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Study of machining characteristics of Inconel 601 with cryogenic cooled electrode in EDM using RSM
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ScienceDirect Materials Today: Proceedings 5 (2018) 24277–24286
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IConAMMA_2017
Study of machining characteristics of Inconel 601 with cryogenic cooled electrode in EDM using RSM Neelesh Singh, B.C. Routara*, R.K. Nayak School of Mechanical Engineering, KIIT University, Bhubaneswar, Odisha 751024, India
Abstract In today’s world different types of materials and alloys are being discovered which are difficult to machine or work with conventional machining process. Inconel is one of such material which has wide range applications in different fields such as fabricating combustion chambers, making trays, baskets, fixtures etc used in heat treatment. The main advantage of Inconel is that it is lightweight and has high strength and is resistant to oxidation at a very high temperature. In this paper, we will investigate the effect of cryogenically cooled copper electrode with Inconel 601 as workpiece in Electric Discharge Machine (EDM) process. Input parameters taken are gap voltage, peak current and pulse on time and output parameters taken are material removal rate, tool wear rate and surface roughness. The experimental work is done using Box-Behnken Design in Design of Experiment (DOE). Response Surface Methodology (RSM) and Analysis of Variance (ANOVA) is used to get the optimize result for the output variables with varying different input variables. The paper will tell us about the effect of cryogenic cooled electrode on Inconel 601 on EDM. © 2018 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Advances in Materials and Manufacturing Applications [IConAMMA 2017]. Keywords:EDM; Inconel 601; Cryogenic Cooling; RSM; ANOVA; DOE.
1. Introduction Electric Discharge Machine (EDM) is an important unconventional machining process which removes material by erosion process. EDM is used to machine those materials which are difficult to machine with other conventional machining processes. EDM is used to cut delicate shapes with ease. In EDM, tool and workpiece don’t make physical contact with each other. It is mainly used in aerospace and automobile industry.
* Corresponding author. Tel.: +91-9438317760. E-mail address: [email protected] 2214-7853© 2018 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Advances in Materials and Manufacturing Applications [IConAMMA 2017].
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In working of EDM, when power is supplied then a voltage is generated around the workpiece and the tool electrode. This led to breakdown of dielectric medium present in between the workpiece and the tool due to growth of strong electrostatic field. Further, electrons are emitted from cathode to anode which strikes the dielectric molecule and break down the molecule into positive and negative ions. The constant repeating electric discharges lead to removal of material from the tool and the workpiece. The removed material is removed during pulse off time from the gap with the help of flushing pressure and new dielectric takes its place and the process is continued. Cryogenic cooling is generally derived from Greek word ‘Kryos’ meaning cold or frost and ‘genic’ meaning to produce. So cryogenic cooling works are simply those works which are done at a very low temperature. There is no specific temperature fixed for cryogenic cooling but the U.S. National Institute of Standards and Technology has decided the temperature as -180ºC or 93.15K. Cryogenic cooling is generally done to reduce the wear rate of the material so that any material can sustain for a longer time. Cryogenic cooling is used for different purposes such as for frozing foods and transporting it to different places, frozing rare blood groups which are not easily available etc. Inconel 601 is an austenite nickel chromium based super alloy with a high content of chromium, niobium and iron. The high strength of Inconel is generally due to the presence of Ni3Nb. Some of the main characteristics of Inconel is that it has high strength and is resistant to carburisation, oxidation etc. The composition of Inconel 601 is Ni 61%, Cr 23%, Al 1.4%, Si 1.0, Mn 1.0%, C 0.1%, S 0.015% and rest is iron. Few important works which are done on cryogenic cooling of electrode or workpiece, Inconel 601 and EDM are shown. Rahul et al. [3] worked on different types of Inconel materials such as Inconel 601, Inconel 718, Inconel 825 and Inconel 625 with various input parameters and it was found that the optimal parameter for different materials using 5-factor 4-level L16 orthogonal array. For Inconel 601, the most appropriate parameter is when flushing pressure = 0.3 bar, duty factor = 80%, peak current = 7 A, pulse on time = 500 µs and gap voltage as 80 V. Liqing et al. [9] investigate the effect of cryogenically cooled workpiece and oxygen mixed to improve surface integrity and material removal rate. It was found that when cryogenic treatment is used then the material removal rate increases by 30-50% and surface roughness is improved by 1-10%. Mathai et al. [10] studied the effect of cryogenic treatment of electrode on EDM and found that it mainly affects the tool wear. There is not much effect of cryogenic cooling on material removal rate and surface roughness. It was also found that there is increase in surface of the material which is cryogenically cooled. Yildiz et al. [14, 15] worked for cryogenic cooling using liquid nitrogen and found that tool wear reduces and surface finish is improved as working temperature of machining is kept in control. Gopalkannan et al. [2] worked and developed a mathematical model to check the effect on material removal rate, tool wear rate and surface roughness in EDM on MMC using RSM. Different observations are recorded like pulse on time and current directly effects the material removal rate and surface roughness. It was noted that surface roughness decreases when voltage is increased up to but later surface roughness starts decreasing when voltage is increased further above 50 V. Singh et al. [13] studied the effect of deep cryogenic treatment on tool wear rate and material removal rate with EN-31 as workpiece by taking three different electrodes namely brass, copper and graphite. It was seen that tool wear rate decreases by 18%, 9% and 31% and material removal rate increases by 173%, 109% and 230% in copper, brass and graphite respectively. Pandey et al. [11] studied the effect ultrasonic assisted cryogenically cooled copper electrode while machining on EDM with M2 grade HSS as the workpiece material and found that the tool life increases along with improvement in shape retention ability and good surface integrity was seen. Mohanty et al. [1] compares the different electrodes namely copper, brass and graphite while working on Inconel 718. The output parameters taken for the variables are material removal rate, tool wear rate, surface roughness and radial overcut. Different electrodes show different results for different parameters. Material removal rate is high for graphite tool but surface roughness is affected due to high discharge energy. It was also seen that brass gives better surface quality but material removal rate is low for brass. Tool wear rate produced by graphite tool is low as compared to copper and brass tool. Kumar et al. [9] investigate the effect of cryogenic treated copper electrode with some additive powder mixed in dielectric for Inconel 718. It was found that tool wear rate and wear ratio decreases.
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Jafferson et al. [7] work with cryogenically treated and normal electrodes and compare its effects on micro EDM. It was seen that electrical conductivity and hardness of tool has improved and tool wear rate decreases by 35% for copper, 51% for brass and 58% for graphite. Rajesha et al. [4, 12] worked on Inconel 718 with different input parameters namely pulse current, gap voltage, duty factor, flushing pressure and sensitivity factor on EDM and found that pulse current is the most dominating factor for MRR as MRR increases from 14.4 to 22.6 mm3/min. Surface roughness is mainly affected by duty cycle and flushing pressure as the lowest surface roughness achieved is around 6.2 µm. Agarwal et al. [6] worked on Inconel 718 with WEDM using RSM technique. It was seen that pulse on time is the most important factor which affects the surface roughness and cutting rate in Inconel 718. Cutting rate decreases as spark gap voltage and pulse off time is increased. Surface roughness decreases with increase in spark gap voltage. The present work is carried out to check the effect of using cryogenic cooled electrode on machining characteristics of Inconel 601 in electric discharge machining process. Input parameters taken are gap voltage, pulse on time and peak current. Output parameters taken are material removal rate, tool wear rate and surface roughness. 1. Experimental Procedure 1.1 Material Used In the present work, an alloy of nickel-chromium or Inconel 601 is taken as workpiece and electrolytic copper is taken as workpiece as shown in figure 1 and figure 2 respectively. The dimension of workpiece is 52mm x 52mm x 5mm and the electrode is 20 cm in diameter. The electrode is machined and cut into a square shape with dimension of 12.5mm x 12.5mm.
Figure 1. Workpiece Inconel 601
Figure 2. Electrode material (Electrolytic Copper)
1.2 Design of Experiment Box-Behnken Design (BBD) is used to study the effect of process parameters in EDM. The experiments are carried out with three input parameters namely pulse on time (Ton in microsecond, μs),peak current (Ip in Ampere, A) and gap voltage (Vg in Voltage, V). The factors with different levels are shown in table 1 below. Table 1. Process parameters with their respective levels Factors Name Low Level (-1) A Pulse on Time 100 B Gap Voltage 60 C Peak Current 6
Medium Level (0) 150 70 8
High Level (1) 200 80 10
Inconel 601 is machined with copper electrode in electric discharge machine setup up to a depth of 1.5 mm with a constant flushing pressure of 8 kg/cm2. The time for machining each workpiece is recorded and is used to calculate the material removal rate and tool wear rate later on. MITUTOYO SURFACE roughness tester is used for measuring the surface roughness of the workpiece after machining. Later, material removal rate (MRR) and tool wear rate (TWR) are calculated by the given equation (i) and (ii) respectively.
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MRR
Wi W f t
gm / min
(i)
where, Wi is the weight of workpiece before machining (in gram), Wf is the weight of workpiece after machining (in gram) and t is the machining time (in minutes).
TWR
Wi W f t
gm / min
(ii)
where, Wi is the initial weight of workpiece (in gram), Wf is the final weight of workpiece (in gram) and t is the machining time (in minutes). The experimental response is shown in Table 2. Table 2. Experimental Response in EDM for Inconel 601 Sl. No. Pulse on Time Gap Voltage Peak Current 1. 100 60 8 2. 200 60 8 3. 100 80 8 4. 200 80 8 5. 100 70 6 6. 200 70 6 7. 100 70 10 8. 200 70 10 9. 150 60 6 10. 150 80 6 11. 150 60 10 12. 150 80 10 13. 150 70 8 14. 150 70 8 15. 150 70 8
MRR
TWR
Ra
8.3012 9.9037 5.0601 4.5463 4.0479 4.5132 10.83 11.3231 4.9845 3.1222 14.2844 7.4872 8.1556 8.123 8.1341
0.053713 0.015509 0.026937 0.038372 0.003561 0.009664 0.088443 0.065432 0.01412 0.013588 0.107068 0.05433 0.048379 0.042844 0.044124
8.6825 5.279 6.305 5.307 6.434 3.746 7.943 5.607 5.411 5.472 6.664 5.606 7.738 7.152 6.9321
2. Results and Discussions The result obtained through the set of experiments is to be analysed for ensuring the fitness of model. This is done by doing a significance test. The test for goodness of fit and lack of fit is also done. MINITAB 17 is used to analyse the experimental data and get the best possible result and analysis of variance is done to sum up the above test. 2.1 Response Surface Methodology (RSM) Response surface methodology is a collection of mathematical and statistical technique for empirical model building. RSM is mainly used to get the optimize result for different inputs. RSM is generally used to find the significance of several input parameters on one or more output parameters. With the help of regression analysis and design of experiment, a response for independent input parameters can be found. In RSM, the independent input parameters can be shown quantitatively by:
y f x1 , x 2 , x 3 ,..., x n
where, denote the error in response y and surface expressed by f(x1,x2,…,xn) is known as response surface. The response can be seen by graphical method in the contour plots or by 3-dimensional space that will help to anticipate shape of response surface. The suitability of response surface methodology is determined with the approximation of f. In first order model, lack of fit is formed due to the interactions between variables and surface curvature. To improve the optimization process second order model is used. An ordinary second order model is given by:
Neelesh Singh / Materials Today: Proceedings 5 (2018) 24277–24286 n
n
n
i 1
i 1
i j
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f a 0 a i x i a ii x i2 a ii x i x j where, aii denotes the quadratic effect of xi, ai denotes the linear effect of xi and aij denotes the line to line interaction between xi and xj and xi and xj are the design variables. This quadratic model allows to locate the region of optimality besides investigating entire factor space. The important data for response surface models is collected with the help of design of experiments, with the adoption of Box-Behnken Design. The final response equations are as follows:
MRR 8.1376 2.5589 10 1 Ton 2.15726Vg 3.40710I p 4.879 10 1 Ton2 6.969 10 1Vg2 2.88 10 2 I p2 5.291 10 1 TonVg 6.9 10 3 Ton I p 1.2337Vg I p TWR 4.512 10 2 5.46 103Ton 7.15 10 3Vg 3.429 10 2 I p 8.49 103Ton2 2.99 10 3Vg2 5.15 10 3 I p2 1.241 10 2 TonVg 7.28 103 Ton I p 1.305 10 2Vg I p Ra 7.272 1.178Ton 4.1810 1Vg 5.9510 1 I p 3.68 10 1Ton2 5.1210 1Vg2 9.7310 1 I p2 6.0110 1TonVg 8.8 10 2 Ton I p 2.810 1Vg I p 2.2 Analysis of Variance (ANOVA) The analysis of variance for material removal rate is given in Table 3. F-value is used to check the significance of values. The probability of F-value which exceeds the calculated F-value due to the noise is given by the Pvalue in the table. If P value is more than 0.05 then the term is insignificant and lacks fit. An insignificant terms is required as it indicates the left out term not significant and hence the develop model fits together. The value of coefficient of determination (R2) and adj.R2 are found to be 1.0000 and 0.9999 respectively. A normality test is further checking, by plotting the results. It is clear from the Figure 3 that the residuals are normally distributed as they fall on a straight line. The plots of residual versus fit denotes the variance to be constant and linearity is maintained. The histogram data shows that the data is not skewed. Also, the systematic effect of order or time is found to be in good agreement in the residual versus order plot. Table 3. ANOVA table for material removal rate (MRR) Source DF Adj SS Model
Adj MS
F-Value
P-Value 0.000
9
140.371
15.5967
20514.08
Linear
3
130.621
43.5402
57267.58
0.000
Ton
1
0.524
0.5238
688.98
0.000
Vg
1
37.230
37.2300
48967.92
0.000
Ip
1
92.867
92.8668
122145.85
0.000
Square
3
2.542
0.8472
1114.34
0.000
Ton * Ton
1
0.879
0.8789
1155.98
0.000
Vg * Vg
1
1.793
1.7931
2358.41
0.000
Ip * Ip
1
0.003
0.0031
4.04
0.101
2-Way Interaction
3
7.208
2.4028
3160.33
0.000
Ton * Vg
1
1.120
1.1197
1472.76
0.000
Ton * Ip
1
0.000
0.0002
0.25
0.636
Vg * Ip
1
6.088
6.0884
8007.96
0.000
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5
0.004
0.0008
Lack-of-Fit
3
0.003
0.0011
Pure Error
2
0.001
0.0003
Total
14
140.375
3.94
0.209
Figure 3. Residual plots for Material Removal Rate
The analysis of variance for tool wear rate is given in Table 4. From the table, the coefficient of determination (R2) and adj. R2 are found to be 0.9724 and 0.9229 respectively. Table 4. ANOVA table for tool wear rate (TWR) Source DF
Adj SS
Adj MS
F-Value
P-Value
Model
9
0.011984
0.001332
19.61
0.002
Linear
3
0.010055
0.003352
49.36
0.000
Ton
1
0.000238
0.000238
3.51
0.120
Vg
1
0.000409
0.000409
6.02
0.058
Ip
1
0.009408
0.009408
138.55
0.000
Square
3
0.000420
0.000140
2.06
0.224
Ton * Ton
1
0.000266
0.000266
3.92
0.105
Vg * Vg
1
0.000033
0.49
0.517
Ip * Ip
1
0.000098
0.000098
1.44
0.283
2-Way Interaction
3
0.001509
0.000503
7.41
0.027
Ton * Vg
1
0.000616
0.000616
9.07
0.030
Ton * Ip
1
0.000212
0.000212
3.12
0.138
10.03
0.025
12.81
0.073
0.000033
Vg * Ip
1
0.000681
0.000681
Error
5
0.000340
0.000068
Lack-of-Fit
3
0.000323
0.000108
Pure Error
2
0.000017
0.000008
Total
14
0.012324
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The analysis of variance for surface roughness is given in Table 5. From the table, the coefficient of determination (R2) and adj. (R2) are found to be 0.9509 and 0.8624 respectively.
Figure 4. Residual plots for Tool Wear Rate Table 5. ANOVA table for surface roughness Source
DF
Adj SS
Model
9
21.5898
Linear
3
15.3335
Ton
1
Vg
Adj MS
F-Value
P-Value
2.3989
10.75
0.009
5.1112
22.91
0.002
11.1050
11.1050
49.77
0.001
1
1.3999
1.399
6.27
0.054
Ip
1
2.8286
2.8286
12.68
0.016
Square
3
4.4656
1.4885
6.67
0.034
Ton * Ton
1
0.5006
0.5006
2.24
0.194
Vg * Vg
1
0.9696
0.9696
4.35
0.092
Ip * Ip
1
3.4980
3.4980
15.68
0.011
2-Way Interaction
3
1.7906
0.5969
2.67
0.158
Ton * Vg
1
1.4466
1.4466
6.48
0.052
Ton * Ip
1
0.0310
0.0310
0.14
0.725
1.40
0.289
1.48
0.428
Vg * Ip
1
0.3130
0.3130
Error
5
1.1157
0.2231
Lack-of-Fit
3
0.7686
0.2562 0.1735
Pure Error
2
0.3471
Total
14
22.7055
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Figure 5. Residual plots for surface roughness
2.3 Surface Plots Surface plot for material removal rate is shown in Figure 6. From the graph, it can be seen that maximum material removal rate of 14.28 mm3/min is achieved when pulse on time is 150 µs, gap voltage is 60 V and peak current is 10 A. It can be seen that material removal rate increase with increase in peak current and decrease with increase in gap voltage.
Figure 6. Response Plot for Vg and Ip for Material Removal Rate
Figure 7. Response Plot for Vg and Ip for Tool Wear Rate
Surface plot for tool wear rate is shown in Figure 7. From the graph, it is found that minimum tool wear rate of 0.003561 mm3/min is achieved when pulse on time is 100µs, gap voltage is 70 V and peak current is 6 A. It can be seen that tool wear rate increase with increase in current. Surface plot for surface roughness of workpiece is shown in figure 8. The lowest surface roughness of 3.746µm is seen in the graph when pulse on time is 200 µs, gap voltage is 70 V and peak current is 6 A. surface roughness increases as current is increased and decreases when pulse on time is increased.
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Figure 8. Response Plot for Ip and Ton for Surface Roughness
2.4 Multi Objective Optimization of Responses Multi objective optimization of response is used to find out the best input setting for which we can get the best result for the output parameters. Here, we are looking for input which gives us maximum material removal rate, minimum tool wear rate and minimum surface roughness. Figure 9 shows the optimization plot. From the figure, we can see the best condition is when pulse on time is 200 µs, gap voltage is 60 V and peak current is at 7.737 A.
Figure 9. Response Optimization Plot
3. Conclusion Results from the experiment carried out in the work are as such that the ANOVA results are in good agreement with the reprised models. Current is the most important factor for all the three output responses namely material removal rate, tool wear rate and surface roughness. Cryogenic cooling improves the material removal rate and surface roughness of the workpiece. Cryogenic cooling also reduces the tool wear rate up to 33% as that compared to normal electrode.
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Acknowledgements The researchers would like to thank KIIT University, Bhubaneswar for their help in conducting experiments and co-operation for this research work. References [1] C.P. Mohanty, S.S. Mahapatra, M.R. Singh, An intelligent approach to optimize the EDM process parameters using utility concept and QPSO alghoritm, Engg. Sci. and Tech., an Int. Journal (2015). [2] S. Gopalakannan, T. Senthilvelan, S. Ranganathan, Modeling and optimization of EDM process parameters on machining of Al 7075-B4C MMC using RSM, Procedia Engg. 38 (2012) 685-690. [3] Rahul, K. Abhishek, S. Datta, B.B. Biswal, S.S. Mahapatra, Machining performance optimization for electrodischarge-machining of Inconel 601, 625, 718 and 825: an integrated optimization route combining satisfaction function, fuzzy interference system and Taguchi approach, J. Braz. Soc. Mech. Sci. Eng. (2016) 1-29. [4] S. Rajesha, A.K. Sharma, P. Kumar, On electric discharge machining of Inconel 718 with hollow tool, J. of Mat. Engg. And Performance 21(6) (2012) 882-891. [5] R.H. Myers, D.C. Montgomery, G.G. Vining, C.M. Borror, S.M. Kowalski, Response surface methodology: a retrospective and literature survey, J. quality Tech. 36(1) (2004) 53. [6] V. Agarwal, S.S. Khangura, R.K. Garg, Parametric modeling and optimization for wire electric discharge machining of Inconel 718 using response surface methodology, Int. J. Adv. Manuf. Tech. 79(1) (2015) 31-47. [7] Jafferson, J. M., and P. Hariharan. "Machining performance of cryogenically treated electrodes in microelectric discharge machining: a comparative experimental study." Materials and Manufacturing Processes 28, no. 4 (2013): 397-402. [8] Jain, Vijay Kumar. Advanced machining processes. Allied publishers, 2009. [9] Liqing, L., and S. Yingjie. "Study of dry EDM with oxygen-mixed and cryogenic cooling approaches." Procedia CIRP 6 (2013): 344-350. [10] Mathai, V. J., R. V. Vaghela, H. K. Dave, H. K. Raval, and K. P. Desai. "Study of the Effect of Cryogenic Treatment of Tool Electrodes during Electro Discharge Machining." 2013. [11] Pandey, Anand, and Shankar Singh. "Current research trends in variants of Electrical Discharge Machining: A review." International Journal of Engineering Science and Technology 2, no. 6 (2010): 2172-2191. [12] Rajesha, S., A. K. Sharma, and Pradeep Kumar. "On electro discharge machining of Inconel 718 with hollow tool." Journal of materials engineering and performance 21, no. 6 (2012): 882-891. [13] Singh Amandeep, Kanth Grover, Neel. "Wear Properties of Cryogenic Treated Electrodes on Machining Of En31." Materials Today: Proceedings 2, no. 4-5 (2015): 1406-1413. [14] Yildiz, Y., M. M. Sundaram, K. P. Rajurkar, and M. Nalbant. "The effects of cold and cryogenic treatments on the machinability of beryllium-copper alloy in electro discharge machining." (2011). [15] Yildiz, Yakup, and Muammer Nalbant. "A review of cryogenic cooling in machining processes." International Journal of Machine Tools and Manufacture 48, no. 9 (2008): 947-964. [16]Kumar, Anil, Sachin Maheshwari, Chitra Sharma, and Naveen Beri. "Machining efficiency evaluation of cryogenically treated copper electrode in additive mixed EDM." Materials and Manufacturing Processes 27, no. 10 (2012): 1051-1058.
ScienceDirect Materials Today: Proceedings 5 (2018) 24277–24286
www.materialstoday.com/proceedings
IConAMMA_2017
Study of machining characteristics of Inconel 601 with cryogenic cooled electrode in EDM using RSM Neelesh Singh, B.C. Routara*, R.K. Nayak School of Mechanical Engineering, KIIT University, Bhubaneswar, Odisha 751024, India
Abstract In today’s world different types of materials and alloys are being discovered which are difficult to machine or work with conventional machining process. Inconel is one of such material which has wide range applications in different fields such as fabricating combustion chambers, making trays, baskets, fixtures etc used in heat treatment. The main advantage of Inconel is that it is lightweight and has high strength and is resistant to oxidation at a very high temperature. In this paper, we will investigate the effect of cryogenically cooled copper electrode with Inconel 601 as workpiece in Electric Discharge Machine (EDM) process. Input parameters taken are gap voltage, peak current and pulse on time and output parameters taken are material removal rate, tool wear rate and surface roughness. The experimental work is done using Box-Behnken Design in Design of Experiment (DOE). Response Surface Methodology (RSM) and Analysis of Variance (ANOVA) is used to get the optimize result for the output variables with varying different input variables. The paper will tell us about the effect of cryogenic cooled electrode on Inconel 601 on EDM. © 2018 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Advances in Materials and Manufacturing Applications [IConAMMA 2017]. Keywords:EDM; Inconel 601; Cryogenic Cooling; RSM; ANOVA; DOE.
1. Introduction Electric Discharge Machine (EDM) is an important unconventional machining process which removes material by erosion process. EDM is used to machine those materials which are difficult to machine with other conventional machining processes. EDM is used to cut delicate shapes with ease. In EDM, tool and workpiece don’t make physical contact with each other. It is mainly used in aerospace and automobile industry.
* Corresponding author. Tel.: +91-9438317760. E-mail address: [email protected] 2214-7853© 2018 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Advances in Materials and Manufacturing Applications [IConAMMA 2017].
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In working of EDM, when power is supplied then a voltage is generated around the workpiece and the tool electrode. This led to breakdown of dielectric medium present in between the workpiece and the tool due to growth of strong electrostatic field. Further, electrons are emitted from cathode to anode which strikes the dielectric molecule and break down the molecule into positive and negative ions. The constant repeating electric discharges lead to removal of material from the tool and the workpiece. The removed material is removed during pulse off time from the gap with the help of flushing pressure and new dielectric takes its place and the process is continued. Cryogenic cooling is generally derived from Greek word ‘Kryos’ meaning cold or frost and ‘genic’ meaning to produce. So cryogenic cooling works are simply those works which are done at a very low temperature. There is no specific temperature fixed for cryogenic cooling but the U.S. National Institute of Standards and Technology has decided the temperature as -180ºC or 93.15K. Cryogenic cooling is generally done to reduce the wear rate of the material so that any material can sustain for a longer time. Cryogenic cooling is used for different purposes such as for frozing foods and transporting it to different places, frozing rare blood groups which are not easily available etc. Inconel 601 is an austenite nickel chromium based super alloy with a high content of chromium, niobium and iron. The high strength of Inconel is generally due to the presence of Ni3Nb. Some of the main characteristics of Inconel is that it has high strength and is resistant to carburisation, oxidation etc. The composition of Inconel 601 is Ni 61%, Cr 23%, Al 1.4%, Si 1.0, Mn 1.0%, C 0.1%, S 0.015% and rest is iron. Few important works which are done on cryogenic cooling of electrode or workpiece, Inconel 601 and EDM are shown. Rahul et al. [3] worked on different types of Inconel materials such as Inconel 601, Inconel 718, Inconel 825 and Inconel 625 with various input parameters and it was found that the optimal parameter for different materials using 5-factor 4-level L16 orthogonal array. For Inconel 601, the most appropriate parameter is when flushing pressure = 0.3 bar, duty factor = 80%, peak current = 7 A, pulse on time = 500 µs and gap voltage as 80 V. Liqing et al. [9] investigate the effect of cryogenically cooled workpiece and oxygen mixed to improve surface integrity and material removal rate. It was found that when cryogenic treatment is used then the material removal rate increases by 30-50% and surface roughness is improved by 1-10%. Mathai et al. [10] studied the effect of cryogenic treatment of electrode on EDM and found that it mainly affects the tool wear. There is not much effect of cryogenic cooling on material removal rate and surface roughness. It was also found that there is increase in surface of the material which is cryogenically cooled. Yildiz et al. [14, 15] worked for cryogenic cooling using liquid nitrogen and found that tool wear reduces and surface finish is improved as working temperature of machining is kept in control. Gopalkannan et al. [2] worked and developed a mathematical model to check the effect on material removal rate, tool wear rate and surface roughness in EDM on MMC using RSM. Different observations are recorded like pulse on time and current directly effects the material removal rate and surface roughness. It was noted that surface roughness decreases when voltage is increased up to but later surface roughness starts decreasing when voltage is increased further above 50 V. Singh et al. [13] studied the effect of deep cryogenic treatment on tool wear rate and material removal rate with EN-31 as workpiece by taking three different electrodes namely brass, copper and graphite. It was seen that tool wear rate decreases by 18%, 9% and 31% and material removal rate increases by 173%, 109% and 230% in copper, brass and graphite respectively. Pandey et al. [11] studied the effect ultrasonic assisted cryogenically cooled copper electrode while machining on EDM with M2 grade HSS as the workpiece material and found that the tool life increases along with improvement in shape retention ability and good surface integrity was seen. Mohanty et al. [1] compares the different electrodes namely copper, brass and graphite while working on Inconel 718. The output parameters taken for the variables are material removal rate, tool wear rate, surface roughness and radial overcut. Different electrodes show different results for different parameters. Material removal rate is high for graphite tool but surface roughness is affected due to high discharge energy. It was also seen that brass gives better surface quality but material removal rate is low for brass. Tool wear rate produced by graphite tool is low as compared to copper and brass tool. Kumar et al. [9] investigate the effect of cryogenic treated copper electrode with some additive powder mixed in dielectric for Inconel 718. It was found that tool wear rate and wear ratio decreases.
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Jafferson et al. [7] work with cryogenically treated and normal electrodes and compare its effects on micro EDM. It was seen that electrical conductivity and hardness of tool has improved and tool wear rate decreases by 35% for copper, 51% for brass and 58% for graphite. Rajesha et al. [4, 12] worked on Inconel 718 with different input parameters namely pulse current, gap voltage, duty factor, flushing pressure and sensitivity factor on EDM and found that pulse current is the most dominating factor for MRR as MRR increases from 14.4 to 22.6 mm3/min. Surface roughness is mainly affected by duty cycle and flushing pressure as the lowest surface roughness achieved is around 6.2 µm. Agarwal et al. [6] worked on Inconel 718 with WEDM using RSM technique. It was seen that pulse on time is the most important factor which affects the surface roughness and cutting rate in Inconel 718. Cutting rate decreases as spark gap voltage and pulse off time is increased. Surface roughness decreases with increase in spark gap voltage. The present work is carried out to check the effect of using cryogenic cooled electrode on machining characteristics of Inconel 601 in electric discharge machining process. Input parameters taken are gap voltage, pulse on time and peak current. Output parameters taken are material removal rate, tool wear rate and surface roughness. 1. Experimental Procedure 1.1 Material Used In the present work, an alloy of nickel-chromium or Inconel 601 is taken as workpiece and electrolytic copper is taken as workpiece as shown in figure 1 and figure 2 respectively. The dimension of workpiece is 52mm x 52mm x 5mm and the electrode is 20 cm in diameter. The electrode is machined and cut into a square shape with dimension of 12.5mm x 12.5mm.
Figure 1. Workpiece Inconel 601
Figure 2. Electrode material (Electrolytic Copper)
1.2 Design of Experiment Box-Behnken Design (BBD) is used to study the effect of process parameters in EDM. The experiments are carried out with three input parameters namely pulse on time (Ton in microsecond, μs),peak current (Ip in Ampere, A) and gap voltage (Vg in Voltage, V). The factors with different levels are shown in table 1 below. Table 1. Process parameters with their respective levels Factors Name Low Level (-1) A Pulse on Time 100 B Gap Voltage 60 C Peak Current 6
Medium Level (0) 150 70 8
High Level (1) 200 80 10
Inconel 601 is machined with copper electrode in electric discharge machine setup up to a depth of 1.5 mm with a constant flushing pressure of 8 kg/cm2. The time for machining each workpiece is recorded and is used to calculate the material removal rate and tool wear rate later on. MITUTOYO SURFACE roughness tester is used for measuring the surface roughness of the workpiece after machining. Later, material removal rate (MRR) and tool wear rate (TWR) are calculated by the given equation (i) and (ii) respectively.
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MRR
Wi W f t
gm / min
(i)
where, Wi is the weight of workpiece before machining (in gram), Wf is the weight of workpiece after machining (in gram) and t is the machining time (in minutes).
TWR
Wi W f t
gm / min
(ii)
where, Wi is the initial weight of workpiece (in gram), Wf is the final weight of workpiece (in gram) and t is the machining time (in minutes). The experimental response is shown in Table 2. Table 2. Experimental Response in EDM for Inconel 601 Sl. No. Pulse on Time Gap Voltage Peak Current 1. 100 60 8 2. 200 60 8 3. 100 80 8 4. 200 80 8 5. 100 70 6 6. 200 70 6 7. 100 70 10 8. 200 70 10 9. 150 60 6 10. 150 80 6 11. 150 60 10 12. 150 80 10 13. 150 70 8 14. 150 70 8 15. 150 70 8
MRR
TWR
Ra
8.3012 9.9037 5.0601 4.5463 4.0479 4.5132 10.83 11.3231 4.9845 3.1222 14.2844 7.4872 8.1556 8.123 8.1341
0.053713 0.015509 0.026937 0.038372 0.003561 0.009664 0.088443 0.065432 0.01412 0.013588 0.107068 0.05433 0.048379 0.042844 0.044124
8.6825 5.279 6.305 5.307 6.434 3.746 7.943 5.607 5.411 5.472 6.664 5.606 7.738 7.152 6.9321
2. Results and Discussions The result obtained through the set of experiments is to be analysed for ensuring the fitness of model. This is done by doing a significance test. The test for goodness of fit and lack of fit is also done. MINITAB 17 is used to analyse the experimental data and get the best possible result and analysis of variance is done to sum up the above test. 2.1 Response Surface Methodology (RSM) Response surface methodology is a collection of mathematical and statistical technique for empirical model building. RSM is mainly used to get the optimize result for different inputs. RSM is generally used to find the significance of several input parameters on one or more output parameters. With the help of regression analysis and design of experiment, a response for independent input parameters can be found. In RSM, the independent input parameters can be shown quantitatively by:
y f x1 , x 2 , x 3 ,..., x n
where, denote the error in response y and surface expressed by f(x1,x2,…,xn) is known as response surface. The response can be seen by graphical method in the contour plots or by 3-dimensional space that will help to anticipate shape of response surface. The suitability of response surface methodology is determined with the approximation of f. In first order model, lack of fit is formed due to the interactions between variables and surface curvature. To improve the optimization process second order model is used. An ordinary second order model is given by:
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n
n
i 1
i 1
i j
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f a 0 a i x i a ii x i2 a ii x i x j where, aii denotes the quadratic effect of xi, ai denotes the linear effect of xi and aij denotes the line to line interaction between xi and xj and xi and xj are the design variables. This quadratic model allows to locate the region of optimality besides investigating entire factor space. The important data for response surface models is collected with the help of design of experiments, with the adoption of Box-Behnken Design. The final response equations are as follows:
MRR 8.1376 2.5589 10 1 Ton 2.15726Vg 3.40710I p 4.879 10 1 Ton2 6.969 10 1Vg2 2.88 10 2 I p2 5.291 10 1 TonVg 6.9 10 3 Ton I p 1.2337Vg I p TWR 4.512 10 2 5.46 103Ton 7.15 10 3Vg 3.429 10 2 I p 8.49 103Ton2 2.99 10 3Vg2 5.15 10 3 I p2 1.241 10 2 TonVg 7.28 103 Ton I p 1.305 10 2Vg I p Ra 7.272 1.178Ton 4.1810 1Vg 5.9510 1 I p 3.68 10 1Ton2 5.1210 1Vg2 9.7310 1 I p2 6.0110 1TonVg 8.8 10 2 Ton I p 2.810 1Vg I p 2.2 Analysis of Variance (ANOVA) The analysis of variance for material removal rate is given in Table 3. F-value is used to check the significance of values. The probability of F-value which exceeds the calculated F-value due to the noise is given by the Pvalue in the table. If P value is more than 0.05 then the term is insignificant and lacks fit. An insignificant terms is required as it indicates the left out term not significant and hence the develop model fits together. The value of coefficient of determination (R2) and adj.R2 are found to be 1.0000 and 0.9999 respectively. A normality test is further checking, by plotting the results. It is clear from the Figure 3 that the residuals are normally distributed as they fall on a straight line. The plots of residual versus fit denotes the variance to be constant and linearity is maintained. The histogram data shows that the data is not skewed. Also, the systematic effect of order or time is found to be in good agreement in the residual versus order plot. Table 3. ANOVA table for material removal rate (MRR) Source DF Adj SS Model
Adj MS
F-Value
P-Value 0.000
9
140.371
15.5967
20514.08
Linear
3
130.621
43.5402
57267.58
0.000
Ton
1
0.524
0.5238
688.98
0.000
Vg
1
37.230
37.2300
48967.92
0.000
Ip
1
92.867
92.8668
122145.85
0.000
Square
3
2.542
0.8472
1114.34
0.000
Ton * Ton
1
0.879
0.8789
1155.98
0.000
Vg * Vg
1
1.793
1.7931
2358.41
0.000
Ip * Ip
1
0.003
0.0031
4.04
0.101
2-Way Interaction
3
7.208
2.4028
3160.33
0.000
Ton * Vg
1
1.120
1.1197
1472.76
0.000
Ton * Ip
1
0.000
0.0002
0.25
0.636
Vg * Ip
1
6.088
6.0884
8007.96
0.000
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5
0.004
0.0008
Lack-of-Fit
3
0.003
0.0011
Pure Error
2
0.001
0.0003
Total
14
140.375
3.94
0.209
Figure 3. Residual plots for Material Removal Rate
The analysis of variance for tool wear rate is given in Table 4. From the table, the coefficient of determination (R2) and adj. R2 are found to be 0.9724 and 0.9229 respectively. Table 4. ANOVA table for tool wear rate (TWR) Source DF
Adj SS
Adj MS
F-Value
P-Value
Model
9
0.011984
0.001332
19.61
0.002
Linear
3
0.010055
0.003352
49.36
0.000
Ton
1
0.000238
0.000238
3.51
0.120
Vg
1
0.000409
0.000409
6.02
0.058
Ip
1
0.009408
0.009408
138.55
0.000
Square
3
0.000420
0.000140
2.06
0.224
Ton * Ton
1
0.000266
0.000266
3.92
0.105
Vg * Vg
1
0.000033
0.49
0.517
Ip * Ip
1
0.000098
0.000098
1.44
0.283
2-Way Interaction
3
0.001509
0.000503
7.41
0.027
Ton * Vg
1
0.000616
0.000616
9.07
0.030
Ton * Ip
1
0.000212
0.000212
3.12
0.138
10.03
0.025
12.81
0.073
0.000033
Vg * Ip
1
0.000681
0.000681
Error
5
0.000340
0.000068
Lack-of-Fit
3
0.000323
0.000108
Pure Error
2
0.000017
0.000008
Total
14
0.012324
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The analysis of variance for surface roughness is given in Table 5. From the table, the coefficient of determination (R2) and adj. (R2) are found to be 0.9509 and 0.8624 respectively.
Figure 4. Residual plots for Tool Wear Rate Table 5. ANOVA table for surface roughness Source
DF
Adj SS
Model
9
21.5898
Linear
3
15.3335
Ton
1
Vg
Adj MS
F-Value
P-Value
2.3989
10.75
0.009
5.1112
22.91
0.002
11.1050
11.1050
49.77
0.001
1
1.3999
1.399
6.27
0.054
Ip
1
2.8286
2.8286
12.68
0.016
Square
3
4.4656
1.4885
6.67
0.034
Ton * Ton
1
0.5006
0.5006
2.24
0.194
Vg * Vg
1
0.9696
0.9696
4.35
0.092
Ip * Ip
1
3.4980
3.4980
15.68
0.011
2-Way Interaction
3
1.7906
0.5969
2.67
0.158
Ton * Vg
1
1.4466
1.4466
6.48
0.052
Ton * Ip
1
0.0310
0.0310
0.14
0.725
1.40
0.289
1.48
0.428
Vg * Ip
1
0.3130
0.3130
Error
5
1.1157
0.2231
Lack-of-Fit
3
0.7686
0.2562 0.1735
Pure Error
2
0.3471
Total
14
22.7055
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Figure 5. Residual plots for surface roughness
2.3 Surface Plots Surface plot for material removal rate is shown in Figure 6. From the graph, it can be seen that maximum material removal rate of 14.28 mm3/min is achieved when pulse on time is 150 µs, gap voltage is 60 V and peak current is 10 A. It can be seen that material removal rate increase with increase in peak current and decrease with increase in gap voltage.
Figure 6. Response Plot for Vg and Ip for Material Removal Rate
Figure 7. Response Plot for Vg and Ip for Tool Wear Rate
Surface plot for tool wear rate is shown in Figure 7. From the graph, it is found that minimum tool wear rate of 0.003561 mm3/min is achieved when pulse on time is 100µs, gap voltage is 70 V and peak current is 6 A. It can be seen that tool wear rate increase with increase in current. Surface plot for surface roughness of workpiece is shown in figure 8. The lowest surface roughness of 3.746µm is seen in the graph when pulse on time is 200 µs, gap voltage is 70 V and peak current is 6 A. surface roughness increases as current is increased and decreases when pulse on time is increased.
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Figure 8. Response Plot for Ip and Ton for Surface Roughness
2.4 Multi Objective Optimization of Responses Multi objective optimization of response is used to find out the best input setting for which we can get the best result for the output parameters. Here, we are looking for input which gives us maximum material removal rate, minimum tool wear rate and minimum surface roughness. Figure 9 shows the optimization plot. From the figure, we can see the best condition is when pulse on time is 200 µs, gap voltage is 60 V and peak current is at 7.737 A.
Figure 9. Response Optimization Plot
3. Conclusion Results from the experiment carried out in the work are as such that the ANOVA results are in good agreement with the reprised models. Current is the most important factor for all the three output responses namely material removal rate, tool wear rate and surface roughness. Cryogenic cooling improves the material removal rate and surface roughness of the workpiece. Cryogenic cooling also reduces the tool wear rate up to 33% as that compared to normal electrode.
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Acknowledgements The researchers would like to thank KIIT University, Bhubaneswar for their help in conducting experiments and co-operation for this research work. References [1] C.P. Mohanty, S.S. Mahapatra, M.R. Singh, An intelligent approach to optimize the EDM process parameters using utility concept and QPSO alghoritm, Engg. Sci. and Tech., an Int. Journal (2015). [2] S. Gopalakannan, T. Senthilvelan, S. Ranganathan, Modeling and optimization of EDM process parameters on machining of Al 7075-B4C MMC using RSM, Procedia Engg. 38 (2012) 685-690. [3] Rahul, K. Abhishek, S. Datta, B.B. Biswal, S.S. Mahapatra, Machining performance optimization for electrodischarge-machining of Inconel 601, 625, 718 and 825: an integrated optimization route combining satisfaction function, fuzzy interference system and Taguchi approach, J. Braz. Soc. Mech. Sci. Eng. (2016) 1-29. [4] S. Rajesha, A.K. Sharma, P. Kumar, On electric discharge machining of Inconel 718 with hollow tool, J. of Mat. Engg. And Performance 21(6) (2012) 882-891. [5] R.H. Myers, D.C. Montgomery, G.G. Vining, C.M. Borror, S.M. Kowalski, Response surface methodology: a retrospective and literature survey, J. quality Tech. 36(1) (2004) 53. [6] V. Agarwal, S.S. Khangura, R.K. Garg, Parametric modeling and optimization for wire electric discharge machining of Inconel 718 using response surface methodology, Int. J. Adv. Manuf. Tech. 79(1) (2015) 31-47. [7] Jafferson, J. M., and P. Hariharan. "Machining performance of cryogenically treated electrodes in microelectric discharge machining: a comparative experimental study." Materials and Manufacturing Processes 28, no. 4 (2013): 397-402. [8] Jain, Vijay Kumar. Advanced machining processes. Allied publishers, 2009. [9] Liqing, L., and S. Yingjie. "Study of dry EDM with oxygen-mixed and cryogenic cooling approaches." Procedia CIRP 6 (2013): 344-350. [10] Mathai, V. J., R. V. Vaghela, H. K. Dave, H. K. Raval, and K. P. Desai. "Study of the Effect of Cryogenic Treatment of Tool Electrodes during Electro Discharge Machining." 2013. [11] Pandey, Anand, and Shankar Singh. "Current research trends in variants of Electrical Discharge Machining: A review." International Journal of Engineering Science and Technology 2, no. 6 (2010): 2172-2191. [12] Rajesha, S., A. K. Sharma, and Pradeep Kumar. "On electro discharge machining of Inconel 718 with hollow tool." Journal of materials engineering and performance 21, no. 6 (2012): 882-891. [13] Singh Amandeep, Kanth Grover, Neel. "Wear Properties of Cryogenic Treated Electrodes on Machining Of En31." Materials Today: Proceedings 2, no. 4-5 (2015): 1406-1413. [14] Yildiz, Y., M. M. Sundaram, K. P. Rajurkar, and M. Nalbant. "The effects of cold and cryogenic treatments on the machinability of beryllium-copper alloy in electro discharge machining." (2011). [15] Yildiz, Yakup, and Muammer Nalbant. "A review of cryogenic cooling in machining processes." International Journal of Machine Tools and Manufacture 48, no. 9 (2008): 947-964. [16]Kumar, Anil, Sachin Maheshwari, Chitra Sharma, and Naveen Beri. "Machining efficiency evaluation of cryogenically treated copper electrode in additive mixed EDM." Materials and Manufacturing Processes 27, no. 10 (2012): 1051-1058.