ISBN: 978-93-82163-09-1 .VOLUME 1 24 and 25 Febuary 2014 ...

International conference on Emerging Trends in Mechanical Engineering ICEME-2014
ISBN: 978-93-82163-09-1 .VOLUME 1 24TH ,25TH FEBRURY 2014

Table3: ANOVA table after dropping interaction terms

Source Sum of DOF Mean F p-value Remark
Squares Square Value Prob> F

Model 0.162 5 0.032 31.322 < 0.0001 significant
A-speed 0.011 1 0.011 10.855 0.007
B-feed 0.101 1 0.101 97.697 < 0.0001
C-doc 0.028 1 0.028 27.789 0.0003
AC 0.004 1 0.004 4.728 0.052
C2 0.016 1 0.016 15.54 0.002
Residual 0.011 11 0.001

Lack of Fit 0.01 7 0.001 5.46 0.06 not
significant
4 0.0002
Pure Error 0.001 16
Cor Total 0.173

Table 4: OTHER ANOVA Parameters after model reduction

Std. Dev. 0.032 R-Squared 0.93

Mean 0.577 Adj R-Squared 0.90

C.V. % 5.573 Pred R-Squared 0.73
PRESS 0.046 Adeq Precision 20.08

The final equation in terms of coded factors is,

The final equation in terms of actual factors is,

Based on the response surface model obtained after regression analysis, the results in terms of effect of speed, feed,
depth of cut and their interaction on surface roughness is discussed in the following subsections.

Surface roughness plays an important role in many areas and is a factor of great importance in the evaluation of
machining accuracy. Although many factors affect the surface condition of machined part, machining parameters such as
cutting speed, feed, and depth of cut have a significant influence on the surface roughness for a given machine tool and
work piece set-up .Titanium is a material generally utilized for parts requiring the greatest reliability and therefore the
surface roughness and any damage to the subsurface layers must be controlled.

The main effect of feed is the most significant factor associated with surface roughness. This is expected because it is
well known that for a given tool nose radius, the classical surface roughness is primarily a function of the feed. It is clear
fro m these figures (Fig 1, 2 and 3) that the surface roughness reduces with the increase of cu tting speed. However, it
increases with the increase of feed and depth of cut. The surface plot shows the influence of different machining
variables, keeping the other variables at constant levels.

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Fig 1. Effect of feed on surface roughness Fig 2. Effect of speed on Surface roughness

Fig 3. Effect of depth of cut on surface roughness

Figure 4 illustrates the surface model for surface roughness by varying the two variables cutting speed, and feed
and keeping the third parameter depth of cut at constant level. The figure indicates that the surface roughness
increases with increase of feed. Contrary to the feed, the surface roughness increases with decrease of cutting
speed. Figure 5 shows the effect of cutting speed with respect to depth of cut on surface roughness. From the
figure, it has been asserted that the increase of cutting speed reduces the surface roughness, whereas the increase
of depth of cut increases the surface roughness. Figure 6 shows the influence of feed and depth of cut on surface
roughness by keeping the cutting speed at middle level. From the figure, it can be asserted that the increases in
feed and depth of cut increase the surface roughness. This is attributed to the increases in the thermal load and
vibration on the machinetool.

Fig 6. Interaction effect of depth of cut and feed on surface
Fig .no 4 interaction effect of feed and speed on surface roughness

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International conference on Emerging Trends in Mechanical Engineering ICEME-2014
ISBN: 978-93-82163-09-1 .VOLUME 1 24TH ,25TH FEBRURY 2014

Fig 5. Interaction effect of speed and depth of cut on surface roughness.

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International conference on Emerging Trends in Mechanical Engineering ICEME-2014
ISBN: 978-93-82163-09-1 .VOLUME- 1, 24th, 25th February 2014

3.2. Single response optimization using desirability function

RSM is a sequential strategy which enables us to approach the optimal region and depict the response efficiently, while
DFA is a useful technique of analyzing of experiments in which response to be optimized. RSM and DFA have b een
demonstrated to be efficient to optimize roller burnishing process parameters for surface roughness. Single response
optimization determines how input parameters affect desirability of individual response. The numerical optimization finds
a point that maximizes the desirability function. Adjusting the weight or importance may alter the characteristics of a goal.
The goal feed for response must be one of five choices: none, maximum, minimum, target or in range. The DFA is to first
transform response to a desirability function that takes values in range 0<d<1. When the response variable is at its goal or
target, d becomes 1, and if the response variable is outside the acceptable range, d becomes zero. In this study, the target
for the response is a minimum value (smaller the better), the transformation of surface roughness is smaller the better
problem. The response is transformed into d as:

d=

Where U: Upper specification limit, T: Target value, y: Response, r: Weight.

Alternative solutions of the optimization approach used to determine the optimum processing conditions are shown in
Table 5.
A contour plot for desirability was drawn keeping input parameters in range and surface roughness at minimum. The
contour plot for single desirability is shown in Fig 7. The near optimal region was located close to the left region of the
plot, which has desirability value (d=1.00) that gradually reduced moving towards right.

Table 5: Iterative determination of optimum conditions

Solutions

Number speed feed doc SR Desirability

1 73.40 0.04 0.51 0.366962 1 Selected

2 60.00 0.04 0.50 0.3525 1

3 57.57 0.04 0.51 0.360862 1

4 43.68 0.04 0.50 0.351015 1

5 74.84 0.04 0.51 0.36985 1

Fig 7. The contour plot for the result of desirability function.

Conclusion

In this experimental study, the evaluations of the three variables cutting speed, feed and depth of cut were
investigated by combining RSM and DFA. RSM with Box Behnken method was employed to evaluate the effects of
machining parameters on the surface roughness of the Titanium alloy (Ti–6Al–4V). The established equations clearly show
that the feed is the factor which influences surface roughness followed by cutting speed. The surface roughness increases
with increasing feed but decreased with increasing cutting speed. The variance analysis for the two factor interaction
model shows that the depth of cut is the least significant parameter. The experimental results at the optimum process
parameter combination confirm the effectiveness of the response surface models for optimum machining parameters.
RSM approach can help manufacturers to determine the appropriate machining conditions, in order to achieve specific
surface roughness. RSM was found to be a useful approach and it should be recommended that this methodology be
adopted to all optimization studies.

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International conference on Emerging Trends in Mechanical Engineering ICEME-2014
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References

[1] X. Yang, C.R. Liu, Machining titanium and its alloys, Mach. Sci. Technol. 3/1 (1999) 107–139.
[2] Norihiko Narutaki, Akio Murakoshi, Hidehiko Takeyama, Study on machining of titanium alloys, Ann. CIRP 32/1 (1983) 65–69.
[3] RajendraPawar and Raju Pawade “Surface Integrity Analysis in Dry High Speed Turning of Titanium Alloy Ti6Al4V,” ICTIME 2012

March 24-25 Dubai.
[4] ] Mohsen GhahramaniNik,& Mohammad R. Movahhedy&JavadAkbari “ Ultrasonic- Associated Grinding (UAG) of Ti-6Al-4V” ; 5th

CIRP Conference on High Performance Cutting 2012, Tehran, Iran
[5] NarasimhuluAndriya, P.VenkateswaraRao and Sudharshan Ghosh, “Dry Machining of Ti-6Al-4V using PVD Coated TiAlN Tools” ;

Proceedings of the World Conference on Engineering 2012 Vol- III WCE -2012 July 4-6 2012 London.(UK)
[6] Syed H. Imran Jaffery and Paul Mativenga, “Wear Mechanism Analysis of Turning Ti6Al4V –towards the development of suitable

tool coatings”, Springer –Verlag London Limited -2011.
[7] Anil K Srivatsava, Xueping Zhang, Tim Bell and Steve Cadigan, “Investigations on turning of TI-6Al-4V titanium alloy using super finished

tool edge geometry generated by micro machining process (MMP)”, .Tech Solve ,Inc., Cincinati, USA, Shangai Jiao Tong University ,Shangai.
[8] M.Venkatramana, K.Srinivasulu, and G.Krishnamohan Rao, “Performance Evaluation and Selection of Optimal Parameters in Turning

of Ti-6Al-4V Alloy under Different Cooling Conditions” IJITCE - 5th May 2011.
[9] SatyanarayanaKosaraju, Venugopal Anne and BangarubabuPopuri “Taguchi Analysis on cutting force and temperature in turning of

Ti-6Al-4V alloy” IJMIE - 2012.
[10] Z. G. Wang & M. Rahman & Y. S. Wong & K. S. Neo & J. Sun & C. H. Tan & H. Onozuka, “Study on orthogonal turning of titanium

alloys with different coolant supply strategies”, International Journal of Advanced Manufacturing Technology (2009) 42:621–632
[11] S. Ramesh, L. Karunamoorthy, K. Palanikumar, “Measurement and analysis of surface roughness in turning of aerospace titanium

alloy”, Measurement (2012), 45, 1266-1272.
[12] Montgomery, D.C. Design and analysis of experiments; 5th Edition, John Wiley & Sons Inc., New York, 2001.

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ISBN: 978-93-82163-09-1 .VOLUME- 1, 24th, 25th February 2014

EFFECTS OF PROCESS PARAMETERS ON KERF

IN ABRASIVE JET DRILLING OF GLASS

D.V. Srikanth1, Dr. M. Sreenivasa Rao2

1Department of Mechanical Engineering, Abhinav Hi-Tech College of Engineering, Hyderabad, A.P. India 2Dept. of Mechanical
Engineering, JNTUH, Kukatpally, Hyderabad, A.P. India.
E-Mail : [email protected]

Keywords: erosion rate, stand of distance. Material removal rate. Glass. Process parameters

ABSTRACT

Abrasive Jet Machining is an emerging technology with distinct advantages over the other non-traditional cutting technologies,
such as high flexibility, , high machining versatility minimum stresses on the work piece, no thermal distortion and less cutting
forces. Abrasive Jet Machining is a micromachining process in which the metal removal takes place due to erosion of High speed
abrasives carried in a medium of gas, generally air. This process is effectively used for hard and brittle material like Glas s,
Ceramics, and Composites etc. This paper investigates the effect of process parameters on width of cut of Glass specimen. There
are numerous associated parameters in this machining process. They are air Pressure, Nozzle diameter, standoff distance, impact
angle, nozzle length, abrasive mass flow rate, abrasive particle size, abrasive particle shape and abrasive particle hardness.
Among these parameters Air Pressure, Nozzle diameter, Stand of distance, Abrasive flow rate are of great importance but
precisely controllable. The Performance measures include Depth of cut, kerf width, Surface roughness and Metal removal rate. In
this paper Kerf width (Top & bottom kerf width) is considered as performance measure and the Process parameters considered
are Pressure, SOD, Abrasive Mass flow rate and Nozzle diameter.

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INTRODUCTION

Abrasive jet machining (AJM) is a Un Conventional Machining Process in which removal of material takes place through the
action of impingement of fine abrasive particles in a high-velocity gas stream. This process is also referred to as “pencil
blasting” or “micro-abrasive blasting”. Micro-abrasive particles are propelled by inert gas at velocities of 150 to 300 m/sec.
When directed at a work piece, the result in erosion can be used for cutting, etching, cleaning, deburring, polishing, and
drilling. Material removal occurs through a chipping action, which is especially effective on hard, brittle materials such as
glass, silicon, tungsten, and ceramics. Soft, resilient materials, such as rubber and some plastics resist the chipping action
and thus are not effectively processed by AJM (1,3). This process is inherently free from chatter, vibration, and heat
problems because the tool never touches the substrate. This process is used in a large variety of applications ranging from
cutting to cleaning.

The gas supply pressure is on the order of 850 kPa (125 psi) and the jet velocity can be as high as 300 m/s and is
Controlled by a valve . Typical cutting speeds vary between 25 -125 mm/min. The dimensional Tolerance Typical ranges ±

2 to ± 5 µm (5) . The Surface Finish Typical Ra values vary from 0.3 - 2.3 µm .
Abrasive jet machining (AJM), also called pencil blasting, is a Non-Traditional machining process in which utilizes a high-
pressure air stream carrying Micro Abrasive particles to impinge the work piece surface for material removal and shape
generation. AJM differ from the conventional sand blasting process in the way that the abrasive is much finer and effective
control over the process parameters and cutting(8). Used mainly to cut hard and brittle mat erials, which are thin and
sensitive to heat. The metal removal occurs due to the erosion of the abrasive particles striking the work piece surface.
AJM has limited material removal capability and is typically used as a finishing, cleaning and deburring pr ocess. The
Physics of this process is first Fine Abrasive particles (0.025mm) are accelerated in a gas stream(3) . The abrasive grains are
then directed towards the focus of machining. As the particles impact the surface, they fracture off the work surface. The
gas stream carries both the abrasive particles and the fractured (wear) particles away. This process is Good for difficult to
reach area.
The work piece material will be removed by the action of mechanical abrasion of the high velocity abrasive parti cles. The
material removal rate is mainly dependent on the flow rate and size of the abrasive particles. High grain size will always
produce more metal removal. At a particular pressure the material removal rate increases with the abrasive flow rate but
after reaching an optimum value, the material removal rate decreases with the further increase in abrasive flow rate(12).
This is due to the fact that mass flow rate of the gas decreases with the increase of abrasive flow rate and hence the
mixing ratio increases causing a decrease in material removal rate because of the decreasing energy available for erosion.
The abrasive particles are generally not used again and again i.e. reuse of abrasives are not preferred. Construction of
Abrasive jet machine is easy because of Low capital cost .

Fig 1 : Abrasive Jet Machining

An abrasive is a small, hard particle having sharp edges and an irregular shape. Abrasives are capable of removing small
amounts of material through a cutting process that produces tiny chips. Various types of abrasive particles with different
sizes used in Abrasive Jet Machine are Aluminum oxides, silicon carbides, Crushed glass, Sodium bicarbonate, Dolomite.
Aluminum oxide, Aluminium oxide is a chemical compound of aluminium and oxygen with the chemical formula
Al2O3.one of the most commonly used materials, is used to clean, cut and deburr.The other promising abrasive is Silicon
carbide also known as carborundum, is a compound of silicon and carbon with chemical formula SiC(7,9,12). It occurs in
nature as the extremely rare mineral moissanite. Silicon carbide powder has been mass-produced since 1893 for use as an
abrasive. For the lightest duty applications, such as the clearing, cutting and deburring of soft materials are performed
with sodium bicarbonate (baking soda) which is a soft powder and can leave surfaces free of scratches.
As the Size of abrasive particle is important, abrasive are available in many sizes ranging from 10u to 50u. The smaller
sizes(fine) are most useful for polishing and cleaning, while the larger sizes (coarse)are best for cutting drilling and
penning .

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The nozzles are typically made of tungsten carbide, brass or sapphire. Tungsten carbide n ozzles with either round or
rectangular holes are available and lost for an average of 300 hr but are 3 to 8 times more expensive. nozzles are available
with diameters ranging from 0.12 to 5 mm . The life of a nozzle must be partly defined by its application. Exacting
operations, such as cutting, required that nozzles be changed more often than when is in etching or cleaning. As nozzles
wear, the jet stream tends to diffuse faster resulting in material damage outside the intended line of cut. This is known as
stray cutting or overspreads.

The gas propulsion system provides the steady supply of clean dry gas used to propel the abrasive particles. Depending
upon the demands of the installation, either an air compressor or bottled gas cylinders may be used. If an air compressor is
used, proper filtering unit must be installed to avoid water or oil contamination of the abrasive powders. The least
expensive, and thus the most common gasses to use, are nitrogen and carbon-di-oxide. Oxygen should never be used as it
presents a fire hazard. The Pressure of the gas is regulated by a pressure regulator. The pressure of gas ranges from 3
kg/cm2 to 8 kg/cm2.High nozzle pressure tends to more metal removal rate but reduces the nozzle life.

Table 1 :Parameters and the details of operational perameters
Standoff distance (SOD) or Nozzle tip distance plays a very important role in material removal in AJM. Material removal
rate increases with the increases in NTD up to a certain limit after which it remains constant for a certain NTD and then
falls gradually. The nozzle tip distance (NTD) has also a direct effect on the width of cut owing to the outward flaring. The
nozzle tip distance should ranges from 0.25-75 mm.

Fig 2: Graph indicates the relation between NTD & MRR
Material removal rate can be improved by increasing the abrasive flow rate provided the mixing ratio could be kept
constant . The mixing ratio can be kept unchanged only by increasing the mass flow rate of the ga s. This one is possible by
increasing the internal gas pressure. As a matter of fact the material removal rate increases with the increase of gas
pressure as. Hence it can be stated conclusively that the optimum material removal rate (MRR) can be predicted
precautions should be taken to avoid its exposure to moisture

Fig 3: Graphs (a & b) indicate the relation between MRR Vs MR and AMFR Vs MRR

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International conference on Emerging Trends in Mechanical Engineering ICEME-2014
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EXPERIMENTAL WORK

MATERIAL

Glass is the Material used as work material due to its homogeneous properties. A general definition for Glass is a
amorphous solid material that exhibits a glass transition, which is the reversible transition in amorphous materials from a
hard and relatively brittle state into a molten or rubber-like state. The test specimens were cut into square and
rectangular shape for machining on AJM unit having different thicknesses. In machine the initial weights of glass
specimens were measured with the help of digital balance(11,13). After machining the final weights were measu red with
the help of digital balance to calculate the material removal rate.

Abrasive Jet Machine Set-Up

Experiments were conducted to confirm the effect of process parameters on width of cut of glass machined by AJM.
The experimental work was carried on a test rig which was designed and manufactured in the workshops of the
Mechanical Engineering Department, Abhinav Hi tech college of Engineering, Hyderabad. The type of abrasive used for

these experimentation Aluminium Oxide (Al2o3). Several Nozzles were made with different bore sizes (2,3,4mm),
Tungsten was used as the nozzle material. Drilling was performed by setting the important parameters. The list of
parameters used are indicated in Table 1.

Fig 4 : A JM Set-Up at AHTCE Fig 5 : Impingement of air through nozzle

1 Type of abrasive S.No Parameters Condition
2 Carrier gas
3 Nozzle tip distance 1 Type of Abrasive Al2O3,SiC
4 Size of abrasive
5 Velocity of abrasive jet 2 Size Of Abrasive 40,60 Mesh
6 Mixing ratio
7 Work material 3 Pressure Up to 6 kg/cm2
8 Nozzle design
9 Shape of cut 4 Stand of Distance 5-14 mm

Table 2: Process Parameters of AJM 5 Nozzle Diameter 1,2,3 mm

6 Abrasive Flow Rate 5gm/min

7 Machining Time 10-20 sec

Table 3: Operational Parameters of AJM

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EXPERIMENTAL RESULTS AND DISCUSSION

By analysing the experimental data, it has been found that the effects of the four basic pa rameters, i.e., Air pressure,
abrasive mass flow rate, nozzle diameter and nozzle standoff distance on the kerf width are in the same fashion as
reported in previous studies for glass and other materials. The effects each of these parameters is studied whi le keeping
the other parameters considered in this study as constant.
Effect of Air pressure on Kerf Width

The influence of water pressure on the depth of cut is shown in Fig. 2. Results indicate that within the operating range that
if the air pressure increases the Top kerf width and bottom Kerf width will increase by keeping the Nozzle
diameter,NTD,Abrasive mass flow rate as constant (2).When air pressure is increased the jet kinetic energy also increases
which leads to High width of cuts. The concept of increase in air pressure will tend to increase in Metal removal rate is also
applicable in width of cut.

Pressure Vs Top Kerf Width Bottom Kerf Width Pressure Vs Bottom Kerf Width
10 10
Kerf Width
Top 5 5

0 2 46 8 0
0 Pressure (kg/cm2) 0 2 4 68
Pressure (kg/cm2)

Fig 6 :Air pressure versus Top kerf width Fig 7 :Air pressure versus Bottom kerf width

Effect of Standoff Distance on Kerf Width

Standoff distance is the distance between the nozzle and the work piece also called as Nozzle tip distance, during cutting
operation If we keep other operational parameters constant, when standoff distance increases, Top & Bottom Kerf widths
decreases. This is shown in Fig.6&7.However standoff distance on depth of cut is not much influential when compared to
the other parameters which are all considered in this study.

Top Kerf width SOD VsTop Kerf Width Bottom Kerf Width SOD Vs Bottom Kerf Width
10 10

5 5

0 0
0 5 10 15 0 5 10 15
Stand of Distance (mm)
Stand off Distance (mm)

Fig 6 : SOD versus Top kerf width Fig 7 : SOD versus Bottom kerf width

Effect of Abrasive Mass Flow Rate on Kerf Width

Increase in abrasive mass flow rate also increases the depth of cut as shown in Fig. 8,9. This is found by keeping other
parameters like Air Pressure, NTD, and Nozzle diameter as constant. The impact between the abrasive particle and the
material to be machined determines the ability of Abrasive Jet to cut the material. The mass flow rate of the abrasive
particles partially determines the frequency of abrasive grains and partially determines the speed at which they hit. With
the high mass flow rate the kinetic energy of air will influence on particles due to this the width of cut will be more at to p
surface and comparatively less at bottom surface. At low mass flow rate It is assumed that the particles do not collide

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International conference on Emerging Trends in Mechanical Engineering ICEME-2014
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with one another and, hence, they hit the material with a maximum velocity and maximum possible kinetic energy. AS the
Mass flow rate increases some of the abrasive particles will collide with each other, hence, losing kinetic energy and
resulting in lower velocity impacts when hitting the material. If abrasive mass-flow rate goes up the particle velocity goes
down. The Abrasive mass flow rate is dependent on many parameters such as the air pressure, the nozzle diameter, and
the mass-flow rate of air. Mass flow rate of the gas (or air) in abrasive jet is inversely proportional to the mass flow rate of
abrasive particles.

Top Kerf Width 10 MFR Vs Top Kerf Width MFR Vs Bottom Kerf Width
10
5 Kerf Width
Bottom 5
0
0 5 10 0 2 46 8
Mass Flow Rate (g/m) 0 Mass Flow Rate (g/m)

Fig 8 : mass flow rate versus Top kerf width Fig 9 : mass flow rate versus Bottom kerf width

Effect of Nozzle diameter on Kerf Width

With stainless steel nozzle with the tip coated with tungsten carbide, the nozzle life was found to be about 10 hr.
However, increased nozzle life can be obtained with tungsten carbide (12-30 hr) and synthetic sapphire (300 hr) as the
nozzle material. As the nozzle diameter increase the kerf width also increases but the metal removal rate decreases. By
increase in size of the nozzle the abrasive particles mixed with air will spread on more surface of the work. For the
experimentation the nozzles of different hole diameters are used.

Top Kerf Width Nozzle diameter vs Top Kerf Width 6 Bottom Kerf WidthNozzle diameter vs Bottom Kerf Width

10 10
5
0 5
02 4
Nozzle Diameter (mm) 0
0 2 46
Nozzle Diameter (mm)

Fig 10 : Nozzle diameter versus Top kerf width Fig 11 : Nozzle diameter versus Bottom kerf width

CONCLUSION

In the present study experimental investigations have been carried for the depth of cut in abrasive jet drilling of Glass. The
effects of different operational parameters such as Air pressure, SOD, Abrasive mass flow rate, Nozzle diameter have been
studied. As a result of this study, it is observed that these operational parameters have direct effect on Width of cut. It can
also be concluded that abrasive jet machining with Aluminium abrasive is suitable for hard and brittle materials like glass
and fibre glass. It can also be concluded that in processes where material removal is of prime importance there stand off
distance should be kept optimum, abrasive of coarser size should be used and high pressure should be employed. While in
cases where surface finish is of prime importance low standoff distance high pressure and finer abrasive should be used.
Further there is much scope for the researchers to study the effect of process parame ter on other performance measures,
mathematical; regression modelling of this process can be developed With the help of various software’s.

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Fig 12 : Glass specimens which are machined by Abrasive Jet Machining

REFERENCES

1. A. P. Verma and G. K. Lal Publication(1984)“An experimental study of abrasive jet machining”, International Journal of Machine
Tool Design and Research, Volume 24, Issue 1,pp 19-29.

2. U.D.Gulhani,P.P.Patkar,S.P.Patel,A.A.Patel et alAnalysis of AJM parameters on MRR,Kerf width of Hard and Brittle materials like
ceramics – IJDMT-April-2013.

3. M.Roopa Rani & S. Seshan “AJM- Process Variableand Current Applications”, Publication-Journal Metals materials & process 1995Vol.7
No.4PP.279-290.

4. BhaskarChandra and JagtarSingh, “Study of Effect of Process Parameters of Abrasive Jet Machining”, International Journal of Engineering
Science and Technology (IJEST) ISSN: 0975-5462 Vol.3 No.1 Jan2011 Pages 504 -512.

5. Modern Machining Processes, P.C.Pandey & H.S.Shan, Tata Mc Graw-Hill.
6. A.EI-Domiaty, H.M.Abd EI –Hafez, and M.A.Shaker(2009) “Drilling of glass sheets by abrasive jet machining”,World Academy of

Science , Engineering and Technology 56.
7. V. K. Jain, S. K. Choudhury and K. M. Ramesh , On the machining of alumina and glass - International Journal of Machine Tools and

ManufactureVolume 42, Issue 11, September 2002,pages269–1276.
8. V.C.Venkatesh, T.N.Goh,K.H.Wong,M.J.Lim -An empirical study of parameters in Abrasive Jet Machining 1989,Int Journal of Machine

Tools and Mfg.
9. N.S.Pawar,R.R.Lakhe,R.L.Shrivastava-- A comparative Experimental Analysis of Sea sand as an abrasive material using Silicon carbide

and mild steel Nozzle in vibrating chamber of Abrasive Jet machining process IJSRP Vol-3 issue-10,October-13.
10. M.wakuda, Y. Yamauchi and S. kanzaki “Effect of Work Piece Properties on Machinability in Abrasive Jet Machining of Ceramic

Material”, Publication:Precision engineering, Volume 26, issue 2, April 2002, pages 193-198.
11. Sagar, Ankur(2009) Mathematical Modelling Of Abrasive Jet Machining For Metal Removal Rate On Fiber Glass Using Abrasive Silicon

Carbide. 30-Jul-2009.
12. V.C.Venkatesh(1984) Parametric Studies on Abrasive Jet Machining Annals of the CIRP vol33 No 1.page 109.
13. Bhaskar Chandra Kandpal1* Naveen Kumar2 Rahul Kumar3 Rahul Sharma4 Sagar Deswal5 Machining Of Glass And Ceramic With

Alumina And Silicon Carbide In Abrasive Jet Machining , International Journal of Advanced Engineering Technology, IJAET/Vol.II/
Issue IV/October-December, 2011/251-256.

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OPTIMIZATION OF PROCESS PARAMETERS OF

WEDM USING TAGUCHI METHOD

B.S.V. Ramarao

Associate professor in Mechanical Engineering Department, Aurora’s Technological & Research Institute, Hyderabad.

ABSTRACT

In the present scenario the most commonly used materials in the market that are available to produce large number of
components in all sector are EN8 & EN 24. These materials have wide range of desirable properties and applications in Tooling,
Automotive and Defense components. These materials EN8 & EN24 are generally machined using advanced machining processes
such as Non-Conventional machining that include Electro Discharge Machining, Electro Chemical machining, Water Jet machining
etc, rather than regular conventional machining such as Turning, Milling, Grinding etc. Observation of process parameters of
Wirecut EDM while working on EN8 & EN24 are discussed in this paper.

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1. INTRODUCTION

1.1. ELECTRO DISCHARGE MACHINING
Electrical discharge machining, commonly known as EDM, is a process that is used to remove metal through

the action of an electrical discharge of short duration and high current density between the tool and the work piece.
There are no physical cutting forces between the tool and the work piece involved. EDM has proved valuable especially
in the machining of super-tough, electrically conductive materials such as the new space-age alloys. It can be used to
produce parts with intricate shape that is impossible when using conventional cutting tools

This machining process is continually finding further applications in the metal machining industry. It is being used
extensively in the plastic industry to produce cavities of almost any shape in metal moulds. Other applications are also
included such as producing critical parts for aerospace, electronics and medical industries. Although the application of
EDM is limited to the machining of electrically conductive work piece materials, the process has the capability to cut these
materials regardless of their hardness or toughness.

Wear of electrodes by spark discharges is an important aspect of electrical discharge machining (EDM). Wear
rates on both tool and electrodes are dependent on several complex and intricate variables, the most important of which
are probably the energy, polarity and duration of the pulse and the thermophysical properties of the electrode material
and the dielectric fluid. The cleanliness of the dielectric fluid, the type of pulse-forming circuit and the control mechanism
also affect electrode wear, but to a lesser extent. For most metals, the volume removed from the electrodes is generally
proportional to the pulse energy over a fairly wide range. Short pulses produced by a relaxation circuit erode the anode at
a faster rate than the cathode. In pulse generators (semiconductor type) the workpiece is usually connected to the anode
but in some cases, particularly for very low tool wear on graphite tools, the polarity is reversed . However, for a particular
pulse generator, control mechanism and dielectric fluid, electrode erosion is mainly determined by the pulse energy and
the thermophysical properties of the electrode material.

The electrical discharge machining (EDM) works on the principle of erosion of metals by spark discharges. The
EDM is one of the most accurate manufacturing processes available for creating simple or complex shapes and the
geometries within parts and assemblies of extremely hard materials (fragile) that are difficult to machine using
conventional methods, as it works using electrical energy turned to thermal energy rather than cutting. Consecutively,
thousands of sparks per second are generated and each spark produces a tiny crater, in the material along the cutting path
by melting and vaporization, thus eroding the workpiece to the shape of the tool. The dielectric (nonconducting) fluid
flushes out the chips and confines the spark. Each spark produces a temperature between 8,000°C and 12,000°C or as high
as 20,000°C. The size of microcrater depends on energy turned out by the spark generator pulsating direct current at
20,000– 30,000 Hz.

1.2. MATERIAL REMOVAL MECHANISM OF EDM
The first serious attempt of providing a physical explanation of the material removal during electric discharge

machining is perhaps that of Van Dijck. Van Dijck presented a thermal model together with a computational simulation to
explain the phenomena between the electrodes during electric discharge machining. However, as Van Dijck himself
admitted in his study, the number of assumptions made to overcome the lack of experimental data at that time was quite
significant.

Further models of what occurs during electric discharge machining in terms of heat transfer were developed in
the late eighties and early nineties, including an investigation at Texas A&M University with the support of AGIE, now
Agiecharmilles. It resulted in three scholarly papers: the first presenting a thermal model of material removal on the
cathode, the second presenting a thermal model for the erosion occurring on the anode and the third introducing a model
describing the plasma channel formed during the passage of the discharge current through the dielectric liquid. Validation
of these models is supported by experimental data provided by AGIE.

These models give the most authoritative support for the claim that EDM is a thermal process, removing material
from the two electrodes because of melting and/or vaporization, along with pressure dynamics established in the spark -
gap by the collapsing of the plasma channel. However, for small discharge energies the models are inadequate to explain
the experimental data. All these models hinge on a number of assumptions from such disparate research areas as
submarine explosions, discharges in gases, and failure of transformers, so it is not surprising that alternative models have
been proposed more recently in the literature trying to explain the EDM process.

Among these, the model from Singh and Ghosh reconnects the removal of material from the electrode to the
presence of an electrical force on the surface of the electrode that could mechanically remove material and create the
craters. This would be possible because the material on the surface has altered mechanical properties due to an increased
temperature caused by the passage of electric current. The authors' simulations showed how they might explain EDM
better than a thermal model (melting and/or evaporation), especially for small discharge energies, which are typically used
in μ-EDM and in finishing operations.

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Given the many available models, it appears that the material removal mechanism in EDM is not yet well
understood and that further investigation is necessary to clarify it, especially considering the lack of experimental scientific
evidence to build and validate the current EDM models. This explains an increased current research effort in related
experimental techniques.
1.3. CLASSIFICATION OF EDM
EDM process is broadly classified into 2 categories:

Sinker EDM

Wire EDM
1.3.1. WIRE EDM

Wire electrical discharge machining (WEDM) is a specialized thermal machining process capable of accurately
machining parts with varying hardness or complex shapes, which have sharp edges that are very difficult to be machined
by the main stream machining processes. This practical technology of the WEDM process is based on the conventional
EDM sparking phenomenon utilizing the widely accepted non-contact technique of material removal. Since the
introduction of the process, WEDM has evolved from a simple means of making tools and dies to the best alternative of
producing micro-scale parts with the highest degree of dimensional accuracy and surface finish quality. Some of the
common applications

of WEDM are including the fabrication of the stamping and extrusion tools and dies, fixtures and gauges, prototypes,
aircraft and medical parts and grinding wheel form tools. However, further improvements are still required to meet the
increasing demand of product precision and accuracy in those sectors of manufacturing. Recently, this process is
significantly used in producing punches, mould and dies which required high quality and accuracy of product s and
components for its applications. Therefore, the results in achieving good surface quality of EDMed surface have become
the most desirable performances in this process.

The material removal mechanism of WEDM is very similar to the conventional EDM process involving the erosion
effect produced by the electrical discharges (sparks). In WEDM, material is eroded from the work piece by a series of
discrete sparks occurring between the work piece and the wire separated by a stream of dielectric fluid, which is
continuously fed to the machining zone. In case of WEDM, a wire electrode is trailing vertically through the workpiece
which usually is fed horizontally. This process utilizes a continuously traveling wire electrode made of thin copper, brass or
tungsten of diameter 0.05 – 0.3 mm, which is capable of achieving very small corner radii. The wire is kept in tension using
a mechanical tensioning device reducing the tendency of producing inaccurate parts. During the WEDM process, the
material is eroded from the workpiece by a series of discrete sparks, ahead of the wire. A varying degree of taper ranging
from 15° for a 100 mm thick to 30° for a 400 mm thick workpiece can be obtained on the cut surface material. The
microprocessor which is used to continuously feed thin wire will constantly maintains the gap between the wire and the
workpiece, which usually varies from 0.025 to 0.05 mm. In WEDM process, there is no direct contact between the
workpiece and the wire thus, eliminating the mechanical stresses during machining the work materials. The electrode
used in this process is renewed constantly in order to avoid rapture (Figure).

Figure: (a) Conventional wire EDM. (b) Close-up view of the gap and continuous electrical sparks.
The advantages of this process are the wire used is cheaper than the complex electrodes used in die sinking

electric discharge machining, less cost of machining are required due to no electrode wear occurred and very little wastes
of workpiece material is provided. Nevertheless, the drawbacks of this operation are it is possible only for ruled surfaces
and the wire may bend or break during machining, which cause substantial shape errors and drastically reducing the
efficiency and accuracy of the WEDM operation. Therefore, today’s WEDM process is commonly conducted on

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workpieces that are totally submerged in a tank filled with dielectric fluid. A submerged method of WEDM is used as it is
promotes temperature stabilization and efficient flushing especially in cases where the workpiece has varying thickness.

Although WEDM can be conducted in both methods such as submerged in tank filled with dielectric or dry
machining, what are the most matters is to get the good quality of machined surface and dimensional accura cy. Regarding
this reason, a significant amount of research has explored the different methodologies of achieving the ultimate WEDM
goals of optimizing the numerous process parameters analytically with the total elimination of the wire breakages thereby
also improving the overall machining reliability.

2. LITERATURE REVIEW

Most of the researchers studied Wire EDM by experiments or developments.
In 1991 Williams and Rajurkar [1] observed that the complex and random nature of the erosion process in WEDM

requires the application of deterministic as well as stochastic techniques. Surface roughness profiles were studied with a
stochastic modeling and analysis methodology to better understand the process mechanism. Scanning electron
microscopic (SEM) examination highlighted important features of WED machined surfaces.

In 1995 Speeding and Wang [2] modelling the WEDM process through Response Surface Methodology and
Artificial Neural Networks. A response surface model based on a central composite rotatable experimental design, and a
back-propagation neural network has been developed. The pulse-width, the time between two pulses, the wire
mechanical tension and the injection set-point are selected as the factors (input parameters), whilst the cutting speed, the
surface roughness and the surface waviness are the responses (output parameters).

The two models are compared for goodness of fit. J.T. Huang et al. [3] found the role of WEDM in precision
manufacturing. To obtain a precise work-piece with good surface quality, some extra repetitive finish cuts along the rough
cutting contour are necessary. An attempt has been made to unveil the influence of the machining parameter (pulse -on
time, pulse-off time, table feed-rate, flushing pressure, distance between wire periphery and work-piece surface, and
machining history) on the machining performance of WEDM in finish cutting operations. Their research shows that the
proposed approach can achieve better performance than that achieved by a well-skilled operator. A better surface quality
and accurate dimension value can be obtained in less machining time.

Speeding and Wang [4] revealed that WEDM technology has been widely used in conductive material machining.
The WEDM process, which is a combination of electro-dynamic, electromagnetic, thermal-dynamic, and hydrodynamic
actions, exhibits a complex and stochastic nature. Its performance, in terms of surface finish and machining productivity, is
affected by many factors. In this an attempt at optimization of the process parametric combinations by modeling the
process using artificial neural networks (ANN) and characterizes the WEDMedsurface through time series techniques.

Kozak et al. [5] experimented that WEDM of low conductive materials demonstrates that total electrical
resistance between the work-piece and wire electrode vary during machining depending upon the clamping position. This
change in resistance causes a change in material removal rate (MRR) and average surface roughness that leads to poor
quality of products. A technique developed in this work minimizes the change in resistance offered by the work -piece
material. A conductive silver coating is applied over the workpiece surface. Due to silver coating, the drop voltage in work-
piece material is reduced; thereby decreasing energy loss in work-piece material. It was also observed that conductive
silver coating not only minimizes the variation in resistance but also it increases the productivity of the process.

Tosun et al. [6] studied the variation of work-piece surface roughness with varying pulse duration, open circuit
voltage, wire speed and dielectric fluid pressure was experimentally investigated in WEDM. Brass wire with 0.25 mm
diameter and SAE 4140 steel with 10 mm thickness were used as tool and work-piece materials in the experiments,
respectively. It is found experimentally that the increasing pulse duration, open circuit voltage and wire speed, increase
the surface roughness whereas the increasing dielectric fluid pressure decreases the surface roughness. The var iation of
work-piece surface roughness with machining parameters is modelled by using a power function. The level of importance
of the machining parameters on the work-piece surface roughness is determined by using analysis of variance (ANOVA).

Puertas and Luis [7] experiment on the optimum selection ofmanufacturing conditions is very important in
manufacturing processes as these ones determine surface quality anddimensional precision of the so -obtained parts.
Thus, it is necessary to know, in advance, properties relating to surface quality and dimensional precision by means of
theoretical models which allow doing some predictions taking into account operation conditions such as gap, dielectric
fluid, penetration speed, etc. Manufacturing materials with non-conventional processes such as electrical discharge
machining, shows really important aspects to study from the point of view of materials science, heat transmission,
mechanics and manufacturing processes optimization. The study is mainly focused on aspects related to surface quality
and dimensional precision, which are one of the most important parameters form the point of view of selecting the
optimum conditions of processes, as well as economical aspects. Functions making it possible to optimize paramet ers
related to surface quality in such manufacturing processes will be obtained by means of using mathematical models that
will allow us to select the optimum manufacturing conditions.

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S. Sarkar et al. [8] presented an investigation on WEDM of -titanium aluminide alloy. An extensive research study
has been carried out with an aim to select the optimum cutting condition with an appropriate wire offset setting in order
to get the desired surface finish and dimensional accuracy. The process has been modeled using additive model in order to
predict the response parameters i.e. cutting speed, surface finish and dimensional deviation as function of different
control parameters and the main influencing factors are determined for each given machining criteria. Fi nally, the
optimum parametric setting for different machining situation arising out of customer requirements have been synthesized
and reported.

El-Taweel et al. [9] reveals that WEDM allowed success in the production of newer materials, especially for the
aerospace and medical industries. Using WEDM technology, complicated cuts can be made through difficult -to-machine
electrically conductive components. The high degree of the obtainable accuracy and the fine surface quality make WEDM
valuable. The right selection of the machining conditions is the most important aspect to take into consideration in
processes related to the WEDM of Inconel 601 material. Their work highlights the development of mathematical models
for correlating the inter-relationships of various WEDM machining parameters of Inconel 601 material such as: peak
current, duty factor, wire tension and water pressure on the metal removal rate, wear ratio and surface roughness. This
work has been established based on the response surface methodology (RSM).

Shajan Kuriakose and M.S. Shunmugam [10] reported that WEDM is one of the important non -traditional
machining processes, which is used for machining of difficult-to-machine materials and intricate profiles. Being a complex
process, it is very difficult to determine optimal parameters for improving cutting performance. Cutting velocity and
surface finish are most important output parameters, which decide the cutting performance. There is no single optimal
combination of cutting parameters, as their influences on the cutting velocity and the surface finish are quite the opposite.
In their work, a multiple regression model is used to represent relationship between input and output variables and a
multiobjective optimization method based on a Non-Dominated Sorting Genetic Algorithm (NSGA) is used to optimize
Wire-EDM process.

K. Kanlayasiri, S. Boonmung [11] developed cold die steel (DC53) from Daido Steel, Japan. It is an improvement
over the familiar cold die steel SKD11. They investigate the effects of machining variables on the surface roughness of
wire-EDMed DC53 die steel. Analysis of variance (ANOVA) technique was used to find out the variables affecting the
surface roughness. Results from the analysis show that pulse-on time and pulse-peak current are significant variables to
the surface roughness of WEDMed DC53 die steel. The surface roughness of the test specimen increases when these two
parameters increase.

In 2009 Singh and Garg [12] found that the material removal rate (MRR) directly increas es with increase in pulse
on time and peak current while decreases with increase in pulse off time and servo voltage. They used hot die steel (H -11)
as work-piece.
In 2011 Natarajan et al. [13] focuses RSM for the multiple response optimization in micro-endmilling operation to achieve
maximum metal removal rate (MRR) and minimum surface roughness. Aluminium block of 60×40×16 mm is used as the
workpiece material and carbide endmill cutter of diameter 1 mm as the cutting tool.

In 1999 Liao et al. [14] studied the geometry properties of WEDM process in corner cutting. The concept of
discharge-angle is introduced, and its mathematical expression is derived by analytical geometry. A model to estimate the
metal removal rate (MRR) in geometrical cutting is developed by considering wire deflection with transformed exponential
trajectory of wire centre. The computed MRR is compared with measured sparking frequency of the process since they are
equivalent to each other for an iso-energy type machine. Both of the discharge-angle and MRR drop drastically to a
minimum value depending on the corner angle being cut as the guides arrive at the corner apex, and then recover to the
same level of straight-path cutting sluggishly.

In 2002 Tosun and Cogun [15] studied the effect of cutting parameter on wire electrode wear experimentally in
Wire electric discharge machining. The experiment was conducted under different setting of pulse duration, open circuit
voltage, wire speed and dielectric fluid pressure. Brass wire of 0.25mm diameter and AISI 4140 steel of 10 mm thickness
were used as tool and work piece. It is found experimentally that increasing the pulse duration and open circuit voltage
increase the wire wear ratio (WWR) whereas increasing the speed decreases it. The variation of work-piece material
removal rate and average surface roughness also investigated in relation of WWR.

3. DESIGN OF EXPERIMENTS

The experimentation is carried out based on the scientifically approach. The Experiments were designed according to
TAGUCHI’s Method by using L18 Orthogonal Array approach.

Taguchi’ Method is helpful to design 18 number of Experiments based on the L18 Orthogonal Array approach, with
different combinations of parameters for each Experiment.

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3.1. CONSTANT PARAMETERS

Pulse on time, Discharge current, Spark gap, Wire diameter, Pulse off time, Amperes off, Amperes on, Material thickness,

Duty factor, Gap current and Wire speed are the parameters that are considered as constants during the experiment on

WEDM.

Initially Wire Diameter has been considered as variable, but when approached to the industry in practical, the standard

wire diameter being used is 0.25mm, and there is no availability of lesser diameters. Lesser wire diameters say 0.15mm
& 0.20mm is machined only for specific jobs in very rare conditions.

0.25mm wire diameter is widely used in the market due to its wide range of advantages.

a. Can withstand tension to a greater limit,

b. No breakage of wire occurs while machining,

c. Any thickness of work material can be machined,

d. Economically lower price,

e. Easily available in the market.

3.2. VARIABLE PARAMETERS

Wire feed rate, Wire tension, Peak current, Water pressure & Servo voltage are the parameters that are

considered as Variables during the experiment on WEDM.

3.3. MATERIAL SELECTION

Work materials selected for the study are EN-8 & EN-24.Because of their wide range of desirable properties and

applications in Tooling, Automotive and Defence components.

EN8 is usually supplied untreated but can be supplied to order in the normalized or finally heat treated (quenched

and tempered to "Q" or "R" properties for limiting ruling sections up to 63mm), which is adequate for a wide range of

applications.

EN8 is a very popular grade of through-hardening medium carbon steel, which is readily machinable in any condition.

(Refer to our machinability guide). EN8 is suitable for the manufacture of parts such as general-purpose axles and shafts,

gears, bolts and studs. It can be further surface-hardened typically to 50-55 HRC by induction processes, producing

components with enhanced wear resistance. For such applications the use of EN8D (080A42) is advisable. It is also

available in a free-machining version, EN8M (212A42)

EN8 in its heat treated forms possesses good homogenous metallurgical structures, giving consistent machining

properties. Good heat treatment results on sections larger than 63mm may still be achievable, but it should be noted that

a fall-off in mechanical properties would be apparent approaching the centre of the bar. It is therefore recommended that

larger sizes of EN8 are supplied in the untreated condition, and that any heat treatment is carried out after initial stock

removal. This should achieve better mechanical properties towards the core.

080M40(EN8)

Carbon 0.36-0.44%

Silicon 0.10-0.40%

Manganese 0.60-1.00%

Sulphur 0.050 Max

Phosphorus 0.050 Max

Chromium -

Molybdenum -

Nickel -

080M40 (EN8) - mechanical properties in "R" condition
700-850 n/mm2
Max Stress 465 n/mm2 Min (up to 19mm LRS)

Yield Stress
0.2% Proof Stress 450 n/mm2 Min(up to 19mm LRS)

Elongation 16% Min (12% if cold drawn)

Impact KCV 28 Joules Min(up to 19mm LRS)

Hardness 201-255 Brinell

EN8 Equivalents

BS970: 1955 EN8

BS970/PD970: 080M40
1970 onwards

European C40, C45, Ck40,Ck45, Cm40, Cm45

Werkstoff No. 1.0511, 1.1186, 1.1189

US SAE (AISI) 1039, 1040, 1042, 1043, 1045

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EN24 is usually supplied in the finally heat treated condition (quenched and tempered to "T" properties) up to a limiting
ruling section of 250mm, which is superior to grades 605M36, 708M40 or 709M40 - see properties below. Please refer to
our selection guide for comparisons.

EN24 is a very popular grade of through-hardening alloy steel, which is readily machinable in the "T" condition. T is most
suitable for the manufacture of parts such as heavy-duty axles and shafts, gears, bolts and studs. EN24T can be further
surface-hardened typically to 58-60 HRC by induction or nitride processes, producing components with enhanced wear
resistance.

In addition to the above, EN24T is capable of retaining good impact values at low temperatures, hence it is frequently
specified for harsh offshore applications such as hydraulic bolt tensioners and shipborne mechanical handling equipment.

EN24 sections larger than 250mm may still be available in the quenched and tempered condition, but it should be
noted that a fall-off in mechanical properties may be apparent approaching the centre of the bar. It is therefore
recommended that larger sizes are supplied in the annealed (softened) condition, and that quenching and tempering is
carried out after initial stock removal. This should achieve better mechanical properties towards the core.

817M40(EN24) 0.36-0.44%
Carbon 0.10-0.35%
Silicon 0.45-0.70%
Manganese 0.040 Max
Sulphur 0.035 Max
Phosphorus 1.00-1.40%
Chromium 0.20-0.35%
Molybdenum 1.30-1.70%
Nickel

817M40 (EN24) - mechanical properties in "T"

condition

Max Stress 850-1000 n/mm2
Yield Stress 680 n/mm2 Min
(up to 150mm LRS)
Yield Stress 650 n/mm2 Min
0.2% Proof Stress (over 150 to 250mm LRS)
665 n/mm2 Min
(up to 150mm LRS)

0.2% Proof Stress 635 n/mm2 Min
(over 150 to 250mm LRS)

Elongation 13% Min
(9% if cold drawn)

Impact KCV 50 Joules Min
(up to 150mm LRS

Impact KCV 35 Joules Min
(over 150 to 250mm LRS)

Hardness 248-302 Brinell
(850-1000 n/mm2)

EN24 Equivalents

BS970: 1955 EN24

BS970/PD970: 817M40
1970 onwards

European 34CrNiMo6

Werkstoff No. 1.6582

US SAE (AISI) 4340

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3.4. TAGUCHI’S METHOD

Using Taguchi’s method there requires Levels of the parameters to be defined. These levels should be taken such that
these are the most common working values during the running of the machine. The following table shows the le vels that
are considered for the Experiment.

LEVELS

S.No. PARAMETERS 1 23

1 Wire feed rate 2 34

2 Wire tension 1.2 1.3 1.4

3 Water flushing pressure 2 34

4 Peak current 40 45 50

5 Servo voltage 7 89

6 Work piece EN8 EN24 -

3.5. L18 ORTHOGONAL ARRAY APPROACH

Using L18 Orthogonal Array Approach as per Taguchi’s Method is helpful to design 18 number of Experiments, with
different combinations of parameters for each Experiment.
Accordingly ORTHOGONAL ARRAY is designed using TAGUCHI’s Method as shown below

Exp No Wire Feed Wire Tension Wire Flushing Peak Current Servo voltage Work piece
m/min Kg-cm Pressure amps v

1 2.0 1.2 2 40 7

2 2.0 1.3 3 45 8

3 2.0 1.4 4 50 9

4 3.0 1.2 2 45 8

5 3.0 1.3 3 50 9

6 3.0 1.4 4 40 7

EN8

7 4.0 1.2 3 40 9

8 4.0 1.3 4 45 7

9 4.0 1.4 2 50 8

10 2.0 1.2 4 50 8

11 2.0 1.3 2 40 9

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12 2.0 1.4 3 45 7

13 3.0 1.2 3 50 7

14 3.0 1.3 4 40 8 EN24

15 3.0 1.4 2 45 9

16 4.0 1.2 4 45 9

17 4.0 1.3 2 50 7

18 4.0 1.4 3 40 8

Using the above L18 ORTHOGONAL ARRAY, 18 experiments with different combinations of parameters are designed.

3.6. DATA COLLECTION & ANALYSIS

18 Experiments are conducted with different combinations based on the Orthogonal Array using Taguchi’s method
designed experiments.

Accordingly cycle time for each experiment is calculated with Area of the material being machined.

Thus cycle times as against the experiment are taken and Material Removal Rate is calculated as shown in the
table. EXPERIMENTAL DATA ANALYSIS

Exp Wire Wire Wire Peak Servo Cycle MRR
No Feed Tensio Flushing Current voltage time mm3/sec
Pressure sec
WORK m/min n v 300.0 1.47
PIECE 274.8 1.61
Kg-cm amps 240.0 1.84
240.0 1.84
1 2.0 1.2 2 40 7 240.0 1.84
2 234.6 1.88
3 2.0 1.3 3 45 8 246.0 1.79
4 228.0 1.93
5 2.0 1.4 4 50 9 255.6 1.73
6 249.0 2.36
7 3.0 1.2 2 45 8 270.0 2.18
EN8
8 3.0 1.3 3 50 9

9 3.0 1.4 4 40 7
10
11 4.0 1.2 3 40 9

4.0 1.3 4 45 7

4.0 1.4 2 50 8

2.0 1.2 4 50 8

2.0 1.3 2 40 9

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12 2.0 1.4 3 45 7 264.6 2.22
50
13 3.0 1.2 3 40 7 259.8 2.26
45
EN24 14 3.0 1.3 4 45 8 258.0 2.28
50
15 3.0 1.4 2 40 9 261.6 2.25

16 4.0 1.2 4 9 249.6 2.35

17 4.0 1.3 2 7 283.8 2.07

18 4.0 1.4 3 8 268.8 2.19

4. RESULT

Maximum MRR is achieved at experiment no. 8 for en8 (1.93mm3/sec) & experiment no. 10 for en24 (2.36mm3/sec) with
the process parameters as mentioned above.

REFERENCE

[1] R. E. Williams, K. P. Rajurkar, “Study of wire electrical discharge machined surface characteristics,” Journal of Materials Processing
Technology, vol. 28, pp. 127-138, 1991.
[2] T. A. Spedding, Z. Q. Wang, “Parametric optimization and surface characterization of wire electrical discharge machining process,”
Precision Engineering, vol. 20, pp. 1-15, 1997.
[3] J.T. Huang, Y.S. Liao, W.J. Hsue, “Determination of finish-cutting operation number and machining-parameters setting in wire electrical
discharge machining,” Journal of Materials Processing Technology, vol. 87, pp. 69–81, 1999.
[4] T.A. Spedding, Z.Q. Wang, “Study on modeling of wire EDM process,” Journal of Materials Processing Technology, vol. 69, pp. 18- 28,
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aluminide alloy,” Journal of Materials Processing Technology, vol. 159, pp. 286–294, 2005.
[9] M.S. Hewidy, T.A. El-Taweel, M.F. El-Safty, “Modelling the machining parameters of wire electrical discharge machining of Inconel 601
using RSM,” Journal of Materials Processing Technology, vol. 169, pp. 328–336, 2005.
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Genetic Algorithm,” Journal of Materials Processing Technology, vol. 170, pp. 133–141, 2005.
[11] K. Kanlayasiri, S. Boonmung, “Effects of wire-EDM machining variables on surface roughness of newly developed DC 53 die steel:
Design of experiments and regression model,” Journal of Materials Processing Technology, vol. 192-193, pp. 459-464, 2007.
[12] H. Singh, R. Garg, “Effects of process parameters on material removal rate in WEDM,” Achievements in Materials and Manufacturing
Engineering, vol. 32, pp.70-74, 2009.
[13] U. Natarajan, P.R. Periyanan, S. H. Yang, “Multiple-response optimization for micro endmilling process using response surface
methodology,” Int J Adv Manuf Technol, vol. 56, pp. 177–185, 2011.
[14] W.J. Hsue, Y.S. Liao, S.S. Lu, “Fundamental geometry analysis of wire electrical discharge machining in corner cutting,” International
Journal of Machine Tools & Manufacture, vol. 39, pp. 651–667, 1999.
[15] N. Tosun, C. Cogun, “An Investigation of Wire Wear in WEDM,” Journal of Materials Processing Technology, vol.134, pp. 273–278,
2003.

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OPTIMIZATION OF PROCESS PARAMETERS IN

THE PRODUCTION OF STEEL THROUGH THE

APPLICATION OF SMART QUALITY

ENGINEERING TECHNIQUES

Allurkar Baswaraj.S

Dr. M. Sreenivasa Rao

D.Venkata Sreekanth

Author Details (optional)

Asst. Professor, Department of Mechanical Engineering, M.B.E.Society’s, College of Engineering, Ambajogai. Dt: Beed (M.S) – 431 517, India. E-

mail: [email protected]
Professor, Department of Mechanical Engineering, JNTU Collelge of Engineering, Kukatpally, Hyderabad (A.P) - 500 85, India, E-
mail: [email protected]
Assoc. Professor, Department of Mechanical Engineering,Abhinav Hi-Tech Engineering College, Hyderabad (A.P )– 500 075, E-
mail: [email protected]

KeyWords

ANN, EAF, Neural network, optimization,scrap recycling, steel bars, taguchi.

ABSTRACT

Steel Refining is playing a very major role in current and modern steel works.However, the difficulty is in making optimal settings
in process parameters, which if not fine tuned will result into defects.This area has received huge attention from engineers and
academicians in the recent time. There are several factors like increased complexity of the sytem, increased quality
requirements, increased cost of the resources. The Secondary steel manufacturing process is very complex and therefore, it is
very hard to develop a comprehensive model involving all parameters. In order to survive in the increasingly customer-oriented
marketplace, continuous quality improvement marks the fastest growing quality organization’s success. In recent years,
attention has been focused on intelligent systems which have shown great promise in supporting quality control. This work aims
at proposing an intelligent system to optimize the process parameters influencing the Quality of TMT steel bars in a
manufacturing and processing industry to overcome the challenges of demanding customers who seek high level quality and
low-cost products. It is suggested that, the usage of the Artificial Neural Networks may ultimately aid in the abolition of the
manual regulatory control being carried out by the operators. Manual regulatory control is totally subservient to human
cognition which has the potential to produce erroneous decisions. The Artificial intelligence techniques adopted reduces the
reliance on human expert knowledge enabling in a way to run the process with less experienced operators and supervisors. Not
only the delight of creating products with a wide variety of additional values must be considered, but also the emission of CO2,
the discharge of harmful wastes during manufacturing, the influences of the use of machines and equipment, recycling, the
limitation of waste disposal, and many other factors, must be evaluated comprehensively.

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INTRODUCTION

The manufacture of steel delivers the goods and services that our societies need – healthcare, telecommunications,
improved agricultural practices, better transport networks, clean water and access to reliable and affordable energy. the
recycling of iron and steel scrap (ferrous scrap) is an important activity worldwide. iron and steel products are used in
many construction and industrial applications, such as in appliances, bridges, buildings, containers, highways, machinery,
tools, and vehicles [14]. because it is economically advantageous to recycle iron and steel by melting and recasting into
semi finished forms for use in the manufacture of new steel products, a significant industry has developed to collect old
scrap (used and obsolete iron and steel products), and new scrap (the ferrous scrap generated in steel mills and steel -
product manufacturing plants). the use of scrap metal has become an integral part of the modern steelmaking industry,
improving the industry’s economic viability and reducing environmental impact. Furthermore, the iron and steel industry
is capital as well as energy intensive. the importance of effective process control in this industry on cost and energy
reduction and environment protection is by no means less than that in other industries. compared to ore extraction, the
use of secondary ferrous metals significantly reduces co2 emissions, energy and water consumption and air pollution. at
the same time, the recycling of steel makes more efficient use of the earth’s natural resources.

LITERATURE SURVEY

Thorough Literature review suggests production of steel from mineral ore is very energy - intensive and thus expensive,
with in the Indian economy and is therefore of particular interest in the context of both local and global environmental
discussions. 2,500 pounds of iron ore, 1,000 pounds of coal and 40 pounds of limestone are saved when one ton of steel is
recycled. As per Koe Nakajima [9] 3000 pounds of iron ore is saved when one ton of steel is recycled. S crap metal
recycling saves us a lot of iron as well as coal. As measured by recycled metal as a percent of apparent metal supply for
each metal, lead was the most recycled metal 65% followed by iron and steel at 60%.

Increase in productivity through the adoption of more efficient and cleaner technologies in the manufacturing sector will
be effective in merging economic, environmental and social development objectives. Steel manufacturing is an intensely
competitive global industry. By continually improving its manufacturing processes and consolidating businesses, the steel
industry has increased productivity sufficiently to remain competitive in the global market.

Fig. 1. Percentage of Components in Automobile Recycling

The important parameters include optimum mixing of alloying elements like carbon, manganese, Phosphorous, silicon
etc., The common quality problems observed are porosity, hairline cracks, blowholes, rusting and corrosion etc., Recycling
in general and steel recycling in particular, many believe, enjoys a very bright future. After all, recycled or secondary steel
competes with primary production. It is also to the benefit of secondary production that government policies are starting
to force mining companies and metallurgical plants to pay the full environmental costs of their operations.These factors
will make recycling more competetive, and over time the relative importance of secondary steel production to society will
grow. The importance of steel recycling has traditionally been emphasised mainly for economical reasons. The recycling of
steel scrap from used products is generally much more involved.

STEEL PRODUCTION IN THE WORLD

A more common way of recycling such secondary scrap, however, is to re-melt it and use it as a base material. In
particular when highly contaminated or blended scrap materials are processed, a reclamation process that is capable of
maintaining a constant high quality of steel is required in order to obtain a base alloy of acceptable chemical
specificationunderline.

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RECYCLING ISSUES

Steel chemical composition. The main parameters to be controlled are the amount of Carbon, Manganese,
Phosphorous and Silicon in the steel
Operating environment

Entrapment of inclusions during fluid flow, segregation, and
solidification Re-oxidation of the molten slag entrapment.

Deoxidization – Removal of oxygen
Degassing – Removal of hydrogen

Desulphurization – To keep sulphur concentrations as low as 0.002%
Micro-cleanliness – Removal of undesirable nonmetallic elements

Inclusion morphology – Changing the composition of remaining impurities to improve the microstructure of the
steel

Most of the parameters are inter-dependent and are constantly in conflict in a complex
way. Recycling-related contributions include the development of:

(i) Scrap recovery and re-use technologies; (ii) By-product recovery and re-use technologies;
Recycling of used materials plays an important role in the solution of environmental-and energy-problems. Recycling of
used materials is very important because of its direct effect of reducing the amount of waste and its indirect effect of
reducing environmental loads through the cut-back of energy consumption in production processes as scrap (the

recycling ratio) averages approx. 75%.On the other hand, increased use of scrap has a desirable effect on the reduction of
carbon-dioxide emission as the melting of scrap consumes only approximately one-third of the energy consumed in the
preparation of molten iron from iron ores. As such, there is a pressing need to develop efficient technologies for the
manufacture of high-quality steel from scrap. Many technical innovations related to scrap recycling have been made
since the 1970s, these include improvements in electric furnaces, the development of continuous casters for mini mills,
and advances in quality improvement and secondary refining technologies.

SECONDARY STEEL MANUFACTURING PROCESS

Although iron and steel is one of the most important industries in the Indian manufacturing sector, India is only the
15th largest steel producer in the world. Secondary steel is produced in an electric arc furnace (EAF) or in an induction
furnace (IF) using scrap. Induction furnaces are very unique to India. The secondary steel industry includes so -called
“mini-mills”, which make relatively simple products from low-priced scrap. In secondary steel production, the scrap is
melted and refined, using a strong electric current. Steel making based on external scrap (scrap from outside the steel
sector) requires less than half as much primary energy as steel made from ore. The units producing secondary steel are
usually relatively small of size. In the basic oxygen furnace, the iron is combined with varying amounts of steel scrap (less
than 30%) and small amounts of flux. A lance is introduced in the vessel and blows 99% pure oxygen causing a
temperature rise to 1700°C. The scrap melts, impurities are oxidized, and the carbon content is reduced by 90%, resulting
in liquid steel. Other processes can follow – secondary steel-making processes – where the properties of steel are
determined by the addition of other elements, such as boron, chromium and molybdenum, amongst others, ensuring the
exact specification can be met. Steel is 100% recyclable. Steel production is a distributed process, involving different
problem solving methods, requiring integration between different steps. To achieve desired objectives, one needs to
make decisions based on alloy and slag additions, stirring, vacuum degassing and reheating. The mixing process of
materials is controlled in order to maintain uniform physical and chemical properties of products. The sequencing, timing
and quantity of additions and applications of stirring, degassing and/or reheating practices will be critical to the successful
completion.

AIMS AND OBJECTIVES OF THE ANALYSIS

Jalna, a place nearby to Aurangabad, in Maharashtra is very famous for recycling of scrap steel. There are nearly 25
small and large scale steel manufacturing plants from scrap. The steel manufactured is supplied around 500 Km. radius on
a large scale and to other parts of our country also. The work is focused on increasing the quality of steel produced. Hence,
it requires keenly understanding the available Processes used. By using different Quality and reliabilit y techniques,
optimization of the parameters, identify and quantify the inefficiencies of existing technologies already in use and
processes in selected method.

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High light the weaknesses. Investigation of various parameters in Steel manufacturing and their relative effect on
the performance. Fine tuning and stream lining of affecting variables.

Thorough Analysis of Quality of the result by using ANOVA and Taguchi techniques. Suggest the alternatives
available.

Selecting the best combination of alternatives and arriving at the best optimum combination of parameters.
Development of an intelligent quality management system using artificial intelligence.

To make fine adjustments to the steel composition alloy additions to the crude steel to adjust composition for the
grade of steel being manufactured.
The goal of the work is to precisely optimize various process parameters and other factors to get required grade
of steel.

METHODOLOGY

The below mentioned techniques can be used to ascertain the Optimization of Process parameters in making steel
bars for better quality.

Design of Experiment:
Design of experiments (DOE) or experimental design is the design of any information-gathering exercise where
variation is present, whether under the full control of the experimenter or not. By design of experiment robust design
will be created. Robust design is an engineering methodology for optimizing product and process conditions which are
minimally sensitive to the various causes of variation. Two major tools used in robust design are:

Signal-to-noise ratio, which measures quality with emphasis on variation.
Orthogonal array, which accommodate many design Factors (parameters) simultaneously.

Artificial intelligence:
Neural Network system: The key to development of an artificial intelligent system in the domain of engineering
design and group technology is its ability to store a large set of patterns as memories that can be recalled. Neural
networks are a family of artificial intelligence and can be defined as ‘massive-parallel interconnected networks of
simple elements and their hierarchical organizations which are interconnected to interact with objects of the real
world in the same way as a biological nervous system does’.
Neural networks have learning accuracy, high recall speed and generalization abilities. They are able to learn the
co-relation between input examples and the expected outcome and more importantly, to generate the
relationship. After training neural networks can generate appropriate outputs in response to new inputs.
This technique greatly reduces the number of experiments and provides superior performance. Researchers have
successfully applied the technique in manufacturing. The application of neural networks in manufacturing is very
broad.
Neural network methodology can be adopted in solving the problems that are cumbersome and intractable with
traditional methods.

Taguchi Technique:
Taguchi analytical methodology is a powerful problem solving tool, which can improve the performance of the
product quality, process design and system with a great decrease in experiment time cost. It consists of quality
loss function concept has been carrying out robust design of processes and products and solving several optimal
problems in Steel manufacturing industries.
This methodology is also used for finding the optimum settings of control factors and making a process insensitive
to noise factors.

CONCLUSION

To summarize in a nut shell, the work is aimed at optimizing the process parameters in the production of steel bars by
using Quality Engineering tools like Design of experiments, Orthogonal arrays and there by prediction of responses
for different set of input parameters through the application of artificial intelligence techniques like neural networks.
The common quality problems faced in secondary steel (Recycling of scrap) production like blow holes, porosity,
hairline cracks, and percentage of ingredients can well be optimized. In order to improve the system capabilities,
these effects have to be studied, and then incorporated in training the system. This will definitely help for steel
manufacturing to achieve higher level quality, there by serving the needs of society in an efficient way.
Although a conclusion may review the main points of the paper, do not replicate the abstract as the conclusion. A

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conclusion might elaborate on the importance of the work or suggest applications and extensions. Authors are strongly
encouraged not to call out multiple figures or tables in the conclusion—these should be referenced in the body of the
paper.

REFERENCES

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The Revolutionary Composite –Carbon Fibre

Reinforced Polymer

Aamer Sohail , J.Sandeep Kumar

Author Details (optional)
Aamer Sohail is currently pursuing masters degree program in MachineDesign at Abhinav Hi-tech College of Engineering, Hyderabad, Andhra
Pradesh, India, PH - +91-9948695704. E-mail: [email protected]

KeyWords

Composite material, Carbon Fiber Reinforced Polymer (CFRP), PAN-based carbon fibers, Pitch based carbon fibers,
polyacrylonitrile, reinforcements, safety equipment

ABSTRACT

This paper is aimed at “Composite Materials” and focuses on a particular type of modern composite made up of carbon
fiber and polymer (Plastic).

Carbon Fiber Reinforced Polymer (CFRP) is a Polymer Matrix Composite material reinforced by carbon fibers. Carbon fiber is
a fiber containing at least 90% carbon. Carbon fibers are used in composites with a lightweight matrix. CFRP is manufactured
by treating organic fibers (precursors) with heat and tension, resulting in a highly ordered 3-D structure. CFRP components
can carry loads only in one direction or in all directions based on their fiber composition. In the early 1960’s successful
commercial production of CFRP started as the requirements of the aerospace industry especially for military aircraft for better
and lightweight materials became essential. . Carbon fiber composites are ideally suited to applications where strength,
stiffness, lower weight, and outstanding fatigue characteristics are critical requirements. They are used in the occasion where
high temperature, chemical inertness and high damping are required also. Recently, carbon fibers have found wide
application in commercial and civilian aircraft, recreational, industrial, and transportation markets.

CFRP is coming into prominence because of its advantages over conventional metals/alloys. These days there is a serious
requirement for a composite like this, especially in the automotive field. Once the cost of this material descends, due to further
improvements in manufacturing processes and material science; it is hoped that it may replace the alloys & heavy metals
widely, the reason being its appreciable characteristics.

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