analysis: Ra v/s Voltage, Pulse-No, Pulse-Off, Current, Bed Speed

         Response Table For Signal To Noise Ratios Smaller Is Better
         Level	Voltage	Pulse-On	Pulse-Off	Current	Bed Speed
         1	-4.240	-4.107	-4.214	-4.723	-3.974
         2	-4.061	-4.412	-4.212	-4.217	-4.110
         3	——	-3.933	-4.026	-3.512	-4.368
         Delta	0.180	0.479	0.187	1.210	0.393
         Rank	5	2	4	1	3

      2. Analysis Of Variance (ANOVA)

         The ANOVA results for Material Removal Rate (MRR) and surface roughness (Ra) are shown in Table VIII and IX and Fig 5 and 6 shows the Percentage contributions of Material Removal Rate (MRR) and surface roughness (Ra)

         Analysis of variance
         Source	DF	Adj SS	Adj MS	F-Value	P-Value
         Regression	5	860.68	172.14	16.01	0.000
         Voltage	1	42.95	42.95	3.99	0.069
         Pulse-On	1	44.31	44.31	4.12	0.065
         Pulse-Off	1	99.69	99.69	9.27	0.010
         Current	1	104.76	104.76	9.74	0.009
         Bed Speed	1	568.97	568.97	52.92	0.000
         Error	12	129.01	10.75	—–	——-
         Total	17	989.70	——–	—–	——-

         Table-VIII Regression Analysis: MRR v/s Voltage, Pulse On, Pulse Off, Current, Bed Speed

         Fig. 5 Percentage contributions by process parameters on MRR

         Table-IX Regression Analysis: Ra v/s Voltage, Pulse-No, Pulse-Off, Current, Bed Speed

         Analysis of variance
         Source	DF	Adj SS	Adj MS	F-Value	P-Value
         Regression	5	0.177183	0.035437	4.05	0.022
         Voltage	1	0.005000	0.005000	0.57	0.464
         Pulse-On	1	0.002977	0.002977	0.34	0.570
         Pulse-Off	1	0.003888	0.003888	0.44	0.517
         Current	1	0.148964	0.148964	17.04	0.001
         Bed Speed	1	0.016354	0.016354	1.87	0.196
         Error	12	0.104894	0.008741	—–	——-
         Total	17	0.28076	——–	—–	——-

         Fig 6 Percentage contributions by process parameters on Ra

      3. Mathematical Model

      Multiple linear regression (MLR) model is performed by the help of MINITAB-17 software. This model is used to predict various performance measures in WEDM process. The Equation (4) and (5) shows the mathematical model, Table X and XI shows the model summary. Experimental value and predicted value are compared as shown in figure 7 and figure 8 of Material Removal Rate (MRR) and surface roughness (Ra) respectively.

      Table – X Model summary for MRR

      Model summary
      S	R-sq		R-sq (adj)	R-sq (pred)
      3.27887	86.96%		81.53%	71.74%
      Coefficients
      Term	Coef	SE Coef	T-Value	P-Value	VIF
      Constant	-7.50	7.61	-0.99	0.344	—–
      Voltage	0.1236	0.0618	2.00	0.069	1.00
      Pulse-On	0.1922	0.0947	2.03	0.065	1.00
      Pulse-Off	-0.961	0.316	-3.05	0.010	1.00
      Current	1.477	0.473	3.12	0.009	1.00
      Bed Speed	0.06886	0.00947	7.27	0.000	1.00

      Regression Equation

      MRR = -7.50+ 0.1236 Voltage + 0.1922 Pulse-On –

      0.961 Pulse-Off + 1.477 Current

      + 0.06886 Bed Speed. Eq. (4)

      Fig. 7 comparison of MRR between experimental and predicted

      Table XI Model summary for Ra

      Model summary
      S	R-sq	R-sq (adj)	R-sq (pred)
      0.0934940	62.81%	47.32%	14.95%
      Coefficients
      Term	Coef	SE Coef	T-Value	P-Value	VIF
      Constant	2.021	0.217	9.32	0.000	—–
      Voltage	-0.00133	0.00176	-0.76	0.464	1.00
      Pulse-On	-0.00157	0.00270	-0.58	0.570	1.00
      Pulse-Off	-0.00600	0.00900	-0.67	0.517	1.00
      Current	-0.0557	0.0135	-4.13	0.001	1.00
      Bed Speed	0.000369	0.000270	1.37	0.196	1.00

      Regression Equation

      Ra =2.021 – 0.00133 Voltage – 0.00157 Pulse-On 0.00600Pulse-Off – 0.0557 Current +0.000369 Bed Speed.

      Eq. (5)

      Fig. 8 comparison of Ra between experimental and predicted

      Response	Factor
      	Voltage (volts)	Pulse- on (µs)	Pulse- off (µs)	Current Amps	Bed speed (µm/s)
      MRR (mm3/min)	100	40	9	6	250
      Ra (µm)	100	40	15	6	50

      Table XII Optimum Value

   4. CONCLUSION

Al7075+10%Al2O3 metal matrix composite (MMC) was fabricated successfully using stir casting process and Taguchi L18 orthogonal array was used to conduct the experiment. From the S/N ratio and ANOVA analysis the following conclusion were drawn

1. Using taguchi method MRRR and Ra were optimized individually.

2. Bed speed is the most influential parameter which significantly affect the material removal rate (MRR). The voltage, pulse-on, pulse-off, current are less influential parameters.

3. According to proposed levels of factors used in this work to maximize MRR can be achieved by selecting combination of parameters, Voltage 100 (volts), Pulse-on 40 (µs), Pulse-off 9(µs), Current 6 Amps, Bed speed 250 (µm/s).

4. Current is the most influential parameter which significantly affect the surface roughness (Ra). The voltage, pulse-on, pulse-off, Bed speed are less influential parameters.

5. For achieving minimum surface roughness the optimum condition are Voltage 100 (volts), Pulse-on 40 (µs), Pulse-off 9(µs), Current 6 Amps, Bed speed 250 (µm/s).

REFERENCES

[1]. S.S.Mahapatra.Amar Patnaik Optimization of wire electrical discharge machining (WEDM) process parameters using Taguchi method International Journal of Advanced manufacturing technology, 2006.

[2]. Sateesh Kumar Reddy K, Ramesh S, Parametric Optimization of Wire Electrical Discharge Machining of Composite Material International Journal of Advanced Research in Computer Engineering & Technology Volume 1, Issue 3, May2012

[3]. C.D.Shah, J.R.Mevada, B.C.Khatri, Optimization of Process Parameter of Wire Electrical Discharge Machine by Response Surface Methodology on Inconel-600 International Journal of Emerging Technology and Advanced Engineering (ISSN 2250- 2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 4,

April 2013)

[4]. Jadi Laxman and Kotakonda Guru Raj Optimization of Electric Discharge Machining Process Parameters Using Taguchi Technique International Journal of Advanced Mechanical Engineering ISSN 2250-3234 Volume 4, Number 7

(2014).

[5]. Amit Nara and Sahil Takhi Optimization of Process Parameters in Finishing of Weld Bead Using WEDM International Journal of Innovative Research in Science, Engineering and Technology (An ISO 3297: 2007 Certified Organization) Vol. 3, Issue 8,

August 2014.

[6]. Pujari Srinivasa Rao, Koona Ramji and Beela Satyanarayana Experimental Investigation of wire EDM parameter for surface roughness MRR and white layer in machining of aluminium alloy International Conference On Advance In Manufacturing And Materials Engineering AMM 2014.

[7]. Miss. Dipti Ghanashyam Gonjari Investigation of Effect of WEDM Process Parameters on Performance Characteristics of Tool Steel Grade AISI D7 IRACST Engineering Science and Technology: An International Journal (ESTIJ), ISSN: 2250- 3498 Vol.4, No.5, October 2014.

[8]. Muthu Kumar V, Suresh Babu A, Venkatasamy R and Raajenthiren M Optimization of the WEDM Parameters on Machining Incoloy800 Super alloy with Multiple Quality Characteristics International Journal of Engineering Science and Technology Vol. 2(6), 2010, 1538-1547.

[9]. Pragya Shandilya, P.K.Jain, N.K.Jain Parametric Optimization During Wire Electrical Discharge Machining Using Response Surface Methodology

[10]. J.Udaya Prakash, T.V. Moorthy, J.Milton Peter Experimental Investigations on Machinability Of Aluminium Alloy (A413)/ Flyash /B4C Hybrid Composites Using Wire EDM international conference on design and manufacturing, icon DM 2013.