This paper depicts the experimental study the input parameters of EDM i.e. current, pulse on time and pulse off time on output parameters material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR). The workpiece materials selected was AISI D2. The aluminium used as tool electrode and EDM oil as dielectric fluid. Taguchi, method was used to perform experiments, L9 orthogonal array was applied using MINITAB software. Signal to Noise (S/N) ratio and ANOVA were employed for parameter optimization and to achieve max MRR, min SR and TWR. The results indicate that the most prompting factor for MRR is Pulse off time. For TWR, the most influencing factor is current. For SR, the most prompting factor is pulse on time. Optimization is done by using Taguchi method on MINITAB 17 software.
Abstract
This paper depicts the experimental study the input parameters of EDM i.e. current, pulse on time and pulse off time on output parameters material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR). The workpiece materials selected was AISI D2. The aluminium used as tool electrode and EDM oil as dielectric fluid. Taguchi, method was used to perform experiments, L9 orthogonal array was applied using MINITAB software. Signal to Noise (S/N) ratio and ANOVA were employed for parameter optimization and to achieve max MRR, min SR and TWR. The results indicate that the most prompting factor for MRR is Pulse off time. For TWR, the most influencing factor is current. For SR, the most prompting factor is pulse on time. Optimization is done by using Taguchi method on MINITAB 17 software.
Keywords: Electric Discharge Machine, Aluminium Electrode, SS316, AISI D2 steel, MINITAB
1. Introduction
Electric Discharge Machine (EDM) is a non-traditional machining process. It has number of applications in die making, punches and molds industry. It also finds application in manufacturing of finished parts in automobile, also in manufacturing of surgical components. In EDM, electric spark is produced between workpiece and electrode and due to this spark material gets eroded from workpiece and tool electrode 1. So this process can be successfully employed to materials which are electrically conductive. Hardness, shape, toughness and brittleness don’t cause any restrictions 2. In die sinking EDM, the shape which is to be produced on workpiece, the tool should be replica of that shape. Both tool electrode and workpiece are dipped in a dielectric fluid like EDM oil, Kerosene etc. The workpiece and electrode are placed at a very close distance and it depends on operating conditions and called as spark gap 3. Modern era of EDM, i.e. from 1995 to till date. Many new aspects has been developed, namely micro-machining by EDM and dry EDM i.e. EDM without dielectric fluid. Now a days EDM is most acceptable technique for MRR [3]. For micro machining, ultrasonic vibration method is apt, dry machining is economical and water EDM is safe and conductive working environment, Powder mixed EDM is concerned more on improving surface quality 4. The viability of machining Tungsten carbide ceramics by EDM with a graphite electrode using Taguchi method is studied and concluded that the current mainly effects the EWR and SR. The pulse duration is the most influencing factor for MRR 5. For machining of tungsten carbide, graphite electrode gives the maximum MRR with comparison to copper and copper tungsten electrode 6. The study of MRR by copper and graphite electrodes of different diameter is performed on XW42 tool steel. Copper is apt for roughing process, while graphite electrode is apt for finishing 7. Copper, copper alloys and graphite has a problem of low wear resistance. A new composite ZrB2-Cu is developed to get an ideal combination of wear resistance, electrical and thermal conductivity. This composite demonstrates more MRR with less TWR than copper tool 8. When manufacturing deep slots in low machinability materials, EDM may be the only method 9. Investigation is done on SS 316L using compressed air as dielectric and copper electrode as tool 10. MRR is primarily influenced by peak current and EWR is primarily influenced by peak current trailed by pulse on time 11. For MRR, using copper as electrode input current & duty cycle is more dominant. Pulse on time and duty cycle for EWR, duty cycle and pressure for surface roughness 12. EDM parameters using grey relational analysis on AISI SS (202), parameters mainly discharge current, and pulse off time and pulse on time 13.The effect of abrasive mixed dielectric on AISI D3steel is studied. At 6 g/ltr of concentration MRR is maximum. EDM (Abrasive mixed) outcomes in 58% more MRR as compared to traditional EDM 14. Experiments were performed to determine factors effecting SR using brass, copper and aluminium as electrodes on Mild steel 15. Aluminium yielded highest MRR followed by copper.
For present work, studies are conducted on AISI D2 steel using aluminium electrode to determine the MRR, TWR and SR and optimum values are predicted using MINITAB 17.
2. Workpiece and Electrode Material
AISI D2 steel is used mainly in die and mould making industry. Now a days dies are of very complicated shape so to achieve that kind of shape, non-conventional machining methods are employed because it cannot be achieved by conventional methods.
Table 1 – Chemical composition of AISI D2 steel
Abbildung in dieser Leseprobe nicht enthalten
Aluminium is silvery white, soft, non-magnetic, ductile material. Aluminium and its alloys are vital to aerospace industry. Aluminium is capable of super conductivity. Aluminium find its application in electrical transmission lines for the purpose of power transmission. Melting point of aluminium is 933.47 K and density 2.70 g/cm3, electrical conductivity 3.50×107 S/m.
Abbildung in dieser Leseprobe nicht enthalten
Figure 1- Machined AISI D2 workpiece and aluminium electrode
3- Evaluation of parameters
3.1 Evaluation of MRR
MRR is articulated as the ratio of the difference of weight of the work piece before and after machining to the machining time. MRR is measured in mg/min.
MRR= (Mi-Mf)/t
Whereas, Mi = Weight of work-piece before machining i.e. initial weight
Mf = Weight of work piece after machining i.e. final weight
3.2 Evaluation of TWR
TWR is articulated as the ratio of the difference of weight of the electrode before and after Machining to the machining time.
EWR =Ei-Ef /t
Whereas, Ei = Weight of electrode before machining.
Ef = Weight of electrode after machining.
3.3 Signal to noise (S/N) ratio
The main objective of the study is to examine the effect of aluminium as electrode on AISI D2 steel. Signal to Noise (S/N) ratio is selected as larger is better for MRR, smaller the better for SR and TWR.
S/N ratios are defined as:
Larger is better = -10 log (MSDHB)
Where MSDHB=1/r )
r = the number of tests in a trial
yi = observed value of response characteristics
For material removal rate (MRR) the S/N ratio is larger is better.
Where, MSDHB = Mean Square deviation for higher the better response.
Smaller is better = -10 log (MSDLB)
Where, MSDLB=1/r ),
r = the number of tests in a trial
yi = observed value of response characteristics
For Tool wear rate (TWR) the S/N ratio is smaller is better.
Where MSDLB = Mean Square deviation for smaller the better response.
4. Design of Experiments
Experiments In present study, Taguchi method was used to design the experiments. For optimization of system, Taguchi Technique reduces the trial of experiments to get only the necessary trial of experiments so there will be no repeated trials and it’s done using DOE. Three parameters viz., pulse on, current and pulse off were chosen as control parameters and apiece parameter has three levels shown in table 2. L9 orthogonal array was used to design the experiments shown in table 3. To obtain accurate results, each experiment was performed three times and there mean value is taken for optimization.
Table 2 – Parameter Selected and their levels
Abbildung in dieser Leseprobe nicht enthalten
Table 3 showing the values of material removal rate, tool wear and Surface roughness. MRR and TWR are measured in grams. SR is measured in microns using surface roughness tester. Experiments were executed three times to get the accurate results.
Table 3 – Observation Table
Abbildung in dieser Leseprobe nicht enthalten
Table 4 shows the values of SN ratio and means values. SN ratio is calculated using formula given in section 3.3.
Table 4 – Observation table
Abbildung in dieser Leseprobe nicht enthalten
Table 5 shows the response table values for current, pulse on and pulse off for material removal rate. Table 6 shows the values for Surface roughness and Table 7shows the corresponding values for tool wear rate.
Table 5- Response table for MRR Table 6- Response table for SR
Abbildung in dieser Leseprobe nicht enthaltenAbbildung in dieser Leseprobe nicht enthalten
Table 7- Response table for TWR
Abbildung in dieser Leseprobe nicht enthalten
5- Results and Discussions
Figure 4, 5, 6 shows the main effects plots for S/N ratio and Mean of means For MRR, TWR and SR respectively. Optimization is established on selecting the values from plots depending upon S/N ratio requirement. From table 5, means graph is drawn and displayed in fig 4. For S/N ratio we always take maximum value irrespective of smaller the better or larger is better. For mean of means, we select maximum values for optimization if S/N is higher the better and we take minimum value if it is smalleris better.
Abbildung in dieser Leseprobe nicht enthalten
Figure 4 – Mean effects plots for MRR
Graph displayed in figure 5 is made using table 7. For TWR, S/N ratio is smaller the better. So for SN ratio graph we take maximum values and for means we take minimum values for optimization.
Abbildung in dieser Leseprobe nicht enthalten
Figure 5-Mean effects plots for TWR
Graph displayed in figure 6 is made using table 6. For SR, S/N ratio is smaller the better. So for SN ratio graph we take maximum values and for means we take minimum values for optimization.
Abbildung in dieser Leseprobe nicht enthalten
Figure 6- Mean effects plots for SR
6- Conclusion
The main motive of the present study was to experimentally inspect the effect of aluminium as electrode which is tool on MRR, TWR and SR on AISI D2 steel. For MRR, the most prompting factor is pulse off time, followed by current and then pulse on time.
1. The Optimum conditions for MRR is Current (10 A), Pulse on (100 µs), Pulse off (10 µs) for AISI D2 steel.
2. For TWR, the most prompting factor is pulse off time, followed by current and then pulse on time. The optimum condition for TWR is Current (6A), Pulse on (150 µs), Pulse off (6 µs) for AISI D2 steel.
3. For SR, pulse on time is the most prompting factor followed by current and pulse off time. The optimum condition for SR is current (6A), Pulse on (50 µs), Pulse off (8 µs) for AISI D2 steel.
References
1. Choudhary, S., Kant, K. and Saini, P. (2013),”Analysis of MRR and SR with different electrode for SS316 on die sinking EDM using Taguchi technique”, Global Journal of Researches in Engineering Mechanical and Mechanics Engineering, Vol 13, Issue 3
2. Haron, C.H., Deros, B.M., Ginting, A. and Fauziah, M. (2001), “Investigation on influence of machining parameters when machining tool steel using EDM”, Journal of Materials Processing Technology, Vol. 116, pp. 84-87.
3. Kojima, H., Kunieda, M. and Nishiwaki, N. (1992), “Understanding discharge location movement during EDM”, Proceedings International Symposium Electro Machining, Vol. 10, pp. 144.
4. Abbas, N.M., Solomon, D.G. and Bahari, M.F. (2007),”A review on current research trends in electrical discharge machining (EDM)”, International Journal of Machine Tools and Manufacture, Vol 47, pp. 1214-1228
5. Amin, N., Lajis, M.A. and Radzi, M. (2009),”The implementation of Taguchi method on EDM process of Tungsten carbide”, European Journal of Scientific Research, Vol 26, issue 4, pp. 609-617
6. Haron, C.H., Ghani, J.A., Burhamidin, Y. and swee, C.Y. (2008),”Copper and graphite electrodes performance in electrical discharge machining of XW42 tool steel”, Journal of Material Processing Technology, Vol 201, pp. 570-573
7. Khanra, A.K., Sarjar, B.R., Bhattacharya, B., Pathak, L.C. and Godkhindi, M.M. (2007),”Performance of ZrB2-Cu composite as an EDM electrode,” Journal of Materials Processing Technology, Vol 183, pp. 122-126
8. Kristian, A.L. (2004),” Performance of two graphite qualities in EDM of Steel slots in a jet engine turbine vane”, Journal of Materials Processing Technology, Vol 149, pp. 152-158
9. Lee, S.H. and Li, X.P. (2001),”Study of the machining parameters on the machining characteristics in electrical discharge machining of Tungsten Carbide”, Journal of Materials Processing Technology, Vol 115, pp. 344-358
10. Murikan, R.T., Jakkamputi, L.P. and Kuppan, P. (2013),”Experimental investigation of dry electrical discharge machining on SS 316L”, International Journal of Latest Trends in Engineering and Technology, Vol 2, Issue 3
11. Dixit, A.C., Kumar, A., Singh, R.K. and Bajpai, R. (2015),”An experimental study of material removal rate and electrode wear rate of high carbon high chromium steel (AISI D3) in EDM process using copper tool electrode”,International Journal of Innovative Research in Advanced Engineering, Vol 3, Issue 1, pp 257-262
12. Rahi, D.K., and Vishwakarma, M. (2014),”Performance measurement of EDM parameters on high carbon high chromium steel”, International Journal of Engineering Research and Development, Vol 10, Issue 7, pp 15-21
13. Jegan, T.M.C., M., Dev Anand, M. and Ravindran, D. (2012),”Determination of electro discharge machining parameters in AISI 202 stainless steel using grey relational analysis”, International Conference on Modelling Optimization and Computing, Volume 38, pp 4005-4012
14. Rajendra, M. and Rao, K.M. (2014),”Experimental evaluation of performance of electrical discharge machining of D3 steel with Al2O3 abrasive mixed dielectric material by using design of experiments”, International Journal of Research in Engineering and Technology, Vol 3, issue 1, pp. 599-606
15. Patel, V.D. Mr., Patel, C.P. Prof, and Patel, V.J. ,”Analysis of different tool materials on MRR and surface roughness of Mild steel in EDM”, International Journal of Engineering Research and Application, Vol 1, Issue 3, pp 394-397
[...]
- Arbeit zitieren
- Sidhant Gupta (Autor:in), S.K. Jain (Autor:in), Gurpinder Singh (Autor:in), 2016, Experimental Study of MRR, TWR, SR on AISI D2 steel, München, GRIN Verlag, https://www.grin.com/document/340653
-
-
-
-
Laden Sie Ihre eigenen Arbeiten hoch! Geld verdienen und iPhone X gewinnen. -
-
Laden Sie Ihre eigenen Arbeiten hoch! Geld verdienen und iPhone X gewinnen. -
-
Laden Sie Ihre eigenen Arbeiten hoch! Geld verdienen und iPhone X gewinnen. -
-
Laden Sie Ihre eigenen Arbeiten hoch! Geld verdienen und iPhone X gewinnen.