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Experimental investigation of EDM process parameters in machining of 17-4 PH Steel using taguchi method
Available online at www.sciencedirect.com
ScienceDirect Materials Today: Proceedings 5 (2018) 5058–5067
www.materialstoday.com/proceedings
ICMPC 2017
Experimental investigation of EDM Process parameters in Machining of 17-4 PH Steel using Taguchi Method Chandramouli S.a*, Eswaraiah K.b. 1,2
Dept. Mechanical Engg, Kakatiya Institute of Technology & Science, Warangal,506015, India
Abstract Electrical discharge machining is one of the advanced unconventional machining processes. EDM process is based on thermoelectric energy between the work piece and an electrode. In the present study, the optimal setting of the process parameters of Electric Discharge Machining was determined. The important process parameters that have been selected are peak current, pulse on time, pulse off time and tool lift time with output response as Material Removal Rate and Surface Roughness. L27 Taguchi experimental design was used to conduct the experiments on 17-4 Precipitation Hardening Stainless Steel (PH Steel) with copper tungsten electrode. ANOVA method was used with the help of MINITAB 17 software to analysis the influence of input process parameters on output response. The process parameters were optimized in order to obtain maximum material removal rate and minimum surface roughness by considering the inter action effects of process parameters and the experimental results were validated by confirmation tests. The analysis of Taguchi method reveals that peak current, pulse on time and tool lift time have significantly affected the material removal rate and surface roughness. Surface topography of the machined samples is analyzed using Scanning Electron Microscopy for optimal levels of process parameters © 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of 7th International Conference of Materials Processing and Characterization. Keywords: copper tungsten electrode; MRR; Taguchi method; PH Steel; SR.
* Corresponding author. Tel.:+91-9849830659; E-mail address:[email protected] 2214-7853© 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of 7th International Conference of Materials Processing and Characterization.
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1. Introduction Electric Discharge Machining is one of the most extensively used non-conventional material removal process for difficult-to-cut materials. EDM is a thermo-electric process in which material is removed from work piece by erosion effect of series of electric discharges known as sparks between tool and work piece immersed in a dielectric liquid. Physical and metallurgical properties do not create any limitation for the materials to be machined on EDM very popular in machining difficult-to-cut materials. The EDM process has a very strong stochastic nature due to the complicated discharge mechanism that makes optimization difficult. Literature reports wide experimental and analytical studies on process modeling and optimization of EDM process to improve its accuracy and productivity. Aditya Kumar et al. [1] performed the parametric analysis of wire EDM parameters by taguchi method and developed a mathematical model for simultaneous optimization by hybrid genetic algorithm. Arshad Noor Siddiquee et al. [2] focused on optimizing deep drilling parameters of CNC lathe machine using solid carbide cutting tool on material AISI 321 austenitic stainless steel based on Taguchi method for minimizing surface roughness. Srinivasa Rao et al. [3] studied hybrid method combining grey, fuzzy and Taguchi approaches was implemented for submerged arc welding. S. Assarzadeh et al. [4] modeled and optimized process parameters in EDM of tungsten carbide-cobalt composite using cylindrical copper tool electrodes in planned machining based on statistical technique Response surface methodology has been used to plan and analyze the experiments. Chen et al. [5] had utilized Taguchi design methodology to optimize the EDM process parameters for the machining of A6061-T6 aluminum alloy. Nikalje et al. [6] used Taguchi method to determine the influence of process parameters and optimization of MDN 300 steel in EDM. Results showed that that the optimal level of the factors for TWR and SR were same but differed from the optimum levels of the factors for MRR and RWR. Kodlinge and Khire [7] had presented detailed investigation on MRR of Tungsten carbide for EDM operation using Kerosene as dielectric medium. Das et al.[8] performed investigation on the effect and optimization of machining parameters on Material Removal Rate in EDM of EN31 tool steel. Observed that the current has the most significant effect on MRR. Dhanabalan et al. [9] described the multi objective optimization based on the orthogonal array with the Grey relational analysis in EDM process ,studied the influence of parameters on the MRR, TWR and SR. S. Gopalakannan et al. [10] investigated the influence of process parameters and their interactions on MRR, EWR and SR of metal matrix composite of aluminum 7075 reinforced with 10 wt. % of B4C.Pragya Shandilya et al [11] optimized the process parameters during machining of SiCp/6061 Al metal matrix composite by wire Electrical discharge machining using response surface methodology and mathematical model have been developed to investigate the kerf, microstructure and surface roughness, concluded that input process parameters play a significant role in the minimization of kerf. Alikbari[12] optimized the process parameters using taguchi method in rotary electric discharge machining of X210CrNi12 alloy material with three copper electrodes: electrode without hole , electrode with one concentric hole and electrode with to symmetric eccentric hole. The tool hole numbers increases MRR, SR, and EWR increases due to the area of the electrode was decreased, and the discharged energy had a higher density and better flushing. From the above literature study several researches have been research done on the EDM process on various materials but not electrical discharge machining of 17-4 Precipitation Hardening Stainless Steel machined with copper tungsten electrode. In this paper, a EDM process is performed with four controllable process parameters peak current, pulse on time, pulse off time and tool lift time, while machining of the Hardening Stainless steel with copper tungsten tool electrode. Then, the machining conditions have been optimized for high performance machining using the Taguchi method because of its simplicity and it gives a systematic approach to optimize the process parameters. 2. Taguchi’s Signal to Noise (S/N) ratio: Taguchi design method is to identify the parameter settings which render the quality of the product or process robust to unavoidable variations in external noise. The relative “quality” of a particular parameter design is evaluated using a generic Signal-to-Noise (S/N) ratio. Depending on the particular design problem, different S/N ratios are applicable, including “lower is better” (LB), “nominal is best” (NB), or “higher is better” (HB). S/N ration can be calculated as a logarithmic transformation of loss function and the characteristics selected for MRR and SR are “higher is better” and “Lower is better” as given in equations 1 and 2 respectively. The experimental values and their corresponding S/N ration values for MRR and SR are shown in table 2.
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Chandramouli S et al./ Materials Today: Proceedings 5 (2018) 5058–5067
= −10
∑
... (1)
= −10
∑
... (2)
3. Experimental Methodology An L27 orthogonal array was used for experimentation and the input levels and number of experiments is decided based on the design of experiments. Input process parameters and their levels are presented in Table.1. At the end of the each experiment, the work piece and tool were removed, washed, dried, and weighed using the digital electronic weighing balance 1mg accuracy. The experiments are conducted for a machining time of the 10 minutes. Machining was done with straight polarity and EDM oil Grade 30 used as the dielectric fluid. Gap voltage is 30 V and flushing pressure maintained constant. To achieve validity and accuracy each test is repeated three replications. The input levels and number of experiments are decided based on the design of experiments. The work piece used for the experiments is 17-4 PH steel used in aerospace and die manufacturing. The work piece is in the form of a rectangular plate with dimensions of 60mm X 50mm X 5 mm. Experiments were conducted using Copper Tungsten (80W:20Cu grade) which had good electrical conductivity, high wear resistance. Due to this tool wear minimum while machining with copper Tungsten. Table.1 Process parameters and their levels S.NO. Process parameters Symbol
Level 1
Level 2
Level 3
1
Discharge current (A)
9
12
15
2
Pulse on time (µs)
B
50
100
200
3
Pulse off time (µs)
C
20
50
100
4
Lift time (µs)
D
10
20
50
A
4. Influence of process parameters on performance measures Pulse current: The effect of pulse current on MRR shows that as the pulse current increases MRR, increase is due to enhancement of spark energy that facilitates the action of melting and vaporization. This action results in advancing the impulsive force in the spark gap and thereby increasing the MRR. The surface roughness parameter increases with the increase in current rate due to increases in MRR Pulse on time: In the present study MRR decreases with increase in pulse on time, due to the amount of energy generated at high pulse on time not utilizing for removing the metal. At high values of pulse on current, instead of sparking in the inter electrode gap arcing observed. The surface roughness increases with decrease of pulse on time due to arcing between tool and work instead of sparking. Pulse off time: MRR increases with increase in pulse off time since sufficient time to flushing the eroded particles from gap between tool and work piece. The surface roughness increases with increase of pulse off time due to increase in MRR. Lift time: MRR decreases with increase in lift time, due to decrease in actual time of machining the work piece. the surface roughness increases with increase of tool lift. 5. Analysis of variance (ANOVA) The analysis of variance (ANOVA) is a common statistical technique to determine the percent contribution of each factor for results of the experiment. It calculates parameters known as sum of squares SS, degree of freedom (DOF), variance and percentage of each factor. The Sum of Squares is a measure of the deviation of the experimental data from the mean value of the data. The Fisher’s ratio is also called F value. F value is defined as the ratio of Mean square for the term to Mean square for the error term all these statistical calculations are done in MINI TAB 17.0 software. Response Tables for SN Ratios MRR and SR shown in Tables 3 and 4 respectively. Pulse current, pulse on time, pulse of time and pulse off time is assigned as rank 2, 1, 4 and 3 respectively according to
Chandramouli S et al./ Materials Today: Proceedings 5 (2018) 5058–5067
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their larger value of delta. Rank 1 means highest contribution factor and 4 means lowest contribution factor for both MRR and SR. The main effects plot for SN ratios and means are presented in Fig. 1 and Fig. 2 respectively. The result of ANOVA presented in Table6. From the ANOVA test, it is concluded that the pulse on time is the most significant EDM process parameter for affecting the MRR characteristics due to its highest percentage contribution pulse on time (77.30%) amongst the selected process parameters. The percentage contribution of other parameters in decreasing order is Discharge current (13.28%), lift time (1.52%) and pulse off time (0.52%). The percentage contribution of parameters interactions are I*Ton (4.41), I*Toff ((1.08) and I*Lift (0.02).Table 6 shows the results of ANOVA for the surface roughness. The highest percentage contribution pulse on time (83.55%) amongst the selected process parameters, the percentage of contribution of other parameters is the order of Discharge current (7.5%), lift time (0.75%) and pulse off time (0.507%). The percentage contribution of parameters interactions are I*Ton (0.66), I*Toff (3.79) and I*Lift (1.1).Therefore based on ANOVA results and from Fig. 1 and Fig. 2 the optimal process parameters are A3B1C3D1 for maximization of MRR and A1B3C1D1 for minimization of surface roughness for factors A, B, C and D, respectively. The optimal parameter settings of process parameters are presented in table 7. From Fig. 3 and Fig.5 there is no interaction between of current and pulse on time, current and lift time for MRR but from Fig. 4 slight interaction between current and pulse off time at 100 µs for MRR . The interaction effects of parameters for surface roughness are shown in Fig. 4, Fig. 5 and Fig. 6. It is observed that parameters interaction between current and pulse off time, current and Lift time for surface roughness as shown in below Fig 7 and Fig 8 , Fig 9. 6. Confirmation test After obtaining the optimal level of the EDM process parameters, the next step is to verify the percentage change of MRR and SR between initial settings and for this optimal combination. Table 8 compares the results of the confirmation experiments using the optimal EDM process parameters. As shown in Table 8, the total mean of MRR improved from 122.6 to 134.19 mg/min (an improvement of 8.63%) for optimal machining parameters of A3B1C3D1 and decrease of surface roughness from 9.78 to 2.89 μm (a decrement of 70.4%) for machining parameters of A1B3C1D1, which shows that optimal combination of the EDM process parameters are good enough to Maximization of MRR and minimization of SR of the machined surface. Table 2. L27 Machining Orthogonal array with the values of response variables S/N ratio
SR Ra
dB
µm
68.73
36.74
6.88
-16.75
20
66.96
36.51
7.63
-17.65
50
70.72
36.98
7.20
-17.15
20
20
24.37
27.74
7.45
-17.45
100
50
50
25.3
28.06
8.07
-18.14
100
100
10
23.7
27.51
5.61
-14.97
9
200
20
50
2.53
8.06
3.46
-10.78
9
200
50
10
6.51
16.26
3.97
-11.97
9
9
200
100
20
5.04
14.05
3.71
-11.38
10
12
50
20
20
47.66
33.57
7.31
-17.30
11
12
50
50
50
26.70
28.53
7.93
-17.98
12
12
50
100
10
94.07
39.47
8.01
-18.07
13
12
100
20
50
33.93
30.62
8.08
-18.15
14
12
100
50
10
29.81
29.48
6.68
-16.49
15
12
100
100
20
31.32
29.91
6.92
-16.80
S.No
current
Pulse on time
Pulse off time
Lift time
1
9
50
20
10
2
9
50
50
3
9
50
100
4
9
100
5
9
6
9
7 8
MRR mg/min
S/N ratio dB
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Chandramouli S et al./ Materials Today: Proceedings 5 (2018) 5058–5067 16
12
200
20
10
9.54
19.59
4.35
-12.78
17
12
200
50
20
11.12
20.82
4.17
-12.41
18
12
200
100
50
10.13
20.11
3.53
-10.97
19
15
50
20
50
122.6
41.76
9.78
-19.81
20
15
50
50
10
189.27
45.54
9.19
-19.27
21
15
50
100
20
162.8
44.23
10.54
-20.45
22
15
100
20
10
54.96
34.81
7.23
-17.19
23
15
100
50
20
45.27
33.12
8.24
-18.33
24
15
100
100
50
39.77
31.21
10.12
-20.11
25
15
200
20
20
15.42
23.75
3.58
-11.10
26
15
200
50
50
14.03
22.94
4.89
-13.79
27
15
200
100
10
16.03
24.09
4.86
-13.74
Table 3. Response Table for S/N Larger is better for MRR Level
I
Ton
Toff
Lift
1
25.77
38.15
28.51
30.39
2
28.01
30.36
29.03
29.30
3
33.58
18.85
29.82
27.67
Delta
7.81
19.30
1.30
2.71
Rank
2
1
4
3
Table 4. Response Table for S/N Smaller is better for SR Level I Ton
Toff
Lift
1
-15.14
-18.27
-15.70
-15.69
2
-15.66
-17.51
-16.22
-15.87
3
-17.09
-12.10
-15.96
-16.32
Delta
1.95
6.17
0.52
0.62
Rank
2
1
4
3
Table 5. Analysis of Variance for MRR Parameters DF Sum of squares
Mean square
F -Value #
Contribution %
I
2
291.42
145.712
19.83
Ton
2
1696.14
848.068
115.43#
13.28 77.30
Toff
2
7.74
3.868
0.53
0.35
Lift
2
33.55
16.77
2.28
1.52 #
I *Ton
4
96.86
24.215
3.30
I* Toff
4
23.79
5.946
0.81
1.08
I *Lift
4
0.5
0.126
0.02
0.02
Residual Error
6
44.08
7.347
Total
26
2194.07
#
Significant at 95% confidence level, F0.05, 2, 26= 3.3
4.41
2 100
Chandramouli S et al./ Materials Today: Proceedings 5 (2018) 5058–5067 Table 6. Analysis of Variance for SR Parameters DF Sum of squares
#
Mean square
F -Value #
5063
Contribution %
I
2
18.324
9.162
10.86
Ton
2
203.907
101.954
120.8#
7.50 83.55
Toff
2
1.239
0.620
0.73
0.507
Lift
2
1.854
0.927
1.1
0.75
I *Ton
4
1.632
0.408
0.48
0.66
I *Toff
4
9.261
2.314
2.74
3.79
I* Lift
4
2.765
0.691
0.82
1.1
Residual Error
6
5.064
0.844
Total
26
244.045
2.07 100
Significant at 95% confidence level, F0.05, 2, 26= 3.37 Table 7. Optimal parameter settings of process parameters Output parameters Optimal combination Level I T on T off Max. MRR A3B1C3D1 15 50 100 Min. SR A1B3C1D1 9 200 20
MRR(mg/min) SR(µm)
Initial setting A3B2C1D3 S/N ratio Mean 41.76 122.6 9.78 9.78
Lift 10 10
Table 8. Results of Confirmation Test Predicted value for optimal Experimental value for parameters optimal parameters S/N ratio Mean S/N ratio Mean 42.57 46.07 142.4 134.19 -10.75 -10.12 3.21 2.89
Fig. 1 Main effects plot for SN ratios of MRR
% Improvement 8.63 70.4
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Chandramouli S et al./ Materials Today: Proceedings 5 (2018) 5058–5067
Fig. 2 Main effects plot for SN ratios of SR
Fig. 3 Interaction plot of current and pulse on time for MRR
Fig. 4 Interaction plot of current and pulse off time for MRR
Chandramouli S et al./ Materials Today: Proceedings 5 (2018) 5058–5067
Fig. 5 Interaction plot of current and Lift time for MRR
Fig. 6 Interaction plot of current and pulse on time for SR
Fig. 7 Interaction plot of current and pulse off time for SR
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Chandramouli S et al./ Materials Today: Proceedings 5 (2018) 5058–5067
Fig. 8 Interaction plot of current and Lift time for SR
Fig. 9 SEM images of EDM for optimal parameters Maximum MRR and Minimum SR
7. Conclusion This paper investigates the optimal input process parameters for machining of 17-4 PH steel material with copper tungsten electrode on EDM using Taguchi method by considering the individual and interaction effects of input parameters. The parameters pulse on time and discharge current have shown significant effect on both MRR and SR, and parameter pulse off time has shown least significant effect compared to other parameters. There is no interaction effect of input parameters for MRR but slight interaction between parameters for surface roughness. The optimal combination of input parameters and their levels for the Maximization of MRR of the EDM process are A3B1C3D1and for minimization of surface roughness are A1B3C1D1. The result of ANOVA reveals that pulse on time has highest percentage contribution for MRR (58.3%) and for SR (76.7%). The confirmation experiments were conducted to verify the optimal machining parameters and there is a significant improvement in MRR and SR from initial machining parameter to the optimal machining parameters is about 8.63% and 70.4% respectively.
Chandramouli S et al./ Materials Today: Proceedings 5 (2018) 5058–5067
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REFERENCES [1] Aditya Kumar , Sachin Maheshwari, “Optimization of submerged Arc welding Rutile Based flux constituents by Hybrid Grey Fuzzy and Taguchi analysis”, Advanced Materials Manufacturing & Characterization”, Vol 5, Issue 2 (2015) 63-70. [2] Ashad Noor Siddiquee, Zahid A.Khan, Pankul Goel, Mukesh kumar, Gaur Agarwal and Noor Zaman Khan, “Optimization of Deep Drilling process Parameters of AISI 321 steel using Taguchi Method”, Procedia Materials Science 6( 2014) 1217-1225. [3] Srinivasa Rao.P, Koona Ramji, Bella Satyanarayana, “Experimental Investigation and Optimization of wire edm parameters for surface roughness , MRR and white layer in machining of Aluminium Alloy”, Procedia Materials Science 5(2014) 2197-2206. [4] Assarzadeh. S , Ghoreishi.M, “Statistical modeling and parametric optimization of process parameters in electro-discharge machining of cobalt-bonded tungsten carbide composite WC/6%Co”, Elsevier Procedia, CIRP 6( 2013) 463-468. [5] Chen, D., Jhang, J., Guo, M. “Application of taguchi design method to optimize the electrical discharge machining”, Journal of Achievements in Materials and Manufacturing Engineering, 57, (2013) 76–82. [6] Nikalje, A. M., Kumar, A., Srinadh, K. V. S., “Influence of parameters and optimization of EDM performance measures on MDN 300 steel using taguchi method”, International Journal of Advanced Manufacturing Technology (2013) 1–9. [7] Kodlinge, P. G., Khire, M., “Some studies on machinability of tungsten carbide during EDM operations”, International Journal of Engineering Science and Technology, 3(2012), 10–13. [8] Das, M., Kumar, K., Barman, T., Sahoo, P., “Optimization of material removal rate in EDM using taguchi method”, Advanced Materials Engineering and Technology, Vol 626( 2012), 270–274. [9] Dhanabalan, S., Sivakumar, K., Sathiya Narayanan, C., “Optimization of EDM process parameters with multiple performance characteristics for titanium grades”. European Journal of Scientific Research, 68( 2012), 297–305. [10] Gopalakannan, S. Senthilven, T., S.Ranganathan, “Modeling and optimization of EDM process parameters on machining of Al 7075-B4C MMC using RSM”, Elsevier Procedia Engineering 38 (2012), 685-690 [11] Pragya Shandilya, P.K.Jain, N.K.Jain, Parametric optimization during wire electric discharge machining using response surface methodology, Elsevier Procedia Engineering 38(2012), 2371- 2377. [12] Aliakbari, E. & Baseri, H. , “Optimization of machining parameters in rotary EDM process by using the Taguchi method”, International Journal of Advanced Manufacturing Technology (2012), ,1041–1053.
ScienceDirect Materials Today: Proceedings 5 (2018) 5058–5067
www.materialstoday.com/proceedings
ICMPC 2017
Experimental investigation of EDM Process parameters in Machining of 17-4 PH Steel using Taguchi Method Chandramouli S.a*, Eswaraiah K.b. 1,2
Dept. Mechanical Engg, Kakatiya Institute of Technology & Science, Warangal,506015, India
Abstract Electrical discharge machining is one of the advanced unconventional machining processes. EDM process is based on thermoelectric energy between the work piece and an electrode. In the present study, the optimal setting of the process parameters of Electric Discharge Machining was determined. The important process parameters that have been selected are peak current, pulse on time, pulse off time and tool lift time with output response as Material Removal Rate and Surface Roughness. L27 Taguchi experimental design was used to conduct the experiments on 17-4 Precipitation Hardening Stainless Steel (PH Steel) with copper tungsten electrode. ANOVA method was used with the help of MINITAB 17 software to analysis the influence of input process parameters on output response. The process parameters were optimized in order to obtain maximum material removal rate and minimum surface roughness by considering the inter action effects of process parameters and the experimental results were validated by confirmation tests. The analysis of Taguchi method reveals that peak current, pulse on time and tool lift time have significantly affected the material removal rate and surface roughness. Surface topography of the machined samples is analyzed using Scanning Electron Microscopy for optimal levels of process parameters © 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of 7th International Conference of Materials Processing and Characterization. Keywords: copper tungsten electrode; MRR; Taguchi method; PH Steel; SR.
* Corresponding author. Tel.:+91-9849830659; E-mail address:[email protected] 2214-7853© 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of 7th International Conference of Materials Processing and Characterization.
Chandramouli S et al./ Materials Today: Proceedings 5 (2018) 5058–5067
5059
1. Introduction Electric Discharge Machining is one of the most extensively used non-conventional material removal process for difficult-to-cut materials. EDM is a thermo-electric process in which material is removed from work piece by erosion effect of series of electric discharges known as sparks between tool and work piece immersed in a dielectric liquid. Physical and metallurgical properties do not create any limitation for the materials to be machined on EDM very popular in machining difficult-to-cut materials. The EDM process has a very strong stochastic nature due to the complicated discharge mechanism that makes optimization difficult. Literature reports wide experimental and analytical studies on process modeling and optimization of EDM process to improve its accuracy and productivity. Aditya Kumar et al. [1] performed the parametric analysis of wire EDM parameters by taguchi method and developed a mathematical model for simultaneous optimization by hybrid genetic algorithm. Arshad Noor Siddiquee et al. [2] focused on optimizing deep drilling parameters of CNC lathe machine using solid carbide cutting tool on material AISI 321 austenitic stainless steel based on Taguchi method for minimizing surface roughness. Srinivasa Rao et al. [3] studied hybrid method combining grey, fuzzy and Taguchi approaches was implemented for submerged arc welding. S. Assarzadeh et al. [4] modeled and optimized process parameters in EDM of tungsten carbide-cobalt composite using cylindrical copper tool electrodes in planned machining based on statistical technique Response surface methodology has been used to plan and analyze the experiments. Chen et al. [5] had utilized Taguchi design methodology to optimize the EDM process parameters for the machining of A6061-T6 aluminum alloy. Nikalje et al. [6] used Taguchi method to determine the influence of process parameters and optimization of MDN 300 steel in EDM. Results showed that that the optimal level of the factors for TWR and SR were same but differed from the optimum levels of the factors for MRR and RWR. Kodlinge and Khire [7] had presented detailed investigation on MRR of Tungsten carbide for EDM operation using Kerosene as dielectric medium. Das et al.[8] performed investigation on the effect and optimization of machining parameters on Material Removal Rate in EDM of EN31 tool steel. Observed that the current has the most significant effect on MRR. Dhanabalan et al. [9] described the multi objective optimization based on the orthogonal array with the Grey relational analysis in EDM process ,studied the influence of parameters on the MRR, TWR and SR. S. Gopalakannan et al. [10] investigated the influence of process parameters and their interactions on MRR, EWR and SR of metal matrix composite of aluminum 7075 reinforced with 10 wt. % of B4C.Pragya Shandilya et al [11] optimized the process parameters during machining of SiCp/6061 Al metal matrix composite by wire Electrical discharge machining using response surface methodology and mathematical model have been developed to investigate the kerf, microstructure and surface roughness, concluded that input process parameters play a significant role in the minimization of kerf. Alikbari[12] optimized the process parameters using taguchi method in rotary electric discharge machining of X210CrNi12 alloy material with three copper electrodes: electrode without hole , electrode with one concentric hole and electrode with to symmetric eccentric hole. The tool hole numbers increases MRR, SR, and EWR increases due to the area of the electrode was decreased, and the discharged energy had a higher density and better flushing. From the above literature study several researches have been research done on the EDM process on various materials but not electrical discharge machining of 17-4 Precipitation Hardening Stainless Steel machined with copper tungsten electrode. In this paper, a EDM process is performed with four controllable process parameters peak current, pulse on time, pulse off time and tool lift time, while machining of the Hardening Stainless steel with copper tungsten tool electrode. Then, the machining conditions have been optimized for high performance machining using the Taguchi method because of its simplicity and it gives a systematic approach to optimize the process parameters. 2. Taguchi’s Signal to Noise (S/N) ratio: Taguchi design method is to identify the parameter settings which render the quality of the product or process robust to unavoidable variations in external noise. The relative “quality” of a particular parameter design is evaluated using a generic Signal-to-Noise (S/N) ratio. Depending on the particular design problem, different S/N ratios are applicable, including “lower is better” (LB), “nominal is best” (NB), or “higher is better” (HB). S/N ration can be calculated as a logarithmic transformation of loss function and the characteristics selected for MRR and SR are “higher is better” and “Lower is better” as given in equations 1 and 2 respectively. The experimental values and their corresponding S/N ration values for MRR and SR are shown in table 2.
5060
Chandramouli S et al./ Materials Today: Proceedings 5 (2018) 5058–5067
= −10
∑
... (1)
= −10
∑
... (2)
3. Experimental Methodology An L27 orthogonal array was used for experimentation and the input levels and number of experiments is decided based on the design of experiments. Input process parameters and their levels are presented in Table.1. At the end of the each experiment, the work piece and tool were removed, washed, dried, and weighed using the digital electronic weighing balance 1mg accuracy. The experiments are conducted for a machining time of the 10 minutes. Machining was done with straight polarity and EDM oil Grade 30 used as the dielectric fluid. Gap voltage is 30 V and flushing pressure maintained constant. To achieve validity and accuracy each test is repeated three replications. The input levels and number of experiments are decided based on the design of experiments. The work piece used for the experiments is 17-4 PH steel used in aerospace and die manufacturing. The work piece is in the form of a rectangular plate with dimensions of 60mm X 50mm X 5 mm. Experiments were conducted using Copper Tungsten (80W:20Cu grade) which had good electrical conductivity, high wear resistance. Due to this tool wear minimum while machining with copper Tungsten. Table.1 Process parameters and their levels S.NO. Process parameters Symbol
Level 1
Level 2
Level 3
1
Discharge current (A)
9
12
15
2
Pulse on time (µs)
B
50
100
200
3
Pulse off time (µs)
C
20
50
100
4
Lift time (µs)
D
10
20
50
A
4. Influence of process parameters on performance measures Pulse current: The effect of pulse current on MRR shows that as the pulse current increases MRR, increase is due to enhancement of spark energy that facilitates the action of melting and vaporization. This action results in advancing the impulsive force in the spark gap and thereby increasing the MRR. The surface roughness parameter increases with the increase in current rate due to increases in MRR Pulse on time: In the present study MRR decreases with increase in pulse on time, due to the amount of energy generated at high pulse on time not utilizing for removing the metal. At high values of pulse on current, instead of sparking in the inter electrode gap arcing observed. The surface roughness increases with decrease of pulse on time due to arcing between tool and work instead of sparking. Pulse off time: MRR increases with increase in pulse off time since sufficient time to flushing the eroded particles from gap between tool and work piece. The surface roughness increases with increase of pulse off time due to increase in MRR. Lift time: MRR decreases with increase in lift time, due to decrease in actual time of machining the work piece. the surface roughness increases with increase of tool lift. 5. Analysis of variance (ANOVA) The analysis of variance (ANOVA) is a common statistical technique to determine the percent contribution of each factor for results of the experiment. It calculates parameters known as sum of squares SS, degree of freedom (DOF), variance and percentage of each factor. The Sum of Squares is a measure of the deviation of the experimental data from the mean value of the data. The Fisher’s ratio is also called F value. F value is defined as the ratio of Mean square for the term to Mean square for the error term all these statistical calculations are done in MINI TAB 17.0 software. Response Tables for SN Ratios MRR and SR shown in Tables 3 and 4 respectively. Pulse current, pulse on time, pulse of time and pulse off time is assigned as rank 2, 1, 4 and 3 respectively according to
Chandramouli S et al./ Materials Today: Proceedings 5 (2018) 5058–5067
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their larger value of delta. Rank 1 means highest contribution factor and 4 means lowest contribution factor for both MRR and SR. The main effects plot for SN ratios and means are presented in Fig. 1 and Fig. 2 respectively. The result of ANOVA presented in Table6. From the ANOVA test, it is concluded that the pulse on time is the most significant EDM process parameter for affecting the MRR characteristics due to its highest percentage contribution pulse on time (77.30%) amongst the selected process parameters. The percentage contribution of other parameters in decreasing order is Discharge current (13.28%), lift time (1.52%) and pulse off time (0.52%). The percentage contribution of parameters interactions are I*Ton (4.41), I*Toff ((1.08) and I*Lift (0.02).Table 6 shows the results of ANOVA for the surface roughness. The highest percentage contribution pulse on time (83.55%) amongst the selected process parameters, the percentage of contribution of other parameters is the order of Discharge current (7.5%), lift time (0.75%) and pulse off time (0.507%). The percentage contribution of parameters interactions are I*Ton (0.66), I*Toff (3.79) and I*Lift (1.1).Therefore based on ANOVA results and from Fig. 1 and Fig. 2 the optimal process parameters are A3B1C3D1 for maximization of MRR and A1B3C1D1 for minimization of surface roughness for factors A, B, C and D, respectively. The optimal parameter settings of process parameters are presented in table 7. From Fig. 3 and Fig.5 there is no interaction between of current and pulse on time, current and lift time for MRR but from Fig. 4 slight interaction between current and pulse off time at 100 µs for MRR . The interaction effects of parameters for surface roughness are shown in Fig. 4, Fig. 5 and Fig. 6. It is observed that parameters interaction between current and pulse off time, current and Lift time for surface roughness as shown in below Fig 7 and Fig 8 , Fig 9. 6. Confirmation test After obtaining the optimal level of the EDM process parameters, the next step is to verify the percentage change of MRR and SR between initial settings and for this optimal combination. Table 8 compares the results of the confirmation experiments using the optimal EDM process parameters. As shown in Table 8, the total mean of MRR improved from 122.6 to 134.19 mg/min (an improvement of 8.63%) for optimal machining parameters of A3B1C3D1 and decrease of surface roughness from 9.78 to 2.89 μm (a decrement of 70.4%) for machining parameters of A1B3C1D1, which shows that optimal combination of the EDM process parameters are good enough to Maximization of MRR and minimization of SR of the machined surface. Table 2. L27 Machining Orthogonal array with the values of response variables S/N ratio
SR Ra
dB
µm
68.73
36.74
6.88
-16.75
20
66.96
36.51
7.63
-17.65
50
70.72
36.98
7.20
-17.15
20
20
24.37
27.74
7.45
-17.45
100
50
50
25.3
28.06
8.07
-18.14
100
100
10
23.7
27.51
5.61
-14.97
9
200
20
50
2.53
8.06
3.46
-10.78
9
200
50
10
6.51
16.26
3.97
-11.97
9
9
200
100
20
5.04
14.05
3.71
-11.38
10
12
50
20
20
47.66
33.57
7.31
-17.30
11
12
50
50
50
26.70
28.53
7.93
-17.98
12
12
50
100
10
94.07
39.47
8.01
-18.07
13
12
100
20
50
33.93
30.62
8.08
-18.15
14
12
100
50
10
29.81
29.48
6.68
-16.49
15
12
100
100
20
31.32
29.91
6.92
-16.80
S.No
current
Pulse on time
Pulse off time
Lift time
1
9
50
20
10
2
9
50
50
3
9
50
100
4
9
100
5
9
6
9
7 8
MRR mg/min
S/N ratio dB
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Chandramouli S et al./ Materials Today: Proceedings 5 (2018) 5058–5067 16
12
200
20
10
9.54
19.59
4.35
-12.78
17
12
200
50
20
11.12
20.82
4.17
-12.41
18
12
200
100
50
10.13
20.11
3.53
-10.97
19
15
50
20
50
122.6
41.76
9.78
-19.81
20
15
50
50
10
189.27
45.54
9.19
-19.27
21
15
50
100
20
162.8
44.23
10.54
-20.45
22
15
100
20
10
54.96
34.81
7.23
-17.19
23
15
100
50
20
45.27
33.12
8.24
-18.33
24
15
100
100
50
39.77
31.21
10.12
-20.11
25
15
200
20
20
15.42
23.75
3.58
-11.10
26
15
200
50
50
14.03
22.94
4.89
-13.79
27
15
200
100
10
16.03
24.09
4.86
-13.74
Table 3. Response Table for S/N Larger is better for MRR Level
I
Ton
Toff
Lift
1
25.77
38.15
28.51
30.39
2
28.01
30.36
29.03
29.30
3
33.58
18.85
29.82
27.67
Delta
7.81
19.30
1.30
2.71
Rank
2
1
4
3
Table 4. Response Table for S/N Smaller is better for SR Level I Ton
Toff
Lift
1
-15.14
-18.27
-15.70
-15.69
2
-15.66
-17.51
-16.22
-15.87
3
-17.09
-12.10
-15.96
-16.32
Delta
1.95
6.17
0.52
0.62
Rank
2
1
4
3
Table 5. Analysis of Variance for MRR Parameters DF Sum of squares
Mean square
F -Value #
Contribution %
I
2
291.42
145.712
19.83
Ton
2
1696.14
848.068
115.43#
13.28 77.30
Toff
2
7.74
3.868
0.53
0.35
Lift
2
33.55
16.77
2.28
1.52 #
I *Ton
4
96.86
24.215
3.30
I* Toff
4
23.79
5.946
0.81
1.08
I *Lift
4
0.5
0.126
0.02
0.02
Residual Error
6
44.08
7.347
Total
26
2194.07
#
Significant at 95% confidence level, F0.05, 2, 26= 3.3
4.41
2 100
Chandramouli S et al./ Materials Today: Proceedings 5 (2018) 5058–5067 Table 6. Analysis of Variance for SR Parameters DF Sum of squares
#
Mean square
F -Value #
5063
Contribution %
I
2
18.324
9.162
10.86
Ton
2
203.907
101.954
120.8#
7.50 83.55
Toff
2
1.239
0.620
0.73
0.507
Lift
2
1.854
0.927
1.1
0.75
I *Ton
4
1.632
0.408
0.48
0.66
I *Toff
4
9.261
2.314
2.74
3.79
I* Lift
4
2.765
0.691
0.82
1.1
Residual Error
6
5.064
0.844
Total
26
244.045
2.07 100
Significant at 95% confidence level, F0.05, 2, 26= 3.37 Table 7. Optimal parameter settings of process parameters Output parameters Optimal combination Level I T on T off Max. MRR A3B1C3D1 15 50 100 Min. SR A1B3C1D1 9 200 20
MRR(mg/min) SR(µm)
Initial setting A3B2C1D3 S/N ratio Mean 41.76 122.6 9.78 9.78
Lift 10 10
Table 8. Results of Confirmation Test Predicted value for optimal Experimental value for parameters optimal parameters S/N ratio Mean S/N ratio Mean 42.57 46.07 142.4 134.19 -10.75 -10.12 3.21 2.89
Fig. 1 Main effects plot for SN ratios of MRR
% Improvement 8.63 70.4
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Fig. 2 Main effects plot for SN ratios of SR
Fig. 3 Interaction plot of current and pulse on time for MRR
Fig. 4 Interaction plot of current and pulse off time for MRR
Chandramouli S et al./ Materials Today: Proceedings 5 (2018) 5058–5067
Fig. 5 Interaction plot of current and Lift time for MRR
Fig. 6 Interaction plot of current and pulse on time for SR
Fig. 7 Interaction plot of current and pulse off time for SR
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Fig. 8 Interaction plot of current and Lift time for SR
Fig. 9 SEM images of EDM for optimal parameters Maximum MRR and Minimum SR
7. Conclusion This paper investigates the optimal input process parameters for machining of 17-4 PH steel material with copper tungsten electrode on EDM using Taguchi method by considering the individual and interaction effects of input parameters. The parameters pulse on time and discharge current have shown significant effect on both MRR and SR, and parameter pulse off time has shown least significant effect compared to other parameters. There is no interaction effect of input parameters for MRR but slight interaction between parameters for surface roughness. The optimal combination of input parameters and their levels for the Maximization of MRR of the EDM process are A3B1C3D1and for minimization of surface roughness are A1B3C1D1. The result of ANOVA reveals that pulse on time has highest percentage contribution for MRR (58.3%) and for SR (76.7%). The confirmation experiments were conducted to verify the optimal machining parameters and there is a significant improvement in MRR and SR from initial machining parameter to the optimal machining parameters is about 8.63% and 70.4% respectively.
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