- Email: [email protected]
Optimization of EDM process parameter for stainless steel D3
Materials Today: Proceedings xxx (xxxx) xxx
Contents lists available at ScienceDirect
Materials Today: Proceedings journal homepage: www.elsevier.com/locate/matpr
Optimization of EDM process parameter for stainless steel D3 Faijan Khan, Jitendra kumar ⇑, Tarun Soota Bundelkhand Institute of Engineering and Technology, Jhansi, 284128 U.P, India
a r t i c l e
i n f o
Article history: Received 21 March 2019 Received in revised form 6 July 2019 Accepted 18 July 2019 Available online xxxx Keywords: Electrical discharge machining Stainless steel D3 Taguchi design ANOVA Regression analysis
a b s t r a c t In this paper, stainless steel (D3) workpiece was use with Taguchi L9 design to conduct experiments on electrical discharge machine (EDM). Pulse-on-time (Ton), Current (A), and Voltage (V) are considered as independent parameters and optimize the response variables material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR). Analysis of variance (ANOVA) and signal-to-noise (S/N ratio) was used to find out optimum independent parameters and their levels. Optimum parameters for response variables are given as MRR (A-7 A, Ton-20 ls, V-125 V), TWR (A-1 A, Ton-10 ls, V-100V) and SR (A-1 A, Ton-10ls, V-150 V) respectively. Ó 2019 Elsevier Ltd. All rights reserved. Peer-review under responsibility of the scientific committee of the 2nd International Conference on Computational and Experimental Methods in Mechanical Engineering.
1. Introduction Electrical discharge machining (EDM) is essentially electrothermal, non-conventional material removal process, which is broadly used to produce parts such as punches, moulds, finished parts for spacecraft and car industry. Machining of ceramics and surgical segments also machined by EDM. Electric discharge machining (EDM) is accomplished when electrical spark generated between anode and cathode, extreme heat generated close to the melts zone and dissipates materials in the melts zone. For enhancing the adequacy of the procedure, the workpiece and the apparatus are submerged in a dielectric liquid. An appropriate gap, known as spark gap, is kept up between the tool electrode and the workpiece surfaces. In an investigation, conducted on M 300 Steel to find surface characteristics, concluded that the parameter current has been found to be the maximum result on the surface roughness trailed by voltage and pulse on time [1]. To analyse effect of all process parameters on response a proper DOE is needed. Taguchi method is a powerful tool in DOE to reduce number of experimental runs and noise in the system. During machining of C–C composite implementation of Taguchi method in EDM process [2,3]. Investigation to put a light on the relation among the material removal and tool wear ratio has been given for Ni-Cr-Mo steel and suggested that open voltage was most significant factor [4]. Economical aspects of EDM discussed with respect to input parameters ⇑ Corresponding author. E-mail address: [email protected] (J. kumar).
and response parameters [5]. For EN 31 die steel, it’s found that peak current have most influencing factor for surface roughness trailed by pulse-on-time [6–8]. Machining of D2 and H13 tool steel with EDM, and found connection among participation parameters (Discharge Current and pulse on time) and surface crack formation [9]. Surface roughness (SR) of aluminium-7075 metal matrix composite initially increased speedily with a rise in pulse off-time and then decreases slowly with a rise in pulse off-time. Negative polarity of electrode is responsible for dropping the surface roughness and growing pulse-on-time leads to yield further uneven surfaces [10,11]. MRR mainly influenced by peak current in AISI D3 material [12]. High MRR was gotten in positive extremity, while better surface quality (surface unpleasantness and white layer thickness) in negative extremity [13,14]. Optimization and DOE of the experimental runs for HSS M2 grade and steel D2 work materials discussed by the researchers [15,16]. A nano fluid mixture of alumina and graphite during turning operation has been discussed by the researchers. Presence of nano fluid during machining reduce tool wear and increase tool life and surface finish [17–19]. This work investigates, potential procedure parameters affecting the MRR, TWR and SR, while machining of steel D3 using electric discharge machining process. This paper explores the connection between the different input process parameters like Pulse-on time (Ton), Applied voltage (Vo) and Current (A) on surface roughness (SR), material removal rate (MRR) and tool wear rate (TWR). Taguchi quality design strategy utilized to increase the quantity of test measure in the test. It’s a successful logical methodology for determining the ideal machining parameters and decreasing test cost. Analysis of variance (ANOVA) and
https://doi.org/10.1016/j.matpr.2019.07.529 2214-7853/Ó 2019 Elsevier Ltd. All rights reserved. Peer-review under responsibility of the scientific committee of the 2nd International Conference on Computational and Experimental Methods in Mechanical Engineering.
Please cite this article as: F. Khan, J. kumar and T. Soota, Optimization of EDM process parameter for stainless steel D3, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.07.529
2
F. Khan et al. / Materials Today: Proceedings xxx (xxxx) xxx
signal-to-noise (S/N ratio) was use to find out optimum independent parameters and their levels. The regression analysis gives relation between dependent and independent parameters. 2. Experimental details The mechanical properties and Chemical composition of material die steel D-3 is shown in the Tables 1 and 2 respectively. The work piece of Die Steel D-3 (10 mm diameter and 5 mm thickness) is used in this experiment. Then with the help of EDM with model ELECTRONICA S-70 ZNC EDM machining operation was performed. Voltage fluctuation, Machine off-set and copper rod with rod diameter 2 mm are fixed process parameters. Table 3 shows diverse independent process constraints and their corresponding levels. 3. Result and discussion In this paper, discussion is carried about the effects or influence of machining parameter, i.e. pulse-on-time (Ton), Current (A), Voltage (Vo) on material removal rate (MRR), surface roughness (SR) and tool wear rate (TWR). 3.1. Material removal rate The ratio of change in weight of the workpiece to machining time and density of the material is called material removal rate (MRR). Mathematical representation of MRR, for all experiments is given by Eq. (1).
MRR ¼
Wa Wb qt
ð1Þ
where W a ,W b is the weight of the workpiece material (grams) before and after respectively and ‘t’ is the time period (minutes) and q is density of the work piece. 3.2. Surface roughness Smoothness of the work piece is measure by surface roughness. Estimations of surface roughness was estimated by Mitutoyo Surface Roughness Tester SJ – 178-602 by an appropriate strategy
3.3. Tool wear rate It characterized as the volumetric proportion of material expulsion on tool electrode. The TWR is ratio of tool weight loss (in grams) to the product of density of tool material (gm/cc) and the machining time. Littler the tool wear rate in the EDM procedure, the better is the machining execution. In this way, tool wear rate is the lower-the-better execution qualities. The tool wear rate is calculated by dividing the tool weight loss (in grams) to the product of density of the tool material (gm/cc) and the machining time. Experimental results for MRR, TWR and SR with respect to Taguchi design are appeared in Table 4.
3.4. Signal-to-noise (S/N) ratio for MRR, TWR, SR There are three classify of execution trademark in the investigation of the S=N ratio that is the smaller-the-better, the larger-thebetter, and the average-the-better. The S/N ratio for different level of process parameter is calculated based on the S=N analysis (given by Eqs. (2) and (3)). S=N ratio for each response is presented in the Table 5.
S Larger is better ðmaximumÞ NLB X ¼ 10log ð1=NÞ ð1=yi Þ2
Smaller is better ðminimumÞ
ð2Þ
X S ¼ 10log ð1=NÞ ðyi Þ2 NNB
ð3Þ
where, ðN Þ is the number of observations or repetitions of a trial and ðyÞ is the observed data. After calculating S/N ratio for each response, mean S=N ratio is calculated for each factor and levels. After conducting the S=N ratio, analysis of variance has been found out with the help of MINITAB. Current has a maximum contribution on MRR followed by Pulse on time, Voltage respectively as given in Table 6. Current have maximum effect on Tool Wear Rate trailed by voltage and Pulse on time respectively as given in Table 7. Pulse on time have maximum effect on Surface Roughness trailed by voltage and current respectively as shown in Table 8.
Table 1 Mechanical properties of Material Die Steel D-3. Density (g/cm3)
Melting point (°C)
Yield strength (MPa)
Elastic modulus (GPa)
Poisson’s Ratio
Brinell Hardness
7.87
1421
470
190
0.28
215
Table 2 Chemical composition of Die Steel D-3. Material
Fe
Ni
Mn
Cr
C
Si
Cu
V
Mo
%Composition
86.58
0.0689
0.269
11.05
2.07
0.191
0.00367
0.0218
<0.002
Table 3 Independent process variables with levels. S.No.
Constraints
Units
Level 1
Level 2
Level 3
1 2 3
Current(A) Pulse-on-time (Ton) Voltage (Vo)
A msec V
1 10 100
4 15 125
7 20 150
Please cite this article as: F. Khan, J. kumar and T. Soota, Optimization of EDM process parameter for stainless steel D3, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.07.529
3
F. Khan et al. / Materials Today: Proceedings xxx (xxxx) xxx Table 4 Taguchi design and experimental results for MRR, TWR and SR. Exp. No
Current (A)
Pulse-on-time (ls)
Voltage (Vo)
MRR (mm3/min)
TWR (mm3/min)
SR (lm)
1 2 3 4 5 6 7 8 9
1 1 1 4 4 4 7 7 7
10 15 20 10 15 20 10 15 20
100 125 150 125 150 100 150 100 125
0.0787 0.1705 0.2046 0.2325 0.2842 0.3410 0.4650 0.6394 0.7673
0.0172 0.0372 0.0446 0.1015 0.0620 0.0372 0.2029 0.2790 0.2832
9.78 10.40 12.72 8.97 10.25 13.12 8.11 13.78 15.03
Table 5 S/N ratio for MRR, TWR and SR. Exp. No
Current (A)
Pulse-on-time (ls)
Voltage (Vo)
S=Nratio for MRR (db)
S=Nratio for TWR (db)
S=Nratio for SR (db)
1 2 3 4 5 6 7 8 9
1 1 1 4 4 4 7 7 7
10 15 20 10 15 20 10 15 20
100 125 150 125 150 100 150 100 125
22.0805 15.3655 13.7819 12.6715 10.9275 9.3449 6.6509 3.8845 2.3007
35.2894 28.5891 27.0133 19.8707 24.1522 28.5891 13.8544 11.0879 10.9581
19.8068 20.3407 22.0897 19.0558 20.2145 22.3587 18.1804 22.7850 23.5392
Table 6 Contribution of parameters on MRR. Source
DOF
SS
Adj MS
F Value
Contribution
Current (A) Pulse on Time (Ton) Voltage Error Total
2 2 2 2 8
0.355752 0.048553 0.007814 0.004056 0.416176
0.177876 0.024277 0.003907 0.002028
87.70 11.97 1.93
85.48 11.67 1.88 0.97 100%
Table 7 Contribution of parameters on TWR. Source
DOF
SS
Adj MS
F Value
Contribution
Current (A) Pulse on Time (Ton) Voltage (V) Error Total
2 2 2 2 8
0.085842 0.000585 0.002337 0.003669 0.092433
0.042921 0.000292 0.001169 0.001834
23.40 0.16 0.64
92.87 0.63 2.53 3.97 100%
Table 8 Contribution of parameters on surface roughness. Source
DOF
SS
Adj MS
F Value
Contribution
Current (A) Pulse on Time (Ton) Voltage (V) Error Total
2 2 2 2 8
4.161 32.784 5.287 2.964 45.196
2.081 16.392 2.643 1.482
1.40 11.06 1.78
9.21 72.54 11.70 6.55 100%
3.5. Regression analysis of MRR, TWR and SR
TWR ¼ 0:031 þ 0:0370 A þ 0:00145 Ton 0:000159 Vo
ð5Þ
Regression is a statistical tool to determine the relation between independent parameters and dependent parameters, and it also gives that which dependent parameters are more influence by the independent parameters. Regression equation 4–6 for MRR, TWR and SR respectively are given below.
SR ¼ 8:12 þ 0:223 A þ 0:467 Ton 0:0373 Vo
ð6Þ
MRR ¼ 0:142 þ 0:0788 A þ 0:0179 Ton 0:00070 Vo
ð4Þ
Normal probability of the residual’s plots (Fig. 1,(a),(b),(c)) are drawn by the MINITAB software, its clearly shows that data are in close agreement with the fitted line. R2 values for MRR, TWR and SR are 92.5%, 80.4% and 89.9% respectively gives that modal are consistent with in the domain.
Please cite this article as: F. Khan, J. kumar and T. Soota, Optimization of EDM process parameter for stainless steel D3, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.07.529
4
F. Khan et al. / Materials Today: Proceedings xxx (xxxx) xxx
Fig. 1. Normal probability plot (a) Normal probability plot of the residuals for material removal rate (MRR) (b) Normal probability of the residuals for tool wear rate (TWR) (c) Normal probability of the residuals for surface roughness (SR).
4. Conclusion: The concerned parameters, i.e. rate of metal removal, surface roughness, and tool wear rate have been checked by changing machining parameters on workpiece stainless steel D3. The copper wire of 2.0 mm diameter has been used as an electrode in EDM. The performance parameters of Pulse-on-time (Ton), Current (A) and Voltage (V) were analysed. Taguchi L9 orthogonal array has been used for experimental design. Signal-to-noise ratio (S/N) and analysis of variance (ANOVA) are employed to measure optimum machining parameters. Optimum parameters for material removal rate (Current-7 A, Pulse on Time-20 ls, Voltage-125 V), tool wear rate (Current1 A, Pulse on Time-10 ls, Voltage-100 V) and for surface roughness (Current-1 A, Pulse on Time-10 ls, Voltage 150 V) has been calculated by signal-to-noise ratio (S/N) and ANOVA. Regression analysis and R2 values for MRR, TWR and SR are 92.5%, 80.4% and 89.9% correspondingly gives significant results.
References [1] D.P. Nair, B.C. Yeldose, C.K. Varghese, Investigation of surface roughness on M300 steel machined By EDM, Int. J. Eng. Innov. Technol. 4 (2) (2014) 91–94. [2] P.P.M. George, B.K. Rahunath, L.M. Manocha, A.M. Warrier, EDM machining of carbon composite a Taguchi approach, J. Mater. Process. Technol. 145 (2004) 66–71. [3] S.B. Salem, W. Tebni, E. Bayraktar, Prediction of surface roughness by experimental design methodology in Electrical Discharge Machining, J. Achiev. Mater. Manuf. Eng. 49 (2011) 2. [4] F. Modica, V. Marrocco, G. Copani, I. Fassi, Sustainable micro-manufacturing of micro-components via micro electrical discharge machining, Sustainability 3 (2011) 2456–2469. [5] S. Kumar, Current research trends in electrical discharge machining, Res. J. Eng. Sci. 2 (2) (2013) 56–60.
[6] S.G. Amoljit, S. Kumar, Surface roughness evaluation for EDM of En31 with CuCr-Ni powder metallurgy tool, Int. J. Mech. Aerospace, Ind., Mechatronic Manuf. Eng. 8 (2014) 1308–1313. [7] H. Singh, Investigating the effect of copper chromium and aluminum electrodes on EN-31 die steel on electric discharge machine using positive polarity, Proceedings of the World Congress on Engineering, London, U.K., 2012. [8] N. Malhotra, S. Rani, K. Sheikh, Optimization of control parameters for surface roughness in side flushing form of die sink EDM, J. Eng. Res. Stud. 2 (4) (2011) 228–233. [9] H.T. Lee, T.Y. Tai, Relationship between EDM parameters and surface crack formation, J. Mater. Process. Technol. 142 (2003) 676–683. [10] S. Rajesha, C.S. Jawalkar, R.R. Mishra, A.K. Sharma, P. Kumar, Study of recast layers and surface roughness on Al-7075 metal matrix composite during EDM machining, Int. J. Recent Adv. Mech. Eng. 3 (1) (2014) 53–62. [11] G. Singh, P. Singh, G. Tejpal, B. Singh, Effect of machining parameters on H13 Steel in EDM process using powder mixed fluid, Int. J. Adv. Eng. Res. Stud. 2 (2001) 148–150. [12] P.R. Kubade, V.S. Jadhav, An Experimental investigation of electrode wear rate, material removal rate and radial overcut in EDM of High carbon-high chromium steel (AISI D3), Int. J. Eng. Adv. Technol. 1 (5) (2012) 135–140. [13] K.H. Syed, K. Palaniyandi, Performance of electrical discharge machining using aluminium powder suspended distilled water, Turkish J. Eng. Environ. Sci. 36 (2012) 195–207. [14] K. Ojha, R.K. Garg, K.K. Singh, MRR improvement in sinking electrical discharge machining: a review, J. Miner. Mater. Charact. Eng. 9 (8) (2010) 709–739. [15] A.S. Shivade, V.D. Shinde, Multi-objective optimization in WEDM of D3 tool steel using integrated approach of Taguchi method and Grey relational analysis, J. Ind. Eng. Int. 10 (4) (2014) 149–162, https://doi.org/10.1007/ s40092-014-0081-7. [16] A. Kumar, T. Soota, J. Kumar, Optimisation of wire-cut EDM process parameter by Grey-based response surface methodology Journal of Industrial Engineering, International (2018), https://doi.org/10.1007/s40092-018-0264-8. [17] R.K. Singh, A.K. Sharma, A.R. Dixit, A.K. Tiwari, A. Pramanik, A. Mandal, Performance evaluation of alumina-graphene hybrid nano-cutting fluid in hard turning, J. Cleaner Prod. 162 (2017) 830–845, https://doi.org/10.1016/j. jclepro.2017.06.104. [18] A.K. Sharma, A.K. Tiwari, A.R. Dixit, R.K. Singh, M. Singh, Novel uses of alumina/graphene hybrid nanoparticle additives for improved tribological properties of lubricant in turning operation, Tribol. Int. 119 (2018) 99–111, https://doi.org/10.1016/j.triboint.2017.10.036. [19] A.K. Sharma, A.K. Tiwari, R.K. Singh, A.R. Dixit, Tribological investigation of TiO2 nanoparticle based cutting fluid in machining under minimum quantity lubrication (MQL), Mater. Today:. Proc. 3 (6) (2016) 2155–2162, https://doi. org/10.1016/j.matpr.2016.04.121.
Please cite this article as: F. Khan, J. kumar and T. Soota, Optimization of EDM process parameter for stainless steel D3, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.07.529
Contents lists available at ScienceDirect
Materials Today: Proceedings journal homepage: www.elsevier.com/locate/matpr
Optimization of EDM process parameter for stainless steel D3 Faijan Khan, Jitendra kumar ⇑, Tarun Soota Bundelkhand Institute of Engineering and Technology, Jhansi, 284128 U.P, India
a r t i c l e
i n f o
Article history: Received 21 March 2019 Received in revised form 6 July 2019 Accepted 18 July 2019 Available online xxxx Keywords: Electrical discharge machining Stainless steel D3 Taguchi design ANOVA Regression analysis
a b s t r a c t In this paper, stainless steel (D3) workpiece was use with Taguchi L9 design to conduct experiments on electrical discharge machine (EDM). Pulse-on-time (Ton), Current (A), and Voltage (V) are considered as independent parameters and optimize the response variables material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR). Analysis of variance (ANOVA) and signal-to-noise (S/N ratio) was used to find out optimum independent parameters and their levels. Optimum parameters for response variables are given as MRR (A-7 A, Ton-20 ls, V-125 V), TWR (A-1 A, Ton-10 ls, V-100V) and SR (A-1 A, Ton-10ls, V-150 V) respectively. Ó 2019 Elsevier Ltd. All rights reserved. Peer-review under responsibility of the scientific committee of the 2nd International Conference on Computational and Experimental Methods in Mechanical Engineering.
1. Introduction Electrical discharge machining (EDM) is essentially electrothermal, non-conventional material removal process, which is broadly used to produce parts such as punches, moulds, finished parts for spacecraft and car industry. Machining of ceramics and surgical segments also machined by EDM. Electric discharge machining (EDM) is accomplished when electrical spark generated between anode and cathode, extreme heat generated close to the melts zone and dissipates materials in the melts zone. For enhancing the adequacy of the procedure, the workpiece and the apparatus are submerged in a dielectric liquid. An appropriate gap, known as spark gap, is kept up between the tool electrode and the workpiece surfaces. In an investigation, conducted on M 300 Steel to find surface characteristics, concluded that the parameter current has been found to be the maximum result on the surface roughness trailed by voltage and pulse on time [1]. To analyse effect of all process parameters on response a proper DOE is needed. Taguchi method is a powerful tool in DOE to reduce number of experimental runs and noise in the system. During machining of C–C composite implementation of Taguchi method in EDM process [2,3]. Investigation to put a light on the relation among the material removal and tool wear ratio has been given for Ni-Cr-Mo steel and suggested that open voltage was most significant factor [4]. Economical aspects of EDM discussed with respect to input parameters ⇑ Corresponding author. E-mail address: [email protected] (J. kumar).
and response parameters [5]. For EN 31 die steel, it’s found that peak current have most influencing factor for surface roughness trailed by pulse-on-time [6–8]. Machining of D2 and H13 tool steel with EDM, and found connection among participation parameters (Discharge Current and pulse on time) and surface crack formation [9]. Surface roughness (SR) of aluminium-7075 metal matrix composite initially increased speedily with a rise in pulse off-time and then decreases slowly with a rise in pulse off-time. Negative polarity of electrode is responsible for dropping the surface roughness and growing pulse-on-time leads to yield further uneven surfaces [10,11]. MRR mainly influenced by peak current in AISI D3 material [12]. High MRR was gotten in positive extremity, while better surface quality (surface unpleasantness and white layer thickness) in negative extremity [13,14]. Optimization and DOE of the experimental runs for HSS M2 grade and steel D2 work materials discussed by the researchers [15,16]. A nano fluid mixture of alumina and graphite during turning operation has been discussed by the researchers. Presence of nano fluid during machining reduce tool wear and increase tool life and surface finish [17–19]. This work investigates, potential procedure parameters affecting the MRR, TWR and SR, while machining of steel D3 using electric discharge machining process. This paper explores the connection between the different input process parameters like Pulse-on time (Ton), Applied voltage (Vo) and Current (A) on surface roughness (SR), material removal rate (MRR) and tool wear rate (TWR). Taguchi quality design strategy utilized to increase the quantity of test measure in the test. It’s a successful logical methodology for determining the ideal machining parameters and decreasing test cost. Analysis of variance (ANOVA) and
https://doi.org/10.1016/j.matpr.2019.07.529 2214-7853/Ó 2019 Elsevier Ltd. All rights reserved. Peer-review under responsibility of the scientific committee of the 2nd International Conference on Computational and Experimental Methods in Mechanical Engineering.
Please cite this article as: F. Khan, J. kumar and T. Soota, Optimization of EDM process parameter for stainless steel D3, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.07.529
2
F. Khan et al. / Materials Today: Proceedings xxx (xxxx) xxx
signal-to-noise (S/N ratio) was use to find out optimum independent parameters and their levels. The regression analysis gives relation between dependent and independent parameters. 2. Experimental details The mechanical properties and Chemical composition of material die steel D-3 is shown in the Tables 1 and 2 respectively. The work piece of Die Steel D-3 (10 mm diameter and 5 mm thickness) is used in this experiment. Then with the help of EDM with model ELECTRONICA S-70 ZNC EDM machining operation was performed. Voltage fluctuation, Machine off-set and copper rod with rod diameter 2 mm are fixed process parameters. Table 3 shows diverse independent process constraints and their corresponding levels. 3. Result and discussion In this paper, discussion is carried about the effects or influence of machining parameter, i.e. pulse-on-time (Ton), Current (A), Voltage (Vo) on material removal rate (MRR), surface roughness (SR) and tool wear rate (TWR). 3.1. Material removal rate The ratio of change in weight of the workpiece to machining time and density of the material is called material removal rate (MRR). Mathematical representation of MRR, for all experiments is given by Eq. (1).
MRR ¼
Wa Wb qt
ð1Þ
where W a ,W b is the weight of the workpiece material (grams) before and after respectively and ‘t’ is the time period (minutes) and q is density of the work piece. 3.2. Surface roughness Smoothness of the work piece is measure by surface roughness. Estimations of surface roughness was estimated by Mitutoyo Surface Roughness Tester SJ – 178-602 by an appropriate strategy
3.3. Tool wear rate It characterized as the volumetric proportion of material expulsion on tool electrode. The TWR is ratio of tool weight loss (in grams) to the product of density of tool material (gm/cc) and the machining time. Littler the tool wear rate in the EDM procedure, the better is the machining execution. In this way, tool wear rate is the lower-the-better execution qualities. The tool wear rate is calculated by dividing the tool weight loss (in grams) to the product of density of the tool material (gm/cc) and the machining time. Experimental results for MRR, TWR and SR with respect to Taguchi design are appeared in Table 4.
3.4. Signal-to-noise (S/N) ratio for MRR, TWR, SR There are three classify of execution trademark in the investigation of the S=N ratio that is the smaller-the-better, the larger-thebetter, and the average-the-better. The S/N ratio for different level of process parameter is calculated based on the S=N analysis (given by Eqs. (2) and (3)). S=N ratio for each response is presented in the Table 5.
S Larger is better ðmaximumÞ NLB X ¼ 10log ð1=NÞ ð1=yi Þ2
Smaller is better ðminimumÞ
ð2Þ
X S ¼ 10log ð1=NÞ ðyi Þ2 NNB
ð3Þ
where, ðN Þ is the number of observations or repetitions of a trial and ðyÞ is the observed data. After calculating S/N ratio for each response, mean S=N ratio is calculated for each factor and levels. After conducting the S=N ratio, analysis of variance has been found out with the help of MINITAB. Current has a maximum contribution on MRR followed by Pulse on time, Voltage respectively as given in Table 6. Current have maximum effect on Tool Wear Rate trailed by voltage and Pulse on time respectively as given in Table 7. Pulse on time have maximum effect on Surface Roughness trailed by voltage and current respectively as shown in Table 8.
Table 1 Mechanical properties of Material Die Steel D-3. Density (g/cm3)
Melting point (°C)
Yield strength (MPa)
Elastic modulus (GPa)
Poisson’s Ratio
Brinell Hardness
7.87
1421
470
190
0.28
215
Table 2 Chemical composition of Die Steel D-3. Material
Fe
Ni
Mn
Cr
C
Si
Cu
V
Mo
%Composition
86.58
0.0689
0.269
11.05
2.07
0.191
0.00367
0.0218
<0.002
Table 3 Independent process variables with levels. S.No.
Constraints
Units
Level 1
Level 2
Level 3
1 2 3
Current(A) Pulse-on-time (Ton) Voltage (Vo)
A msec V
1 10 100
4 15 125
7 20 150
Please cite this article as: F. Khan, J. kumar and T. Soota, Optimization of EDM process parameter for stainless steel D3, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.07.529
3
F. Khan et al. / Materials Today: Proceedings xxx (xxxx) xxx Table 4 Taguchi design and experimental results for MRR, TWR and SR. Exp. No
Current (A)
Pulse-on-time (ls)
Voltage (Vo)
MRR (mm3/min)
TWR (mm3/min)
SR (lm)
1 2 3 4 5 6 7 8 9
1 1 1 4 4 4 7 7 7
10 15 20 10 15 20 10 15 20
100 125 150 125 150 100 150 100 125
0.0787 0.1705 0.2046 0.2325 0.2842 0.3410 0.4650 0.6394 0.7673
0.0172 0.0372 0.0446 0.1015 0.0620 0.0372 0.2029 0.2790 0.2832
9.78 10.40 12.72 8.97 10.25 13.12 8.11 13.78 15.03
Table 5 S/N ratio for MRR, TWR and SR. Exp. No
Current (A)
Pulse-on-time (ls)
Voltage (Vo)
S=Nratio for MRR (db)
S=Nratio for TWR (db)
S=Nratio for SR (db)
1 2 3 4 5 6 7 8 9
1 1 1 4 4 4 7 7 7
10 15 20 10 15 20 10 15 20
100 125 150 125 150 100 150 100 125
22.0805 15.3655 13.7819 12.6715 10.9275 9.3449 6.6509 3.8845 2.3007
35.2894 28.5891 27.0133 19.8707 24.1522 28.5891 13.8544 11.0879 10.9581
19.8068 20.3407 22.0897 19.0558 20.2145 22.3587 18.1804 22.7850 23.5392
Table 6 Contribution of parameters on MRR. Source
DOF
SS
Adj MS
F Value
Contribution
Current (A) Pulse on Time (Ton) Voltage Error Total
2 2 2 2 8
0.355752 0.048553 0.007814 0.004056 0.416176
0.177876 0.024277 0.003907 0.002028
87.70 11.97 1.93
85.48 11.67 1.88 0.97 100%
Table 7 Contribution of parameters on TWR. Source
DOF
SS
Adj MS
F Value
Contribution
Current (A) Pulse on Time (Ton) Voltage (V) Error Total
2 2 2 2 8
0.085842 0.000585 0.002337 0.003669 0.092433
0.042921 0.000292 0.001169 0.001834
23.40 0.16 0.64
92.87 0.63 2.53 3.97 100%
Table 8 Contribution of parameters on surface roughness. Source
DOF
SS
Adj MS
F Value
Contribution
Current (A) Pulse on Time (Ton) Voltage (V) Error Total
2 2 2 2 8
4.161 32.784 5.287 2.964 45.196
2.081 16.392 2.643 1.482
1.40 11.06 1.78
9.21 72.54 11.70 6.55 100%
3.5. Regression analysis of MRR, TWR and SR
TWR ¼ 0:031 þ 0:0370 A þ 0:00145 Ton 0:000159 Vo
ð5Þ
Regression is a statistical tool to determine the relation between independent parameters and dependent parameters, and it also gives that which dependent parameters are more influence by the independent parameters. Regression equation 4–6 for MRR, TWR and SR respectively are given below.
SR ¼ 8:12 þ 0:223 A þ 0:467 Ton 0:0373 Vo
ð6Þ
MRR ¼ 0:142 þ 0:0788 A þ 0:0179 Ton 0:00070 Vo
ð4Þ
Normal probability of the residual’s plots (Fig. 1,(a),(b),(c)) are drawn by the MINITAB software, its clearly shows that data are in close agreement with the fitted line. R2 values for MRR, TWR and SR are 92.5%, 80.4% and 89.9% respectively gives that modal are consistent with in the domain.
Please cite this article as: F. Khan, J. kumar and T. Soota, Optimization of EDM process parameter for stainless steel D3, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.07.529
4
F. Khan et al. / Materials Today: Proceedings xxx (xxxx) xxx
Fig. 1. Normal probability plot (a) Normal probability plot of the residuals for material removal rate (MRR) (b) Normal probability of the residuals for tool wear rate (TWR) (c) Normal probability of the residuals for surface roughness (SR).
4. Conclusion: The concerned parameters, i.e. rate of metal removal, surface roughness, and tool wear rate have been checked by changing machining parameters on workpiece stainless steel D3. The copper wire of 2.0 mm diameter has been used as an electrode in EDM. The performance parameters of Pulse-on-time (Ton), Current (A) and Voltage (V) were analysed. Taguchi L9 orthogonal array has been used for experimental design. Signal-to-noise ratio (S/N) and analysis of variance (ANOVA) are employed to measure optimum machining parameters. Optimum parameters for material removal rate (Current-7 A, Pulse on Time-20 ls, Voltage-125 V), tool wear rate (Current1 A, Pulse on Time-10 ls, Voltage-100 V) and for surface roughness (Current-1 A, Pulse on Time-10 ls, Voltage 150 V) has been calculated by signal-to-noise ratio (S/N) and ANOVA. Regression analysis and R2 values for MRR, TWR and SR are 92.5%, 80.4% and 89.9% correspondingly gives significant results.
References [1] D.P. Nair, B.C. Yeldose, C.K. Varghese, Investigation of surface roughness on M300 steel machined By EDM, Int. J. Eng. Innov. Technol. 4 (2) (2014) 91–94. [2] P.P.M. George, B.K. Rahunath, L.M. Manocha, A.M. Warrier, EDM machining of carbon composite a Taguchi approach, J. Mater. Process. Technol. 145 (2004) 66–71. [3] S.B. Salem, W. Tebni, E. Bayraktar, Prediction of surface roughness by experimental design methodology in Electrical Discharge Machining, J. Achiev. Mater. Manuf. Eng. 49 (2011) 2. [4] F. Modica, V. Marrocco, G. Copani, I. Fassi, Sustainable micro-manufacturing of micro-components via micro electrical discharge machining, Sustainability 3 (2011) 2456–2469. [5] S. Kumar, Current research trends in electrical discharge machining, Res. J. Eng. Sci. 2 (2) (2013) 56–60.
[6] S.G. Amoljit, S. Kumar, Surface roughness evaluation for EDM of En31 with CuCr-Ni powder metallurgy tool, Int. J. Mech. Aerospace, Ind., Mechatronic Manuf. Eng. 8 (2014) 1308–1313. [7] H. Singh, Investigating the effect of copper chromium and aluminum electrodes on EN-31 die steel on electric discharge machine using positive polarity, Proceedings of the World Congress on Engineering, London, U.K., 2012. [8] N. Malhotra, S. Rani, K. Sheikh, Optimization of control parameters for surface roughness in side flushing form of die sink EDM, J. Eng. Res. Stud. 2 (4) (2011) 228–233. [9] H.T. Lee, T.Y. Tai, Relationship between EDM parameters and surface crack formation, J. Mater. Process. Technol. 142 (2003) 676–683. [10] S. Rajesha, C.S. Jawalkar, R.R. Mishra, A.K. Sharma, P. Kumar, Study of recast layers and surface roughness on Al-7075 metal matrix composite during EDM machining, Int. J. Recent Adv. Mech. Eng. 3 (1) (2014) 53–62. [11] G. Singh, P. Singh, G. Tejpal, B. Singh, Effect of machining parameters on H13 Steel in EDM process using powder mixed fluid, Int. J. Adv. Eng. Res. Stud. 2 (2001) 148–150. [12] P.R. Kubade, V.S. Jadhav, An Experimental investigation of electrode wear rate, material removal rate and radial overcut in EDM of High carbon-high chromium steel (AISI D3), Int. J. Eng. Adv. Technol. 1 (5) (2012) 135–140. [13] K.H. Syed, K. Palaniyandi, Performance of electrical discharge machining using aluminium powder suspended distilled water, Turkish J. Eng. Environ. Sci. 36 (2012) 195–207. [14] K. Ojha, R.K. Garg, K.K. Singh, MRR improvement in sinking electrical discharge machining: a review, J. Miner. Mater. Charact. Eng. 9 (8) (2010) 709–739. [15] A.S. Shivade, V.D. Shinde, Multi-objective optimization in WEDM of D3 tool steel using integrated approach of Taguchi method and Grey relational analysis, J. Ind. Eng. Int. 10 (4) (2014) 149–162, https://doi.org/10.1007/ s40092-014-0081-7. [16] A. Kumar, T. Soota, J. Kumar, Optimisation of wire-cut EDM process parameter by Grey-based response surface methodology Journal of Industrial Engineering, International (2018), https://doi.org/10.1007/s40092-018-0264-8. [17] R.K. Singh, A.K. Sharma, A.R. Dixit, A.K. Tiwari, A. Pramanik, A. Mandal, Performance evaluation of alumina-graphene hybrid nano-cutting fluid in hard turning, J. Cleaner Prod. 162 (2017) 830–845, https://doi.org/10.1016/j. jclepro.2017.06.104. [18] A.K. Sharma, A.K. Tiwari, A.R. Dixit, R.K. Singh, M. Singh, Novel uses of alumina/graphene hybrid nanoparticle additives for improved tribological properties of lubricant in turning operation, Tribol. Int. 119 (2018) 99–111, https://doi.org/10.1016/j.triboint.2017.10.036. [19] A.K. Sharma, A.K. Tiwari, R.K. Singh, A.R. Dixit, Tribological investigation of TiO2 nanoparticle based cutting fluid in machining under minimum quantity lubrication (MQL), Mater. Today:. Proc. 3 (6) (2016) 2155–2162, https://doi. org/10.1016/j.matpr.2016.04.121.
Please cite this article as: F. Khan, J. kumar and T. Soota, Optimization of EDM process parameter for stainless steel D3, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2019.07.529