PROCESS PARAMETERS OPTIMIZATION IN EDM FOR AISI D3 STEEL BY GREY RELATIONAL ANALYSIS METHOD
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This study focuses on optimizing process parameters for Electric Discharge Machining (EDM) of AISI D3 steel to enhance productivity by maximizing Material Removal Rate (MRR) and minimizing surface roughness (Ra) using Grey Relational Analysis and Taguchi methods. Experiments were structured using an L16 orthogonal array, examining variations in discharge current, pulse on time, pulse off time, and spark gap. Results indicate that discharge current and spark on time are key factors impacting MRR and surface roughness, allowing for improved efficiency and cost savings in machining processes.
![NOVATEUR PUBLICATIONS
INTERNATIONAL JOURNAL OF INNOVATIONS IN ENGINEERING RESEARCH AND TECHNOLOGY [IJIERT]
ISSN: 2394-3696
VOLUME 2, ISSUE 5, MAY-2015
1 | P a g e
PROCESS PARAMETERS OPTIMIZATION IN EDM FOR AISI D3
STEEL BY GREY RELATIONAL ANALYSIS METHOD
Sunil. B. Mishra
Department of Mechanical Engineering,
Mahatma Gandhi Mission’s College of Engineering, Nanded, India
Prof. J. K. Sawale
Department of Mechanical Engineering,
Mahatma Gandhi Mission’s College of Engineering, Nanded, India
ABSTRACT
In this work, optimization problem for AISI D3 material has been solved to satisfy requirements
of productivity in EDM operation. Experiments on die sinking EDM have been conducted using
L16 orthogonal array design using various process control parameters like discharge current (Ip),
pulse on time (Ton), pulse off time (Toff) and spark gap (SG) which are varied in four different
levels. Material Removal Rate (MRR) and surface roughness (Ra) has been measured for each
experimental run. Problem has been formulated in maximization of MRR (in order to increase
productivity) and minimization of Ra. Taguchi method is used for Design of experiment.
Optimum values of process parameters are obtained using grey relational analysis method.
KEYWORDS
EDM, optimization, process parameters, Taguchi method, grey relational analysis method,
Material removal rate, surface roughness (Ra), etc.
INTRODUCTION
Electric discharge machining is a thermo-electric non-traditional machining process. Material is
removed from the work piece through localized melting and vaporization of material. Electric
sparks are generated between two electrodes when the electrodes are held at a small distance
from each other in a dielectric medium and a high potential difference is applied across them.
Localized regions of high temperatures are formed due to the sparks occurring between the two
electrode surfaces. Work piece material in this localized zone melts and vaporizes. Most of the
molten and vaporized material is carried away from the inter-electrode gap by the dielectric flow
in the form of debris particles. To prevent excessive heating, electric power is supplied in the
form of short pulses. Spark occurs wherever the gap between the tool and the work piece surface
is smallest. After material is removed due to a spark, this gap increases and the location of the
next spark shifts to a different point on the work piece surface. In this way several sparks occur
at various locations over the entire surface of the work piece corresponding to the work piece-](https://image.slidesharecdn.com/1432100499volume2issue5may-2015-181126042440/85/PROCESS-PARAMETERS-OPTIMIZATION-IN-EDM-FOR-AISI-D3-STEEL-BY-GREY-RELATIONAL-ANALYSIS-METHOD-1-320.jpg)
![NOVATEUR PUBLICATIONS
INTERNATIONAL JOURNAL OF INNOVATIONS IN ENGINEERING RESEARCH AND TECHNOLOGY [IJIERT]
ISSN: 2394-3696
VOLUME 2, ISSUE 5, MAY-2015
2 | P a g e
tool gap. Because of the material removal due to sparks, after some time a uniform gap distance
is formed throughout the gap between the tool and the work piece.
EXPERIMENTAL SETUP
Figure01. EDM machine
For this experiment the whole work is done by using Electric Discharge Machine, model
ELECTRONICA- ELECTRAPULS PS 50ZNC (die-sinking type), having provision of
programming in the Z-vertical axis and manually operated X and Y axes. The tool is made of
cathode and the work piece as anode. Commercial grade EDM oil (specific gravity= 0.763 kg/
m3
), freezing point= 94°C) was used as dielectric fluid with lateral flushing (pressure of 0.3
kgf/cm2) system for effective flushing of machining debris from working gap region. The pulsed
discharge current was applied in various steps in positive mode.
DESIGN OF EXPERIMENT
Design of Experiments (DOE) refers to planning, designing and analyzing an experiment so that
valid and objective conclusions can be drawn effectively and efficiently. In performing a
designed experiment, changes are made to the input variables and the corresponding changes in
the output variables are observed. The input variables are called resources and the output
variables are called response.
Input variables: Discharge current (Ip); Spark on time (Ton); Spark off time (Toff); Spark gap
(SG)
Response Variables: Material removal rate(MRR), Surface Roughness (Ra)](https://image.slidesharecdn.com/1432100499volume2issue5may-2015-181126042440/85/PROCESS-PARAMETERS-OPTIMIZATION-IN-EDM-FOR-AISI-D3-STEEL-BY-GREY-RELATIONAL-ANALYSIS-METHOD-2-320.jpg)
![NOVATEUR PUBLICATIONS
INTERNATIONAL JOURNAL OF INNOVATIONS IN ENGINEERING RESEARCH AND TECHNOLOGY [IJIERT]
ISSN: 2394-3696
VOLUME 2, ISSUE 5, MAY-2015
3 | P a g e
TAGUCHI METHOD
Taguchi Method is developed by Dr. Genichi Taguchi, a Japanese quality management
consultant. The method explores the concept of quadratic quality loss function and uses a
statistical measure of performance called Signal-to-Noise (S/N) ratio. The S/N ratio takes both
the mean and the variability into account. The S/N ratio is the ratio of the mean (Signal) to the
standard deviation (Noise). The ratio depends on the quality characteristics of the
product/process to be optimized. The standard S/N ratios generally used are as follows: -
Nominal is Best (NB), Lower the Better (LB) and Higher the Better
Exp. No. Ip
(A)
Ton
(µs)
Toff
(µs)
SG
(mm)
1 3 40 5 0.05
2 3 50 6 0.1
3 3 60 7 0.15
4 3 70 8 0.2
5 7 40 6 0.15
6 7 50 5 0.2
7 7 60 8 0.05
8 7 70 7 0.1
9 11 40 7 0.2
10 11 50 8 0.15
11 11 60 5 0.1
12 11 70 6 0.05
13 15 40 8 0.1
14 15 50 7 0.05
15 15 60 6 0.2
16 15 70 5 0.15
Table 1. L16 Orthogonal Array](https://image.slidesharecdn.com/1432100499volume2issue5may-2015-181126042440/85/PROCESS-PARAMETERS-OPTIMIZATION-IN-EDM-FOR-AISI-D3-STEEL-BY-GREY-RELATIONAL-ANALYSIS-METHOD-3-320.jpg)
![NOVATEUR PUBLICATIONS
INTERNATIONAL JOURNAL OF INNOVATIONS IN ENGINEERING RESEARCH AND TECHNOLOGY [IJIERT]
ISSN: 2394-3696
VOLUME 2, ISSUE 5, MAY-2015
4 | P a g e
EXPERIMENTAL RESULTS
Table 2 Experimental results
Exp.
No.
Ip
(A)
Ton
(µs)
Toff
(µs)
SG
(mm)
Time
(min)
Density
(gm/mm3
)
MRR
(mm3
/mim)
Ra
Value
1 3 40 5 0.05 5 0.00765 3.9215 2.603
2 3 50 6 0.1 5 0.00765 4.6143 3.656
3 3 60 7 0.15 5 0.00765 5.6143 4.311
4 3 70 8 0.2 5 0.00765 7.8300 5.026
5 7 40 6 0.15 5 0.00765 22.4183 5.594
6 7 50 5 0.2 5 0.00765 25.5555 4.817
7 7 60 8 0.05 5 0.00765 26.1568 3.975
8 7 70 7 0.1 5 0.00765 27.7124 3.124
9 11 40 7 0.2 5 0.00765 28.4967 5.606
10 11 50 8 0.15 5 0.00765 32.6797 4.352
11 11 60 5 0.1 5 0.00765 36.0784 5.876
12 11 70 6 0.05 5 0.00765 38.1895 5.175
13 15 40 8 0.1 5 0.00765 30.5882 6.346
14 15 50 7 0.05 5 0.00765 36.8627 6.746
15 15 60 6 0.2 5 0.00765 39.8039 5.124
16 15 70 5 0.15 5 0.00765 40.5882 5.840](https://image.slidesharecdn.com/1432100499volume2issue5may-2015-181126042440/85/PROCESS-PARAMETERS-OPTIMIZATION-IN-EDM-FOR-AISI-D3-STEEL-BY-GREY-RELATIONAL-ANALYSIS-METHOD-4-320.jpg)
![NOVATEUR PUBLICATIONS
INTERNATIONAL JOURNAL OF INNOVATIONS IN ENGINEERING RESEARCH AND TECHNOLOGY [IJIERT]
ISSN: 2394-3696
VOLUME 2, ISSUE 5, MAY-2015
5 | P a g e
GREY RELATIONAL ANALYSIS METHOD
This method gives the common optimum values for material removal rate and surface roughness.
MeanofS/NRatio
151173
-2.0
-2.5
-3.0
70605040
8765
-2.0
-2.5
-3.0
0.200.150.100.05
Ip T on
T off SG
M ain Effe cts Plot (data me ans) for S/N Ratio
Figure02 Graph 1 S/N Ratio plot of overall grey relational grade
CONFIRMATORY EXPERIMENTS
Optimal setting
Prediction Experiment
Level of factors Ip2, Ton4, Toff1, SG1 Ip2, Ton4, Toff1, SG1
S/N ratio -1.2016 -0.8949
Overall grey relational grade 0.8708 0.9021
Table 3 Results of confirmatory experiment](https://image.slidesharecdn.com/1432100499volume2issue5may-2015-181126042440/85/PROCESS-PARAMETERS-OPTIMIZATION-IN-EDM-FOR-AISI-D3-STEEL-BY-GREY-RELATIONAL-ANALYSIS-METHOD-5-320.jpg)
![NOVATEUR PUBLICATIONS
INTERNATIONAL JOURNAL OF INNOVATIONS IN ENGINEERING RESEARCH AND TECHNOLOGY [IJIERT]
ISSN: 2394-3696
VOLUME 2, ISSUE 5, MAY-2015
6 | P a g e
CONCLUSIONS
In the present study the effect of machining parameters on MRR and surface roughness (Ra) for
material AISI D3 using the cylindrical shaped copper tool with side flushing system have
investigated for EDM process.
Discharge current and Spark on time are the most influencing factors. MRR increases with the
increase in discharge current (Ip) and Spark on time. While machining the material AISI D3, the
industrialist can directly use the optimum values so that the material removal rate will be
maximum and Ra value will be minimum. This will save the time required for machining,
improve surface roughness save the electrical power consumption, reduce labor cost, etc
REFERENCES
[1] Md. Ashikur Rahman Khan, M.M. Rahman1, K. Kadirgama1, M.A. Maleque and M. Ishak
Prediction Of Surface Roughness Of Ti-6al-4v In Electrical Discharge Machining: A Regression
Model Journal Of Mechanical Engineering And Sciences (Jmes) E-Issn: 2231-8380; Volume 1,
Pp. 16-24, December 2011.
[2] Pichai Janmanee and Apiwat Muttamara, Optimization of Electrical Discharge Machining of
Composite 90WC-10Co Base on Taguchi Approach, European Journal of Scientific Research
ISSN 1450-216X Vol.64 No.3 (2011), pp. 426-436 © EuroJournals Publishing, Inc. 2011
[3] S. Murugesan and K. Balamurugan, Study on EDM of Al-15%SiC MMC using Solid and
Multihole Electrodes- A Taguchi Approach,European Journal of Scientific Research ISSN 1450-
216X Vol.68 No.2 (2012), pp. 161-171 © EuroJournals Publishing, Inc. 2012
[4] Prof.B. R. Jadhav and Prof.M.V.Kavade, Experimental Study Of Ai-Si Alloy Plate On
Electric Discharge Machining Using Tungsten Electrode, International Journal Of Advanced
Engineering Research And Studies E-Issn2249– 8974
[5] B. C. Routara, P. Sahoo, A. Bandyopadhyay, Application Of Response Surface Method For
Modelling Of Statistical Roughness Parameters On Electric Discharge Machining, Proceedings
Of The International Conference On Mechanical Engineering 2007 (Icme2007) 29- 31
December 2007, Dhaka, Bangladesh Icme2007-Am-17
[6] Mohan Kumar Pradhan and Chandan Kumar Biswas, Modelling of machining parameters for
MRR in EDM using response surface methodology, Proceedings of NCMSTA’08 Conference
National Conference on Mechanism Science and Technology: from Theory to Application
November 13-14, 2008 National Institute of Technology, Hamirpur
[7] Saurabh P Shah, Anand Y Joshi, Application of Taguchi Method for Optimization of
Parameters used in Electrical Discharge Machining Process, International Conference on
Science, Technology and Innovation for Sustainable Well-Being (STISWB), 23-24 July 2009,
Mahasarakham University, Thailand.](https://image.slidesharecdn.com/1432100499volume2issue5may-2015-181126042440/85/PROCESS-PARAMETERS-OPTIMIZATION-IN-EDM-FOR-AISI-D3-STEEL-BY-GREY-RELATIONAL-ANALYSIS-METHOD-6-320.jpg)
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PROCESS PARAMETERS OPTIMIZATION IN EDM FOR AISI D3 STEEL BY GREY RELATIONAL ANALYSIS METHOD
- 1. NOVATEUR PUBLICATIONS INTERNATIONAL JOURNAL OF INNOVATIONS IN ENGINEERING RESEARCH AND TECHNOLOGY [IJIERT] ISSN: 2394-3696 VOLUME 2, ISSUE 5, MAY-2015 1 | P a g e PROCESS PARAMETERS OPTIMIZATION IN EDM FOR AISI D3 STEEL BY GREY RELATIONAL ANALYSIS METHOD Sunil. B. Mishra Department of Mechanical Engineering, Mahatma Gandhi Mission’s College of Engineering, Nanded, India Prof. J. K. Sawale Department of Mechanical Engineering, Mahatma Gandhi Mission’s College of Engineering, Nanded, India ABSTRACT In this work, optimization problem for AISI D3 material has been solved to satisfy requirements of productivity in EDM operation. Experiments on die sinking EDM have been conducted using L16 orthogonal array design using various process control parameters like discharge current (Ip), pulse on time (Ton), pulse off time (Toff) and spark gap (SG) which are varied in four different levels. Material Removal Rate (MRR) and surface roughness (Ra) has been measured for each experimental run. Problem has been formulated in maximization of MRR (in order to increase productivity) and minimization of Ra. Taguchi method is used for Design of experiment. Optimum values of process parameters are obtained using grey relational analysis method. KEYWORDS EDM, optimization, process parameters, Taguchi method, grey relational analysis method, Material removal rate, surface roughness (Ra), etc. INTRODUCTION Electric discharge machining is a thermo-electric non-traditional machining process. Material is removed from the work piece through localized melting and vaporization of material. Electric sparks are generated between two electrodes when the electrodes are held at a small distance from each other in a dielectric medium and a high potential difference is applied across them. Localized regions of high temperatures are formed due to the sparks occurring between the two electrode surfaces. Work piece material in this localized zone melts and vaporizes. Most of the molten and vaporized material is carried away from the inter-electrode gap by the dielectric flow in the form of debris particles. To prevent excessive heating, electric power is supplied in the form of short pulses. Spark occurs wherever the gap between the tool and the work piece surface is smallest. After material is removed due to a spark, this gap increases and the location of the next spark shifts to a different point on the work piece surface. In this way several sparks occur at various locations over the entire surface of the work piece corresponding to the work piece-
- 2. NOVATEUR PUBLICATIONS INTERNATIONAL JOURNAL OF INNOVATIONS IN ENGINEERING RESEARCH AND TECHNOLOGY [IJIERT] ISSN: 2394-3696 VOLUME 2, ISSUE 5, MAY-2015 2 | P a g e tool gap. Because of the material removal due to sparks, after some time a uniform gap distance is formed throughout the gap between the tool and the work piece. EXPERIMENTAL SETUP Figure01. EDM machine For this experiment the whole work is done by using Electric Discharge Machine, model ELECTRONICA- ELECTRAPULS PS 50ZNC (die-sinking type), having provision of programming in the Z-vertical axis and manually operated X and Y axes. The tool is made of cathode and the work piece as anode. Commercial grade EDM oil (specific gravity= 0.763 kg/ m3 ), freezing point= 94°C) was used as dielectric fluid with lateral flushing (pressure of 0.3 kgf/cm2) system for effective flushing of machining debris from working gap region. The pulsed discharge current was applied in various steps in positive mode. DESIGN OF EXPERIMENT Design of Experiments (DOE) refers to planning, designing and analyzing an experiment so that valid and objective conclusions can be drawn effectively and efficiently. In performing a designed experiment, changes are made to the input variables and the corresponding changes in the output variables are observed. The input variables are called resources and the output variables are called response. Input variables: Discharge current (Ip); Spark on time (Ton); Spark off time (Toff); Spark gap (SG) Response Variables: Material removal rate(MRR), Surface Roughness (Ra)
- 3. NOVATEUR PUBLICATIONS INTERNATIONAL JOURNAL OF INNOVATIONS IN ENGINEERING RESEARCH AND TECHNOLOGY [IJIERT] ISSN: 2394-3696 VOLUME 2, ISSUE 5, MAY-2015 3 | P a g e TAGUCHI METHOD Taguchi Method is developed by Dr. Genichi Taguchi, a Japanese quality management consultant. The method explores the concept of quadratic quality loss function and uses a statistical measure of performance called Signal-to-Noise (S/N) ratio. The S/N ratio takes both the mean and the variability into account. The S/N ratio is the ratio of the mean (Signal) to the standard deviation (Noise). The ratio depends on the quality characteristics of the product/process to be optimized. The standard S/N ratios generally used are as follows: - Nominal is Best (NB), Lower the Better (LB) and Higher the Better Exp. No. Ip (A) Ton (µs) Toff (µs) SG (mm) 1 3 40 5 0.05 2 3 50 6 0.1 3 3 60 7 0.15 4 3 70 8 0.2 5 7 40 6 0.15 6 7 50 5 0.2 7 7 60 8 0.05 8 7 70 7 0.1 9 11 40 7 0.2 10 11 50 8 0.15 11 11 60 5 0.1 12 11 70 6 0.05 13 15 40 8 0.1 14 15 50 7 0.05 15 15 60 6 0.2 16 15 70 5 0.15 Table 1. L16 Orthogonal Array
- 4. NOVATEUR PUBLICATIONS INTERNATIONAL JOURNAL OF INNOVATIONS IN ENGINEERING RESEARCH AND TECHNOLOGY [IJIERT] ISSN: 2394-3696 VOLUME 2, ISSUE 5, MAY-2015 4 | P a g e EXPERIMENTAL RESULTS Table 2 Experimental results Exp. No. Ip (A) Ton (µs) Toff (µs) SG (mm) Time (min) Density (gm/mm3 ) MRR (mm3 /mim) Ra Value 1 3 40 5 0.05 5 0.00765 3.9215 2.603 2 3 50 6 0.1 5 0.00765 4.6143 3.656 3 3 60 7 0.15 5 0.00765 5.6143 4.311 4 3 70 8 0.2 5 0.00765 7.8300 5.026 5 7 40 6 0.15 5 0.00765 22.4183 5.594 6 7 50 5 0.2 5 0.00765 25.5555 4.817 7 7 60 8 0.05 5 0.00765 26.1568 3.975 8 7 70 7 0.1 5 0.00765 27.7124 3.124 9 11 40 7 0.2 5 0.00765 28.4967 5.606 10 11 50 8 0.15 5 0.00765 32.6797 4.352 11 11 60 5 0.1 5 0.00765 36.0784 5.876 12 11 70 6 0.05 5 0.00765 38.1895 5.175 13 15 40 8 0.1 5 0.00765 30.5882 6.346 14 15 50 7 0.05 5 0.00765 36.8627 6.746 15 15 60 6 0.2 5 0.00765 39.8039 5.124 16 15 70 5 0.15 5 0.00765 40.5882 5.840
- 5. NOVATEUR PUBLICATIONS INTERNATIONAL JOURNAL OF INNOVATIONS IN ENGINEERING RESEARCH AND TECHNOLOGY [IJIERT] ISSN: 2394-3696 VOLUME 2, ISSUE 5, MAY-2015 5 | P a g e GREY RELATIONAL ANALYSIS METHOD This method gives the common optimum values for material removal rate and surface roughness. MeanofS/NRatio 151173 -2.0 -2.5 -3.0 70605040 8765 -2.0 -2.5 -3.0 0.200.150.100.05 Ip T on T off SG M ain Effe cts Plot (data me ans) for S/N Ratio Figure02 Graph 1 S/N Ratio plot of overall grey relational grade CONFIRMATORY EXPERIMENTS Optimal setting Prediction Experiment Level of factors Ip2, Ton4, Toff1, SG1 Ip2, Ton4, Toff1, SG1 S/N ratio -1.2016 -0.8949 Overall grey relational grade 0.8708 0.9021 Table 3 Results of confirmatory experiment
- 6. NOVATEUR PUBLICATIONS INTERNATIONAL JOURNAL OF INNOVATIONS IN ENGINEERING RESEARCH AND TECHNOLOGY [IJIERT] ISSN: 2394-3696 VOLUME 2, ISSUE 5, MAY-2015 6 | P a g e CONCLUSIONS In the present study the effect of machining parameters on MRR and surface roughness (Ra) for material AISI D3 using the cylindrical shaped copper tool with side flushing system have investigated for EDM process. Discharge current and Spark on time are the most influencing factors. MRR increases with the increase in discharge current (Ip) and Spark on time. While machining the material AISI D3, the industrialist can directly use the optimum values so that the material removal rate will be maximum and Ra value will be minimum. This will save the time required for machining, improve surface roughness save the electrical power consumption, reduce labor cost, etc REFERENCES [1] Md. Ashikur Rahman Khan, M.M. Rahman1, K. Kadirgama1, M.A. Maleque and M. Ishak Prediction Of Surface Roughness Of Ti-6al-4v In Electrical Discharge Machining: A Regression Model Journal Of Mechanical Engineering And Sciences (Jmes) E-Issn: 2231-8380; Volume 1, Pp. 16-24, December 2011. [2] Pichai Janmanee and Apiwat Muttamara, Optimization of Electrical Discharge Machining of Composite 90WC-10Co Base on Taguchi Approach, European Journal of Scientific Research ISSN 1450-216X Vol.64 No.3 (2011), pp. 426-436 © EuroJournals Publishing, Inc. 2011 [3] S. Murugesan and K. Balamurugan, Study on EDM of Al-15%SiC MMC using Solid and Multihole Electrodes- A Taguchi Approach,European Journal of Scientific Research ISSN 1450- 216X Vol.68 No.2 (2012), pp. 161-171 © EuroJournals Publishing, Inc. 2012 [4] Prof.B. R. Jadhav and Prof.M.V.Kavade, Experimental Study Of Ai-Si Alloy Plate On Electric Discharge Machining Using Tungsten Electrode, International Journal Of Advanced Engineering Research And Studies E-Issn2249– 8974 [5] B. C. Routara, P. Sahoo, A. Bandyopadhyay, Application Of Response Surface Method For Modelling Of Statistical Roughness Parameters On Electric Discharge Machining, Proceedings Of The International Conference On Mechanical Engineering 2007 (Icme2007) 29- 31 December 2007, Dhaka, Bangladesh Icme2007-Am-17 [6] Mohan Kumar Pradhan and Chandan Kumar Biswas, Modelling of machining parameters for MRR in EDM using response surface methodology, Proceedings of NCMSTA’08 Conference National Conference on Mechanism Science and Technology: from Theory to Application November 13-14, 2008 National Institute of Technology, Hamirpur [7] Saurabh P Shah, Anand Y Joshi, Application of Taguchi Method for Optimization of Parameters used in Electrical Discharge Machining Process, International Conference on Science, Technology and Innovation for Sustainable Well-Being (STISWB), 23-24 July 2009, Mahasarakham University, Thailand.