- Email: [email protected]
Effect and Optimization of Machine Process Parameters on MRR for EN19 & EN41 Materials Using Taguchi
Available online at www.sciencedirect.com
ScienceDirect Procedia Technology 14 (2014) 204 – 210
2nd International Conference on Innovations in Automation and Mechatronics Engineering, ICIAME 2014
Effect and Optimization of Machine Process Parameters on MRR for EN19 & EN41 materials using Taguchi Vikasa, Shashikanta, A.K.Royb and Kaushik Kumarb* b
a Research Scholar, Department of Mechanical Engineering, Birla Institute of Technology, Mesra, Ranchi, 835215, India Associate Professor, Department of Mechanical Engineering, Birla Institute of Technology, Mesra, Ranchi, 835215, India
Abstract
The present work deals with the comparison of the MRR for EN19 and EN41 material in a die sinking EDM machine. The various input factors like Pulse ON time, Pulse OFF time, Discharge current and voltage were considered as the input processing parameters, while the MRR is considered as the output. Optimization using Taguchi method was performed to predict the best combination of inputs towards maximum output. A comparison was done to obtain the effect of these input parameters over the MRR for both the material, and simultaneously the impact of the carbon percentage over the MRR was investigated. It was found that the Discharge current in case of the EN41 material and EN19 material had a larger impact as compare to other processing parameters on the MRR. A relative study of the carbon composition for both the material was also done. © 2014 Elsevier Ltd. This is an open access article under the CC BY-NC-ND license © 2014 The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/3.0/). Selection and/or peer-review under responsibility of the Organizing Committee of ICIAME 2014. Peer-review under responsibility of the Organizing Committee of ICIAME 2014. Keywords: EN19, EN41, EDM, MRR,Taguchi, Design of Experiments, Optimization
* Corresponding author. Tel.: +91-9431597463. E-mail address: [email protected]
1. Introduction In an EDM process, choosing the correct parameter for finding out the Optimized value of MRR is very important. Different input parameters like Pulse-ON time, Pulse-OFF time, Discharge current and Voltage affects the MRR for both EN19 and EN41 material in a different manner. Apart from these input parameters, there are many other parameters, which affect the MRR differently. They may be flushing pressure, feed rate, etc. A lot of work has been carried out in this field for the optimization of the MRR with different materials and in the presence of different
2212-0173 © 2014 Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Peer-review under responsibility of the Organizing Committee of ICIAME 2014. doi:10.1016/j.protcy.2014.08.027
Vikas et al. / Procedia Technology 14 (2014) 204 – 210
methods. Vikas et al (2013) carried out the optimization of the MRR for EN41 material based on the 4 input parameters like the pulse on time, pulse off time, discharge current and gap voltage. He found out that the current along with the pulse-off time had a larger impact over the MRR followed by some of the interaction plot, while the affect of the other parameters were negligible. Kamal Hassan et al (2012) carried out the same optimization technique using Taguchi to optimize the MRR for medium brass alloy in CNC turning machine. He used the various input parameters like cutting speed, depth of cut, feed, etc to optimize the MRR. He found that the cutting speed and the feed rate had significant effect over the MRR followed by their interaction. He found that the cutting speed and the feed rate had direct effect over the MRR, as they increased directly with them. Similarly, Kuldeep ojha et al (2010) and AKM Asif Iqbal et al (2010) also carried out the improvement of the MRR on the different input parameters. Many researcher (Bhaduri et al (2009), Belgassim and Abusada (2012), Ikram et al (2013), Natrajan and Arunachalam (2011), Tiwari (2013), Sanchez et al (2002), Singh et al (2004), Kurnia et al (2008), Jahan et al (2009), and Antony (2001)) have worked with different materials and on different non conventional machines using different parameters to obtain most optimized value in order to economize the outputs.
Nomenclature C1 C2 C3 C4 A B C D
Pulse-On time for EN19 material Pulse-Off time for EN19 material Discharge Current for EN19 material Voltage for EN19 material Pulse-On time for EN41 material Pulse-Off time for EN41 material Discharge Current for EN41 material Voltage for EN41 material
2. Experimental Setup The entire experiment was carried on a Die sinking EDM machine (Electronica EMT-43 Machine). Experiment was performed individually for both the materials one after another. The work-piece on which the EDM process was carried out was EN19 and EN41 material of cylindrical cross-VHFWLRQ RI GLPHQVLRQ ij PP ; PP ZKLOH WKH Tool for EDM operation was a rectangular positive polarity Copper of dimension 25x25mm. Paraffin oil was selected as the dielectric medium. First of all the initial weight of the work-piece was taken before carrying out the EDM operation. The final weight was then compared with the initial weight and the difference of the two weights yield the MRR for the EN material. The experiment was carried out according to the design of experiment table generated by the taguchi design of experiments. For each set of experiments, the value of all the 4-input parameters were made constant and accordingly, the material removal was carried out in the EDM machine. The process was repeated for all the 27 experiments. The percentage composition of the different elements were obtained by Energy Dispersive X-Ray Spectroscopy (EDX) (JSM 63901v, Resolution=3nm at 30kV at high vaccum mode and 4nm at 40 kV low vaccum mode). The numbers of levels are selected by dividing the total span of available values of each of the input parameters in three parts namely Lower level, medium level and the upper level. The Taguchi design of experiments is constructed on the basis of the same. A set of 4-input values of A, B, C, D, C1, C2, C3 and C4 were considered and depicted in the table below:
205
206
Vikas et al. / Procedia Technology 14 (2014) 204 – 210 Table I: Design factor along with their levels:
Variable
Coding for EN41
Pulse ON Time(Ton) ȝ6
A
Coding for EN19
Level
1
2
3
200
300
400
C1
Pulse OFF time (Toff) ȝ6
B
2300
2200
2100
C2
Discharge current (Ip) (Amp)
C
8
16
24
C3
Gap voltage (V) (Volt)
D
40
60
80
C4
Table II: Chemical composition of EN 41 Tool Steel
Element
App Conc.
Intensity Corrn.
Weight%
Weight% Sigma
Atomic%
CK
2.36
0.5005
9.02
2.23
22.97
OK
13.86
1.2359
21.48
1.44
41.07
Cr K
1.01
1.1053
1.76
0.43
1.03
Fe K
29.95
0.9322
61.46
2.07
33.67
Eu L
2.95
0.8966
6.29
1.30
1.27
TOTAL
100
Table III: Chemical composition of EN 19 Tool Steel
Element
App Conc.
Intensity Corrn.
Weight%
Weight% Sigma
Atomic%
CK
2.45
0.5073
13.67
3.03
30.75
OK
9.25
1.1469
22.79
1.48
38.50
Fe K
20.58
0.9142
63.54
2.49
30.75
TOTAL
100
3. Result and Discussion : 3.1 Taguchi Method: The Taguchi method of optimization is a 3-step process (Jameson (2001), Montgomery (2001)), which deals with the selection of raw material at the first stage, based on the engineering properties of that material. At the 2nd stage, the optimization process is carried out on the basis of the design of experiment table. The 3rd stage is the stage, where the comparison between the experimental and the predicted values are done to validate the result. On the basis of the different combinations of inputs obtained by the Taguchi method, the corresponding S/N ratio was generated for both the materials individually by the use of Minitab 16 software (Minitab Manual 2010). Table IV: Experimental Results:
207
Vikas et al. / Procedia Technology 14 (2014) 204 – 210
A (TON)
B (TOFF)
C (IP)
D (V)
EN41
EN19
MRR (gm/min)
S/N Ratio(dB)
MRR (gm/min)
S/N Ratio(dB)
1 1
1 1
1 2
1 2
7.222222 12.05263
17.17342 21.62164
5.1956 9.6296
14.3127 19.6722
1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3
1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3
3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3
3 2 3 1 3 1 2 2 3 1 3 1 2 1 2 3 3 1 2 1 2 3 2 3 1
16.53125 7.380952 14.1 31 7.827586 24.9 31.96 5.569767 11.18182 24.63889 6.090909 20.2766 27.82759 11.30435 21.0625 29 4.693878 15.9322 23.24 9.142857 17.25532 23.72727 8.949153 17.43137 41.73333
24.36611 17.36225 22.98438 29.82723 17.87256 27.92399 30.09214 14.91674 20.97025 27.83242 15.69364 26.1399 28.88951 21.06491 26.4702 29.24796 13.43064 24.04551 27.32472 19.22164 24.73846 27.50496 19.03564 24.82663 32.40966
13.7146 4.5641 11.6657 26.3242 4.4659 21.4438 28.4438 2.4438 7.4437 20.2521 3.6310 16.5551 21.2836 16.4579 18.3849 24.4432 2.2377 11.4052 19.4559 6.4533 12.4428 18.8812 4.3619 13.4438 35.4438
22.7437 13.1871 21.3383 28.4071 12.9982 26.6260 29.0797 7.7611 17.4358 26.1294 11.2004 24.3787 26.5609 24.3275 25.2892 27.7632 6.9962 21.1421 25.7810 16.1957 21.8983 25.5206 12.7935 22.5704 30.9908
After the generation of the above table, the Response table for Mean S/N ratio for both the materials were obtained, on the basis of which the corresponding the rank of the different parameters were used to find the level of importance towards affecting the MRR. Table V: Response table for Mean S/N ratio for EN41: Level 1 2 3 Delta Rank
A (TON) 23.25 23.47 23.62 0.37 4
B (TOFF) 21.3 23.6 25.44 4.14 2
C (IP) 17.31 24.41 28.61 11.3 1
D (V) 25.07 23.38 21.88 3.19 3
Mean (A+B+C+D)/4 21.7325 23.715 24.8875
Table VI: Response table for Mean S/N ratio for EN19: Level 1 2 3 Delta Rank
C1 (TON) 13.939 14.544 13.792 0.752 4
C2 (TOFF) 18.543 13.533 10.198 8.346 2
C3 (IP) 5.535 13.602 23.138 17.603 1
C4 (V) 17.726 13.446 11.103 6.623 3
Mean (A+B+C+D)/4 13.93575 13.78125 14.5575
208
Vikas et al. / Procedia Technology 14 (2014) 204 – 210
The graphs were then plotted on the basis of the response table so obtained. From the graph, the optimized value for EN41 material was obtained at A3B3C3D1; While, the optimal condition for EN19 material were found out at [C1]3 [C2]1 [C3]3 [C4]1.
Fig1: S/N plot for EN41 and EN19 material
The ANOVA table, also called the Analysis of Variance table was then generated using the Minitab software. The ANOVA table is the table showing the importance of the different parameters towards the MRR. The entire result was confirmed at 95% confidence level and in both the cases, it was found out that Current followed by the Pulse-off time and voltage had larger impact over the MRR. However, the Pulse-ON time and the interaction of parameters did not have any impact over the MRR. Table VII: ANOVA Results for EN41: Source
DOF
A
2
B
2
C
2
Seq SS
Adj SS
Adj MS
F
4.66
4.66
2.33
0.66
296.96
296.96
148.48
41.78*
1831.31
1831.31
915.65
257.64* 24.23*
D
2
172.23
172.23
86.12
A*B
4
17.08
17.08
4.27
12
A*C
4
11.73
11.73
2.93
0.82 4.51
B*C
4
64.10
64.10
16.02
Error
6
21.32
21.32
3.55
Total
26
2419.39
Significant at 95% confidence level(*F0.05,2,6=19.33)
Table VIII: ANOVA Results for EN19: Source C1 C2 C3 C4 C1*C2 C1*C3 C2*C3 Error Total
DOF
Seq SS
Adj SS
Adj MS
2 2.861 2.861 1.431 2 317.625 317.625 148.48 2 1397.705 1397.705 915.65 2 203 203 101.500 4 11.774 11.774 2.944 4 33.698 33.698 8.424 4 31.715 31.715 7.929 6 27.532 27.532 4.589 26 2025.911 Significant at 95% confidence level(*F0.05,2,6=19.33)
F 0.31 34.61* 152.3* 22.12* 0.64 1.84 1.73
209
Vikas et al. / Procedia Technology 14 (2014) 204 – 210
4. CONFIRMATION TABLE : A confirmation table related with both the material was obtained and the differences between the experimental and predicted values were calculated. Both these values were found very close to one another. It was found out that the MRR for EN41 and EN19 material was mostly influenced by the Current followed by the pulse-off time. The experimental values were calculated by the formula: ߛ ൌ ߛ σ ୀ ሺߛ ଵ ݅ െ ߛ ݉ ሻ
(1)
Where, Ȗ is the optimal level of process parameter , Ȗ i is the mean value of S/N ratio and Ȗ M is the total mean S/N ratio and O is the number of main design parameter. Table IX: Confirmations Test: Parameter
Mean Parameter for EN41 MEAN
S/N ratio
Optimal Parameter for EN41 Mean Parameter for EN19 PREDECTION
EXPERIMENTA L
A2B2C2D A3B3C3D1 2 28.74 32.23 Improvement of S/N ratio=3.67dB
32.41
Optimal Parameter for EN19 PREDICTION
EXPERIMENTAL
[C1]2[C2]2[C3 [C1]3[C2]1[C3]3 ]2[C4]2 [C4]1 14.3127 18.8267 28.042 Improvement of S/N ratio= 4.5 dB
CONCLUSION: MRR plays a very important role in the manufacturing domain as it decides on the time and ultimately cost. In this work using various values of Current, voltage, pulse on time and pulse off time, considered as input process parameters, Taguchi method was utilized for maximization of MRR of two different materials namely EN19 and EN41. The optimal process parameters for maximum MRR for EN19 were current 24 amps, voltage 40 V, pulse on time 400 μs and pulse off time 2300 μs whereas the same for EN41 are 24 amps, 40V, 400 μs and 2100 μs respectively. The predicted and measured value from confirmation test was compared by checking the variation in the percentage error. The variation percentage error was found within 1%. The optimum value obtained from the analysis also showed a good agreement with that of experimental value. It was also observed from the experimental data for both EN19 and EN41 that the MRR values in general for any particular combination of input parameters was higher for EN41 than in case of EN19. This was due to the fact that the carbon percentages, as depicted in table II and III, decreases from EN19 to EN41 which increases the ease of material removal rate and hence the machining. References [1]. Adeel Ikram , Nadeem Ahmad Mufti, Muhammad Qaiser Saleem and Ahmed Raza Khan: Parametric optimization for surface roughness, kerf and MRR in wire electrical discharge machining (WEDM) using Taguchi design of experiment , Journal of Mechanical Science and Technology(2013), DOI 10.1007/s12206-013-0526-8 [2]. A.K.M. Asif Iqbal and Ahsan Ali Khan. Modeling and Analysis of MRR, EWR and Surface Roughness in EDM Milling through Response Surface Methodology. American J. of Engineering and Applied Sciences 3 (4): 611-619, 2010 [3]. D. Bhaduri, A. S. Kuar, S. Sarkar, S. K. Biswas and S. Mitra: Electro Discharge Machining of Titanium Nitride-Aluminium Oxide Composite for Optimum Process Criterial Yield, Materials and Manufacturing Processes,Volume 24, Issue 12, 2009, DOI:10.1080/10426910902996987. [4]. E. C. Jameson: Book on Electrical discharge machining: tooling, methods and applications, 2001 edition [5]. Jahan MP, Wong YS, Rahman M (2009) A study on the quality micro-hole machining of tungsten carbide by micro-EDM processusing transistor and RC-type pulse generator. J Mater Process Technol 209:1706–1716. [6]. Jiju Antony and Friene Jiju Antony Teaching the Taguchi method to industrial engineers, Work Study, Volume 50 . Number 4 . 2001 . pp. 141 – 149 [7]. Kamal Hassan, Anish Kumar, M.P.Garg . International Journal of Engineering Research and Applications (IJERA) ,Vol. 2, Issue 2,Mar-Apr 2012, pp.1581-1590
210
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[8]. Kuldeep Ojha, R. K. Garg, K. K. Singh. MRR Improvement in Sinking Electrical Discharge Machining: A Review Journal of Minerals & Materials Characterization & Engineering, Vol. 9, No.8, pp.709-739, 2010 [9]. Kurnia W, Tan PC, Yeo SH, Wong M (2008) Analytical approximation of the erosion rate and electrode wear in micro electrical discharge machining. J Micromech Microeng 18:085011. [10]. Montgomery, D. C., Design and analysis of experiments , John Wiley, New York, 2001. [11]. Minitab user manual release 16 MINITAB Inc., State College, PA USA, 2010. [12]. N Natrajan and RM Arunachalam: Optimization of Micro-EDM with multiple performance using Taguchi method and Grey relational analysis, Journal of Scientific and Industrial Research, vol 70,July 2011,pp 500-505. [13]. Narender Singh P, Raghukandan K, Rathinasabapathi M, Pai BC (2004) Electric discharge machining of Al–10%SiCP as-cast metal matrix composites. J Mat Process Technol 155–156:1653–1657. [14]. Othman Belgassim and Abdurrahman Abusada: Optimization of the EDM Parameters on the Surface Roughness of AISI D3 Tool Steel, Proceedings of the 2012 International Conference on Industrial Engineering and Operations Management Istanbul, Turkey, July 3 – 6, 2012. [15]. S Tiwari : International Journal of Engineering Research & Technology (IJERT),Vol. 2 Issue 7, July – 2013, IJERT ISSN: 22780181 [16]. Sanchez JA, Lopez de Lacalle DL, Lamikiz A, Bravo U (2002) Dimensional accuracy optimisation of multi-stage planetary EDM. Int J Mach Tools Manuf 42:1643–1648. [17]. Vikas, Apurba Kumar Roy, Kaushik Kumar. Effect and Optimization of Machine Process Parameters on Material Removal Rate in EDM for EN41 Material Using Taguchi. International Journal of Mechanical Engineering and Computer Applications, Vol 1, Issue 5,October 2013, pp- 35-39.
ScienceDirect Procedia Technology 14 (2014) 204 – 210
2nd International Conference on Innovations in Automation and Mechatronics Engineering, ICIAME 2014
Effect and Optimization of Machine Process Parameters on MRR for EN19 & EN41 materials using Taguchi Vikasa, Shashikanta, A.K.Royb and Kaushik Kumarb* b
a Research Scholar, Department of Mechanical Engineering, Birla Institute of Technology, Mesra, Ranchi, 835215, India Associate Professor, Department of Mechanical Engineering, Birla Institute of Technology, Mesra, Ranchi, 835215, India
Abstract
The present work deals with the comparison of the MRR for EN19 and EN41 material in a die sinking EDM machine. The various input factors like Pulse ON time, Pulse OFF time, Discharge current and voltage were considered as the input processing parameters, while the MRR is considered as the output. Optimization using Taguchi method was performed to predict the best combination of inputs towards maximum output. A comparison was done to obtain the effect of these input parameters over the MRR for both the material, and simultaneously the impact of the carbon percentage over the MRR was investigated. It was found that the Discharge current in case of the EN41 material and EN19 material had a larger impact as compare to other processing parameters on the MRR. A relative study of the carbon composition for both the material was also done. © 2014 Elsevier Ltd. This is an open access article under the CC BY-NC-ND license © 2014 The Authors. Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/3.0/). Selection and/or peer-review under responsibility of the Organizing Committee of ICIAME 2014. Peer-review under responsibility of the Organizing Committee of ICIAME 2014. Keywords: EN19, EN41, EDM, MRR,Taguchi, Design of Experiments, Optimization
* Corresponding author. Tel.: +91-9431597463. E-mail address: [email protected]
1. Introduction In an EDM process, choosing the correct parameter for finding out the Optimized value of MRR is very important. Different input parameters like Pulse-ON time, Pulse-OFF time, Discharge current and Voltage affects the MRR for both EN19 and EN41 material in a different manner. Apart from these input parameters, there are many other parameters, which affect the MRR differently. They may be flushing pressure, feed rate, etc. A lot of work has been carried out in this field for the optimization of the MRR with different materials and in the presence of different
2212-0173 © 2014 Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Peer-review under responsibility of the Organizing Committee of ICIAME 2014. doi:10.1016/j.protcy.2014.08.027
Vikas et al. / Procedia Technology 14 (2014) 204 – 210
methods. Vikas et al (2013) carried out the optimization of the MRR for EN41 material based on the 4 input parameters like the pulse on time, pulse off time, discharge current and gap voltage. He found out that the current along with the pulse-off time had a larger impact over the MRR followed by some of the interaction plot, while the affect of the other parameters were negligible. Kamal Hassan et al (2012) carried out the same optimization technique using Taguchi to optimize the MRR for medium brass alloy in CNC turning machine. He used the various input parameters like cutting speed, depth of cut, feed, etc to optimize the MRR. He found that the cutting speed and the feed rate had significant effect over the MRR followed by their interaction. He found that the cutting speed and the feed rate had direct effect over the MRR, as they increased directly with them. Similarly, Kuldeep ojha et al (2010) and AKM Asif Iqbal et al (2010) also carried out the improvement of the MRR on the different input parameters. Many researcher (Bhaduri et al (2009), Belgassim and Abusada (2012), Ikram et al (2013), Natrajan and Arunachalam (2011), Tiwari (2013), Sanchez et al (2002), Singh et al (2004), Kurnia et al (2008), Jahan et al (2009), and Antony (2001)) have worked with different materials and on different non conventional machines using different parameters to obtain most optimized value in order to economize the outputs.
Nomenclature C1 C2 C3 C4 A B C D
Pulse-On time for EN19 material Pulse-Off time for EN19 material Discharge Current for EN19 material Voltage for EN19 material Pulse-On time for EN41 material Pulse-Off time for EN41 material Discharge Current for EN41 material Voltage for EN41 material
2. Experimental Setup The entire experiment was carried on a Die sinking EDM machine (Electronica EMT-43 Machine). Experiment was performed individually for both the materials one after another. The work-piece on which the EDM process was carried out was EN19 and EN41 material of cylindrical cross-VHFWLRQ RI GLPHQVLRQ ij PP ; PP ZKLOH WKH Tool for EDM operation was a rectangular positive polarity Copper of dimension 25x25mm. Paraffin oil was selected as the dielectric medium. First of all the initial weight of the work-piece was taken before carrying out the EDM operation. The final weight was then compared with the initial weight and the difference of the two weights yield the MRR for the EN material. The experiment was carried out according to the design of experiment table generated by the taguchi design of experiments. For each set of experiments, the value of all the 4-input parameters were made constant and accordingly, the material removal was carried out in the EDM machine. The process was repeated for all the 27 experiments. The percentage composition of the different elements were obtained by Energy Dispersive X-Ray Spectroscopy (EDX) (JSM 63901v, Resolution=3nm at 30kV at high vaccum mode and 4nm at 40 kV low vaccum mode). The numbers of levels are selected by dividing the total span of available values of each of the input parameters in three parts namely Lower level, medium level and the upper level. The Taguchi design of experiments is constructed on the basis of the same. A set of 4-input values of A, B, C, D, C1, C2, C3 and C4 were considered and depicted in the table below:
205
206
Vikas et al. / Procedia Technology 14 (2014) 204 – 210 Table I: Design factor along with their levels:
Variable
Coding for EN41
Pulse ON Time(Ton) ȝ6
A
Coding for EN19
Level
1
2
3
200
300
400
C1
Pulse OFF time (Toff) ȝ6
B
2300
2200
2100
C2
Discharge current (Ip) (Amp)
C
8
16
24
C3
Gap voltage (V) (Volt)
D
40
60
80
C4
Table II: Chemical composition of EN 41 Tool Steel
Element
App Conc.
Intensity Corrn.
Weight%
Weight% Sigma
Atomic%
CK
2.36
0.5005
9.02
2.23
22.97
OK
13.86
1.2359
21.48
1.44
41.07
Cr K
1.01
1.1053
1.76
0.43
1.03
Fe K
29.95
0.9322
61.46
2.07
33.67
Eu L
2.95
0.8966
6.29
1.30
1.27
TOTAL
100
Table III: Chemical composition of EN 19 Tool Steel
Element
App Conc.
Intensity Corrn.
Weight%
Weight% Sigma
Atomic%
CK
2.45
0.5073
13.67
3.03
30.75
OK
9.25
1.1469
22.79
1.48
38.50
Fe K
20.58
0.9142
63.54
2.49
30.75
TOTAL
100
3. Result and Discussion : 3.1 Taguchi Method: The Taguchi method of optimization is a 3-step process (Jameson (2001), Montgomery (2001)), which deals with the selection of raw material at the first stage, based on the engineering properties of that material. At the 2nd stage, the optimization process is carried out on the basis of the design of experiment table. The 3rd stage is the stage, where the comparison between the experimental and the predicted values are done to validate the result. On the basis of the different combinations of inputs obtained by the Taguchi method, the corresponding S/N ratio was generated for both the materials individually by the use of Minitab 16 software (Minitab Manual 2010). Table IV: Experimental Results:
207
Vikas et al. / Procedia Technology 14 (2014) 204 – 210
A (TON)
B (TOFF)
C (IP)
D (V)
EN41
EN19
MRR (gm/min)
S/N Ratio(dB)
MRR (gm/min)
S/N Ratio(dB)
1 1
1 1
1 2
1 2
7.222222 12.05263
17.17342 21.62164
5.1956 9.6296
14.3127 19.6722
1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3
1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3 1 1 1 2 2 2 3 3 3
3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3
3 2 3 1 3 1 2 2 3 1 3 1 2 1 2 3 3 1 2 1 2 3 2 3 1
16.53125 7.380952 14.1 31 7.827586 24.9 31.96 5.569767 11.18182 24.63889 6.090909 20.2766 27.82759 11.30435 21.0625 29 4.693878 15.9322 23.24 9.142857 17.25532 23.72727 8.949153 17.43137 41.73333
24.36611 17.36225 22.98438 29.82723 17.87256 27.92399 30.09214 14.91674 20.97025 27.83242 15.69364 26.1399 28.88951 21.06491 26.4702 29.24796 13.43064 24.04551 27.32472 19.22164 24.73846 27.50496 19.03564 24.82663 32.40966
13.7146 4.5641 11.6657 26.3242 4.4659 21.4438 28.4438 2.4438 7.4437 20.2521 3.6310 16.5551 21.2836 16.4579 18.3849 24.4432 2.2377 11.4052 19.4559 6.4533 12.4428 18.8812 4.3619 13.4438 35.4438
22.7437 13.1871 21.3383 28.4071 12.9982 26.6260 29.0797 7.7611 17.4358 26.1294 11.2004 24.3787 26.5609 24.3275 25.2892 27.7632 6.9962 21.1421 25.7810 16.1957 21.8983 25.5206 12.7935 22.5704 30.9908
After the generation of the above table, the Response table for Mean S/N ratio for both the materials were obtained, on the basis of which the corresponding the rank of the different parameters were used to find the level of importance towards affecting the MRR. Table V: Response table for Mean S/N ratio for EN41: Level 1 2 3 Delta Rank
A (TON) 23.25 23.47 23.62 0.37 4
B (TOFF) 21.3 23.6 25.44 4.14 2
C (IP) 17.31 24.41 28.61 11.3 1
D (V) 25.07 23.38 21.88 3.19 3
Mean (A+B+C+D)/4 21.7325 23.715 24.8875
Table VI: Response table for Mean S/N ratio for EN19: Level 1 2 3 Delta Rank
C1 (TON) 13.939 14.544 13.792 0.752 4
C2 (TOFF) 18.543 13.533 10.198 8.346 2
C3 (IP) 5.535 13.602 23.138 17.603 1
C4 (V) 17.726 13.446 11.103 6.623 3
Mean (A+B+C+D)/4 13.93575 13.78125 14.5575
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The graphs were then plotted on the basis of the response table so obtained. From the graph, the optimized value for EN41 material was obtained at A3B3C3D1; While, the optimal condition for EN19 material were found out at [C1]3 [C2]1 [C3]3 [C4]1.
Fig1: S/N plot for EN41 and EN19 material
The ANOVA table, also called the Analysis of Variance table was then generated using the Minitab software. The ANOVA table is the table showing the importance of the different parameters towards the MRR. The entire result was confirmed at 95% confidence level and in both the cases, it was found out that Current followed by the Pulse-off time and voltage had larger impact over the MRR. However, the Pulse-ON time and the interaction of parameters did not have any impact over the MRR. Table VII: ANOVA Results for EN41: Source
DOF
A
2
B
2
C
2
Seq SS
Adj SS
Adj MS
F
4.66
4.66
2.33
0.66
296.96
296.96
148.48
41.78*
1831.31
1831.31
915.65
257.64* 24.23*
D
2
172.23
172.23
86.12
A*B
4
17.08
17.08
4.27
12
A*C
4
11.73
11.73
2.93
0.82 4.51
B*C
4
64.10
64.10
16.02
Error
6
21.32
21.32
3.55
Total
26
2419.39
Significant at 95% confidence level(*F0.05,2,6=19.33)
Table VIII: ANOVA Results for EN19: Source C1 C2 C3 C4 C1*C2 C1*C3 C2*C3 Error Total
DOF
Seq SS
Adj SS
Adj MS
2 2.861 2.861 1.431 2 317.625 317.625 148.48 2 1397.705 1397.705 915.65 2 203 203 101.500 4 11.774 11.774 2.944 4 33.698 33.698 8.424 4 31.715 31.715 7.929 6 27.532 27.532 4.589 26 2025.911 Significant at 95% confidence level(*F0.05,2,6=19.33)
F 0.31 34.61* 152.3* 22.12* 0.64 1.84 1.73
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4. CONFIRMATION TABLE : A confirmation table related with both the material was obtained and the differences between the experimental and predicted values were calculated. Both these values were found very close to one another. It was found out that the MRR for EN41 and EN19 material was mostly influenced by the Current followed by the pulse-off time. The experimental values were calculated by the formula: ߛ ൌ ߛ σ ୀ ሺߛ ଵ ݅ െ ߛ ݉ ሻ
(1)
Where, Ȗ is the optimal level of process parameter , Ȗ i is the mean value of S/N ratio and Ȗ M is the total mean S/N ratio and O is the number of main design parameter. Table IX: Confirmations Test: Parameter
Mean Parameter for EN41 MEAN
S/N ratio
Optimal Parameter for EN41 Mean Parameter for EN19 PREDECTION
EXPERIMENTA L
A2B2C2D A3B3C3D1 2 28.74 32.23 Improvement of S/N ratio=3.67dB
32.41
Optimal Parameter for EN19 PREDICTION
EXPERIMENTAL
[C1]2[C2]2[C3 [C1]3[C2]1[C3]3 ]2[C4]2 [C4]1 14.3127 18.8267 28.042 Improvement of S/N ratio= 4.5 dB
CONCLUSION: MRR plays a very important role in the manufacturing domain as it decides on the time and ultimately cost. In this work using various values of Current, voltage, pulse on time and pulse off time, considered as input process parameters, Taguchi method was utilized for maximization of MRR of two different materials namely EN19 and EN41. The optimal process parameters for maximum MRR for EN19 were current 24 amps, voltage 40 V, pulse on time 400 μs and pulse off time 2300 μs whereas the same for EN41 are 24 amps, 40V, 400 μs and 2100 μs respectively. The predicted and measured value from confirmation test was compared by checking the variation in the percentage error. The variation percentage error was found within 1%. The optimum value obtained from the analysis also showed a good agreement with that of experimental value. It was also observed from the experimental data for both EN19 and EN41 that the MRR values in general for any particular combination of input parameters was higher for EN41 than in case of EN19. This was due to the fact that the carbon percentages, as depicted in table II and III, decreases from EN19 to EN41 which increases the ease of material removal rate and hence the machining. References [1]. Adeel Ikram , Nadeem Ahmad Mufti, Muhammad Qaiser Saleem and Ahmed Raza Khan: Parametric optimization for surface roughness, kerf and MRR in wire electrical discharge machining (WEDM) using Taguchi design of experiment , Journal of Mechanical Science and Technology(2013), DOI 10.1007/s12206-013-0526-8 [2]. A.K.M. Asif Iqbal and Ahsan Ali Khan. Modeling and Analysis of MRR, EWR and Surface Roughness in EDM Milling through Response Surface Methodology. American J. of Engineering and Applied Sciences 3 (4): 611-619, 2010 [3]. D. Bhaduri, A. S. Kuar, S. Sarkar, S. K. Biswas and S. Mitra: Electro Discharge Machining of Titanium Nitride-Aluminium Oxide Composite for Optimum Process Criterial Yield, Materials and Manufacturing Processes,Volume 24, Issue 12, 2009, DOI:10.1080/10426910902996987. [4]. E. C. Jameson: Book on Electrical discharge machining: tooling, methods and applications, 2001 edition [5]. Jahan MP, Wong YS, Rahman M (2009) A study on the quality micro-hole machining of tungsten carbide by micro-EDM processusing transistor and RC-type pulse generator. J Mater Process Technol 209:1706–1716. [6]. Jiju Antony and Friene Jiju Antony Teaching the Taguchi method to industrial engineers, Work Study, Volume 50 . Number 4 . 2001 . pp. 141 – 149 [7]. Kamal Hassan, Anish Kumar, M.P.Garg . International Journal of Engineering Research and Applications (IJERA) ,Vol. 2, Issue 2,Mar-Apr 2012, pp.1581-1590
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