Optimization of EDM process parameters by using heuristic approach

Available online at www.sciencedirect.com

ScienceDirect Materials Today: Proceedings 5 (2018) 27036–27042

www.materialstoday.com/proceedings

ICAMM_2016

Optimization of EDM process parameters by using heuristic approach M.Dastagiria*, P. Srinivasa Raob, P. Madar Vallic a

Department of Mechanical Engineering, Annamacharya Institute of Technology and Sciences, Rajampet, A.P, 516126, b Department of Mechanical Engineering, Centurion University, Odhisa, India c Department of Mechanical Engineering, Al-Habeeb Engineering College, Hyderabad, Telangana, India

Abstract Electrical Discharge Machining (EDM) is unique and precise manufacturing method. And it is availed for generating intricate or unpretentious shapes and geometries within parts and assemblies. In any manufacturing process yield and quality are two essential aspects that have become great anxieties in today’s competitive market. Every industrialized unit mostly efforts on said areas in relation to the techniques as well as processes. Therefore, in this work ‘multi-objective optimization’ as a technique is solve the instantaneously dueled on to rail the quality and yield of EDM operation. The four input process parameters opted as a part of this technique are: Discharge current (IP), Pulse on time (Ton), Discharge voltage (v) and Inter Electrode Gap (IEG). These parameters are going to be examined in three different levels. Iam going to consider Material Removal Rate (MRR), Surface Roughness (Ra), Tool Wear Rate (TWR) as output and measured for each investigational run. The present paper focusing on proving maximization of MRR (in order to upsurge productivity), mitigate Surface Roughness (to improve off line quality) and parallel to lesser Tool Wear Rate of the electrode. Therefore, the Heuristic method thus has been adopted to forecast the results mentioned. © 2018 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of ICAMM-2016. Keywords: MRR; SR; TWR; Taguchi’s; Grey Relation Analysis.

1. Introduction In this cut throat world the manufacturing procedures is getting adopted from conventional to unconventional and sophisticated materials are utilized. Among the various methods available, EDM is unique to synchronize sophisticated materials and procedures to go hand in hand sorting out the problems which ever might occur in the

* M.Dastagiri. Tel.: +91-9849211142. E-mail address: [email protected] 2214-7853 © 2018 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of ICAMM-2016.

M.Dastagiri / Materials Today: Proceedings 5 (2018) 27036–27042

27037

previous attempts. EDM works on thermo-electric method where materials removal takes place with exact spark generation. It is one of the utmost unconventional machining method existing today’s industrial operations. Dhar and Purohit [1] appraises the influence of current (c), pulse-on time (p) and air gap voltage (v) on MRR, TWR, ROC of EDM with Al–4Cu–6Si alloy–10 wt. % SiCP composites. This test done on PS LEADER ZNC EDM machine and a cylindrical brass electrode of 30 mm diameter. The noteworthy replicas were checked using ANOVA and discovers the Material Removal Rate, Tool Wear Rate and Radial over Cut rise significant in a non-linear fashion with growth in current. Karthikeyan et .al [2] worked on the mathematical molding of EDM with aluminum-silicon carbide particulate composites. A three level full factorial design was picked. Eventually vital prototypes were examined by ANOVA. Wei Bin et al. [3] worked on EDM with multiple holes in a work piece, and was successful in showing at least one electrical discharge unit for guiding a supply of electrical energy from the first and second electrode to the workpiece. Kunge et al. [4] worked on proven the outcome of electrode wear rate and material removal rate demonstration on the powder mixed electrical discharge machining (PMEDM) of cobalt-bonded tungsten carbide (WC-Co) was used. He further used the Response surface method to explore the work. The residuals thus found fall on a straight line inferring the errors which are usually spread. Additionally, this supports suitability of the least squares fit. The material removal rate normally grows with a rise of aluminum powder application. Tsai et al [5] have worked on material of copper alloys and graphite using electrical discharge machine as these materials have superb electrical, thermal conductivity and great melting point. The electrodes are made by powder metallurgy process from special powders and used to alter electric discharge machine surfaces in now days, to corrosion resistance and increase wear. Electrodes are made in a hot mounting machine at 20MPa and at 200°C temperature. As per the research results, explains a combining ratio of Cu–0wt% Cr and a sinter pressure of 20 MPa found better material removal rate. Likewise, the current effort also discloses that the composite electrodes got a greater material removal rate than Copper metal electrodes. The recast layer was thinner and fewer cracks are exists on the machined surface. Ajit Singh, Amitabha Ghosh [6], clarified the obliviousness of electrostatic strength acting on the surface of the metal for shot pulse durations (less than 5 μs). For lengthy pulses (greater than 100 μs), here electrostatic strength becomes very small and does not play a momentous role in the removal of metal. P.C. Pandey and S.T. Jilani [7] did an analytical model for the computation of erosion of electrodes by a single spark in EDM. An investigation for this calculation of the plasma channel size was obtained as a function of pulse on duration in EDM. It shows that accounting for the effects of the plasma growing leads to perceptible development in analytical results obtained. This exploration also advises a technique for appraising the thickness of the re-solidified layer in edm work pieces. B.Mohan and Satyanarayana [8] was succeeded in showing the influence of the Electric discharge machine electrode marital polarity, pulse duration, current and surface roughness, rotation of electrode on MRR and Tool wear rate of Al-Sic with 20-25 vol. % SiC on the Electric discharge machine. Polarity of the electrode and volume existing of SiC, the material removal rate rose with improved discharge current and specific current it reduced with growing in pulse duration. 2. Experimental Setup 2.1. Equipment used for experimentation The paraphernalia used to execute the trials is ROBOFORM 54 Charmillies Technologies die sinking equipment is shown in Fig. 1. The device is energized by pulse generator of 128A. As well, a jet cleaning system is used to adequate cleaning of the electric discharge machine method wreckage form the hole is working. Dielectric pressure is regulated physically while beginning the trials. The work piece material composition, physical and mechanical properties are shown in tables 1, 2 and 3 respectively.

Fig. 1. Die-Sinking ROBOFORM 54 EDM Machine.

27038

M.Dastagiri/ Materials Today: Proceedings 5 (2018) 27036–27042

Table 1 Chemical composition of Work Piece Material (SS-316) Chemical Composition Properties of SS-316 Type

Fe

C

Ni

Mn

Mo

Cr

SS-316

Bal

0.2%

10.9%

1.6%

2.1%

17.2%

Table 2 Physical properties of SS-316

Table 3 Mechanical Properties of SS-316

Density

7.99 gm/cc

Hardness, Rockwell B

79

Melting Point

1370° C

Tensile Strength, Ultimate

579 Mpa

Tensile Strength, Yield

290 Mpa

Modulus of Elasticity

193 Gpa

Poisson’s Ratio

0.25

Shear Modulus

77 Gpa

Thermal Conductivity

21.4 W/m-K

Electrical Resistivity

7.4x 10^-5 ohm-cm

Magnetic permeability

1.02

Table 4 Process parameters and their levels Inputs

Level 1

A. Discharge current(Amps) B. Pulse Time on

(μ sec)

C Pulse Time-Off (μ sec)

Level 2

Level 3

3

15

35

100

200

300

2

5

20

In this work considering three factors mixed level system is elected. The investigation has three variables at three altered sets shown in table.4. A full factorial experiment consists 34 = 81 trials. I led Taguchi experimentation with a L9 orthogonal array (9 trials, 3 variables, 3 levels) shown in table 5. Table 5 A typical L9 orthogonal arrays S.NO

A

B

C

1

1

1

1

2

1

2

2

3

1

3

3

4

2

1

2

5

2

2

3

6

2

3

1

7

3

1

3

8

3

2

1

9

3

3

2

Table 6 Design of experiments and experimental results Pulse Pulse TWR Exp Current on off MRR no (A) (μ (μ (mm3/min) (mm3/min) sec) sec) 1 3 100 2 6.608 0.426 2 3 200 5 8.202 0.764 3 3 300 20 6.810 1.735 4 15 100 5 16.360 4.292 5 15 200 20 18.898 6.101 6 15 300 2 24.456 4.213 7 35 100 20 53.278 12.55 8 35 200 2 45.658 10.370 9

35

300

5

58.088

11.168

Ra (μm) 3.6 3.8 3.9 6.8 7.8 9.8 8.2 8.6 9.4

2.2. Design of experiments observation table Design of Experiments aids to inspect the possessions of I/P variables on O/P variable (response) parallel. Now, I/P variables are pulse on time, current and pulse off time, are stated in table 6. These trials consist of sequence of trials, where decisive variations are prepared to the I/P variables, are MRR, TWR, Ra. 2.3. Material removal rate The volume of the material detached per minute is MRR. It is evaluated by Eq. (1).

MRR 

Wi  Wj   1000 Du  f 

MRR- (mm3/min), Wi = Primary weight of work piece (gms), Wf =Ending weight of work piece (gms), Dw =Density of work piece, t=time of run (sec)

(1)

M.Dastagiri / Materials Today: Proceedings 5 (2018) 27036–27042

27039

The tool wear rate (TWR) of the electrode is the amount of the tool wear per minute. It can be calculated using the following Eq.(2).

TWR 

Ti  Tf 1000 De  f 

(2)

2.4. Taguchi’s signal –to-noise ratio analysis on response variables Statistical analysis was carried out on the experimental obtained through Taguchi experimental design using statistical software MINITAB 17 as shown in table 7. In this step, S/N ratios are calculated for responses obtained from EDM operation and optimum combination of input parameters are determined based on the quality requirement. The normalization values of S/N ratio is given in table 8. In EDM process response characteristics such as surface roughness, Tool wear rate should be low for better quality, hence smaller S/N ratios are considered for these parameters. Whereas material removal is higher for machining so larger S/N ratios are considered. Signal-To-Noise ratio for Smaller the Better: 1. Tool wear rate 2. Surface roughness Signal to Noise Ratio for Larger-The-Better: 1.Material Removal Rate Type 1: = −10 Type 2:

S

Type 3:

S

N LB

NNB

[( )(∑



  10 log 10  Y

)]

(3)



(4)

 2 N

 10 log 10  1 S  2 

(5)

Where Yij is the value of the response “j” in the ith trial ailment, with i=1, 2, 3,…n; j = 1,2…k and S2 are the trial mean and variance Table 7 Signal-To-Noise Ratios for the Response variables Exp Response values S/N Ratios (dB) No MRR TWR Ra MRR TWR Ra 1 6.608 0.426 3.6 16.4014 7.411 -11.126

Table:8 Normalization of S/N ratio Exp No

Normalized Signal to Noise Ratios MRR 0.000

TWR 0.0000

Ra 0.0000

2

8.202

0.764

3.8

18.278

2.388

-11.5956

1

3

6.810

1.735

3.9

16.6629

-4.7859

-11.8212

2

0.6318

0.1720

0.0539

-16.6501

3 4

0.099 0.4170

0.4150 0.6820

0.0799 0.6351

5 6

0.4833 0.6020

0.7867 0.6773

0.7721 1

7 8

0.8892 0.9602

0.9435 0.9654

0.8720 0.8695

9

1

1

0.9583

4

16.360

4.292

6.8

24.275

-12.653

5

18.898

6.101

7.8

25.528

-15.708

-17.848

6

24.456

4.213

9.8

27.7677

-12.4918

-19.824

7

53.278

12.55

8.2

34.5307

-21.9728

-18.276

8

45.658

10.37

8.6

33.190

-20.3155

-18.689

9

58.088

11.168

9.4

35.2817

-20.9598

-19.462

2.5. Normalization of signal to noise ratio values For normalization of Signal to Noise Ratio values Higher is better for MRR Zij 

Yij  min(Yij ,i 1, 2 ,....n )

max(Yij ,i 1, 2 ,...n )  min(Yij ,i 1, 2 ,...n )

(6)

27040

M.Dastagiri/ Materials Today: Proceedings 5 (2018) 27036–27042

2.6. Calculation by grey relation coefficient (GRC) Grey relation coefficient for all the arrangements delivers the association between the best and actual normalized signal to noise ratio. If the two arrangements approve at all sets, then its grey relation coefficient is 1.The GRC performance characteristics in the ith experiment can be expressed as i ( K ) 

 min  max

(7)

 0 i ( K )  max

Where, Δ0i (k) is the deviance order of the position series and comparability order. Δoi (k) = ∥ y0 (k) − yi (k) ∥ Δmin = min min ∥ y0 (k) − yi (k) ∥ ⩝j ∈i

⩝k

Δmax = max max ∥ y0 (k) − yi (k) ∥ ⩝j ∈i

⩝k

Above Eq. indicates the series and y j (k) means the comparability series. ζ is unique or identified factor. The value of ζ is lesser and illustrates capacity is greater. ζ = 0.5 is normally used. The grey relational grade was resolute by averaging the grey relational coefficient consistent to each concert distinctive, and it is mentioned in the Table 10. The complete concert distinctive of the multiple response method determined by grey relational grade. The grey relational grade calculated by i 

1 n i ( K )  N k 1

(8)

Where,  i is the grey relational grade for the jth trial and k is the number of performance characteristics, is mentioned in the Table 11. Table 9 Deviation sequence of responses S.No MRR TWR Ra 1 1 1 1 2 0.9862 0.828 0.946 3 0.901 0.585 0.9201 4 0.583 0.318 0.3469 5 0.5167 0.2133 0.2279 6 0.398 0.3227 0 7 0.1108 0.0565 0.128 8 0.0398 0.3 0.1305 9 0 0 0.0417

Table 10 Grey Relation co-efficient S.No MRR TWR 1 0.3333 0.3333 2 0.3364 0.376 3 0.3568 0.4608 4 0.4616 0.611 5 0.4917 0.700 6 0.5567 0.6077 7 0.8185 0.8984 8 0.9262 0.625 9 1 1

Ra 0.3333 0.345 0.3520 0.5903 0.6869 1 0.7961 0.7930 0.9230

Table 11 Grey Relation grade S.No

CURRENT

PULSE ON

PULSE OFF

1 2 3 4 5 6 7 8 9

1 1 1 2 2 2 3 3 3

1 2 3 1 2 3 1 2 3

1 2 3 2 3 1 3 1 2

GREY RELATIO N GRADE 0.3333 0.3525 0.3898 0.5543 0.6262 0.7214 0.8376 0.7814 0.974

RANK 9 8 7 6 5 4 2 3 1 Fig.2 Grey relational grades for extreme MRR, lowest TWR and smallest Ra.

The Fig.2 shows the grey relational grades for least tool wear rate, lowest surface roughness and extreme material removal rate. Meanwhile the investigational strategy is orthogonal, it is feasible to diverse the influence of each machining parameter on the grey relational grade at dissimilar levels. For example, the mean of the grey relational grade for the pulse current at levels 1, 2 and 3 can be getting by averaging the grey relational grade for the trials 1 to 3, 4 to 6, and 7 to 9 correspondingly. The mean of the grey relational grade for each level of the machining parameters is précised and mentioned in Table 12

M.Dastagiri / Materials Today: Proceedings 5 (2018) 27036–27042

27041

The bigger the grey relational grade, the superior is the multiple performance characteristics. Nevertheless, the corresponding significance of the machining factors for the multiple performance characteristics still needs to be known, so that the best groupings of the machining constraint stages can be resolute more precisely. From the Table13, the best parameter mixture was resolute as A3 (pulse current, 35A), B3 (pulse on time, 300μs) and C2 (pulse off time, 5μs) Table 12. The factors and corresponding Grey relation grade Max-

Rank

Parameters

Level 1

Level 2

Level 3

Current

0.3585

0.634

0.8643

0.5058

1

Pulse on

0.5750

0.5800

0.6950

0.6200

2

Pulse off

0.6120

0.6269

0.6179

0.0149

3

Min

Fig.3 workpiece and tool used in the experiment.

2.7. Calculation by grey relation coefficient (GRC) The validation trial for the best parameters with its levels was led to appraise quality characteristics for EDM of Stainless steel 316. Table shows maximum grey relational grade, specifying the primary process parameter set of A3B3C2 for the finest multiple performance characteristics among the 9 experimentations. Table 13 briefs the contrast of the investigational results for the ideal conditions (A3B3C2) with forecasted results for optimal (A3B3C2) EDM parameters. The forecasted values are calculated by Predicted Response = Average of A3 + Average of B3 + Average of C2 – 2 x Mean of response (Yij) The response values obtained from the experimentations are compared with predicted values and show the noble contract between the expected and the investigational values. Table 13.Comparission of the expected & experimental values of the responses Optimal process parameters Experimental Level A3B3C2 MRR (mm3/min) 58.088 TWR (mm3/min) 11.1685 Ra (μm) 9.4

Predicted A3B3C2 57.682 11.486 9.6

3. Conclusion Taguchi’s S/N ratio and GRA are used in the present research to get better the multi-response characteristics such as MRR, TWR and Surface Roughness of SS 316 through EDM method. The present work concludes:  The optimal parameters mixture was finding as A3B3C2 i.e. pulse current at 35A, pulse ON time at 300μs and pulse OFF time at 5μs.  The expected results were examined with investigational results and a good treaty was established.  This work shows the implementing of Taguchi methods for optimizing the EDM constraints for multiple response characteristics.  In summary, the suggested work allows the manufacturing persons to choose the ideal values dependent on the manufacturing necessities and as a after-effect, mechanization of the method could be done based on the ideal values.

27042

M.Dastagiri/ Materials Today: Proceedings 5 (2018) 27036–27042

References [1]. Dhar, s., Purohit, r., Saini, n., Sharma, a. and Kumar, G.H., “Mathematical modeling of electric discharge machining of cast Al-4Cu-6Si alloy-10 wt.% sicp composites”, Journal of Materials Processing Technology, 193(1-3), 24-29. 2007. [2] Karthikeyan, R., Lakshmi Narayanan, P.R., and Naagarazan, R.S., “Mathematical modelling for electric discharge machining of aluminium–silicon carbide particulate composites”, J. Mater. Process. Tech., Vol.87, pp.59–63, 1999. [3] Wei; Bin, and Lee; Martin Kin-fei “Apparatus and method for electrical discharge machining multiple holes” United States Patent 6,373,018, et al. April 16, 2002. [4]. Kung, K.-., Horng, J.-. and Chiang, K. “Material removal rate and electrode wear ratio study on the powder mixed electrical discharge machining of cobalt-bonded tungsten carbide”, International Journal of Advanced Manufacturing Technology, 40(1-2), 95-104, 2009. [5]. Tsai, H.C., Yan, B.H. and Huang, F.Y., “EDM performance of Cr/Cu-based composite electrodes”, International Journal of Machine Tools and Manufacture, 43(3), 245-252, 2003. [6] Singh Ajit, Ghosh Amitabh, “A thermo-electric model of material removal during electric discharge machining”, International Journal of Machine Tools and Manufacture 39 (1999) 669e682, 1999. [7] P.C. Pandey, S.T. Jilani, “Experimetnal investigations into the performance of water as dielectric in EDM,” International Journal of Mechanical tool, vol. 24, no. 1, pp. 31-43, 1984. [8] B. Mohan, A. Rajadurai, K.G. Satyanarayana, “Effect of SiC and rotation of electrode on Electric Discharge Machining of Al-SiC composite”, Journal of Materials Processing Technology, 124 (2002) pg. no 297 – 304, 2002 [9] Y.S.Tarng, C.M.Tseng and L.K.Chung, “A. Fuzzy pulse discriminating system for electrical discharging machining”, Int. journal of Mfg., vol.37, No.4, pp511-522, 1997. [10] G.Petropoulos, N.M.Vaaxevanidis and C.pandazaras, “Modeling of surface finish in electro-discharge machining based upon statistical multi-parameter analysis”, Journal of Materials Processing Technology, Volumes 155-156, pages1247-1251, 30 November 2004. [11] W. j. Tomlinson, J.R. Adkin, “Microstructure and properties of electro discharge machined surfaces”, J. Surf. Eng 8(4)(1992)283-288, 1992. [12] D.C. Mongomery, Design and Analysis of Experiments, 5th ed., Willey, New York, 2001 [13] I.Puertas and C.J.Luis, “A study on the machining parameters optimization of electrical discharge machining”, journal of Materials processing Technology Volumes 143-144, pp 521-526, 20 December2003. [14] P.M.George, B.K.Raghunath, L.M, Manochac, AshishM.Warrier. EDM machining of carbon-carbon composite – a Taguchi approach. Journal of materials Processing Technology 145 (2005), pp66-71, 2005 [15] Yih-fong Tzeng, Fu-chen Chen, “A simple approach for robust design of high speed electrical-discharge machining technology”, Int. journal of machine tools & manufacture 43(2003), pp. 217-227, 2003. [16] H.Ramaswamy, L.Blunt, “Effect of EDM process parameters on 3D Surface topography”, Journal of Materials Processing Technology 148(2004) 155-164, 2004. [17] J.Simao, H.G.Lee, “Workpiece surface modification using electrical discharging machining”, Int. journal of Machine tools & Manufacture 43(2003)121-128, 2003. [18] E.Karthikeyan,P.R.Lakshminarayanan,R.S.Naagarazan,”Mathematical modelling for electric Discharge Machining of aluminium-silicon carbide particulate composites”, Journal of materials processing technology 87 (1999) 59 – 63, 1999. [19] C.J.uis, I.Puertas And G.Villa, “Material Removal Rate and Electrode wear study on the EDM of Silicon Carbide”, Journal of materials processing technology (2005) [20] C.J.uis, I.Puertas and G.Villa, “Spacing of roughness parameters study on the EDM of silicon carbide”, journal of materials processing technology (2005). [21] Keshang Wang, Yi Wang, “A. hybrid intelligent method for modelling the EDM processes”, Int. journal of Machine tools & manufacture 43(2003)995-999, 2003