Optimization of surface finish during milling of hardened aisi4340 steel with minimal pulsed jet of fluid application using response surface methodology no restriction

International Journal of Advanced Research in and Technology (IJARET)
International Journal of Advanced Research in Engineering
Engineering
and Technology (IJARET), ISSN 0976 – 6480(Print) IJARET
ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 2, Number 1, Jan - Feb (2011), © IAEME
ISSN 0976 – 6499(Online) Volume 2
Number 1, Jan - Feb (2011), pp. 12-28 © IAEME
© IAEME, http://www.iaeme.com/ijaret.html

OPTIMIZATION OF SURFACE FINISH DURING MILLING OF
HARDENED AISI4340 STEEL WITH MINIMAL PULSED JET OF
FLUID APPLICATION USING RESPONSE SURFACE
METHODOLOGY
K. Leo Dev Wins
School of Mechanical Sciences
Karunya University, Coimbatore
Tamilnadu, E-Mail: leodevwins@gmail.com
A. S. Varadarajan
School of Mechanical Sciences
Karunya University, Coimbatore
Tamilnadu
ABSTRACT
Machining with minimal fluid application is involves the use of extremely small
quantities of cutting fluid so that for all practical purposes it resembles dry machining.
This technique is free from problems associated with procurement, storage and disposal
of cutting fluid and helps in promoting an eco friendly atmosphere on the shop floor.
Apart from machining parameters, the fluid application parameters such as pressure of
the fluid injector, frequency of pulsing and the rate of fluid application also influence the
cutting performance during minimal fluid application. Good surface finish is a functional
requirement for many engineering components and in the present investigation an attempt
is made to optimize surface finish during milling of hardened AISI4340 steel with
minimal fluid application using response surface methodology. The surface finish
predicted by the model matched well with the experimental results.
Key words: Central composite; Environment friendly; Mathematical models; Minimal
cutting fluid application; Pulsed jet; Rotatable design.
1. INTRODUCTION
Conventional surface milling of hardened steel involves application of large
quantities of cutting fluid. Procurement, storage and disposal of cutting fluid incur
expenses and large scale use of cutting fluid causes serious environmental and health

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International Journal of Advanced Research in Engineering and Technology (IJARET)

hazards on the shop floor. It also leads to problems in disposal of cutting fluid which has
to comply with environmental legislation as well. According to the Occupational Safety
and Health Administration (OSHA) regulations, the permissible exposure Level for mist
within the plant (PEL) is 5 mg/m³and is likely to be reduced to 0.5 mg/m³ [1]. In this
context, pure dry milling is a logical alternative which is totally free from the problems
associated with storage and disposal of cutting fluid. But it is difficult to implement on
the existing shop floor as it requires ultra hard cutting tools and extremely rigid machine
tools [2]. Ultra hard cutting tools may be introduced but the existing machine tools may
not be rigid enough to accept them. In this context the best alternative is to introduce
pseudo dry milling or milling with minimal fluid application [3 - 6]. By introducing the
cutting fluid precisely at the cutting zone, better cutting performance can be achieved
which will result in better surface finish, reduction of tool wear and cutting force [7–9].
In minimal fluid application, extremely small quantities of cutting fluid is introduced as
high velocity (70 m/s) tiny droplets at critical zones so that for all practical purposes it
resembles dry milling [10].
It is reported that minimal cutting fluid application can bring forth better cutting
performance during turning and in the case of minimal application, heat produced during
machining is transferred predominantly in the evaporative mode, which is more efficient
than the convective heat transfer prevalent in conventional wet turning [3, 10]. Very less
work is reported in the area of fluid minimization during milling [11, 12]. Research work
carried out in our laboratory indicated that good cutting performance could be achieved
in terms of surface finish, tool wear and cutting force when a specially formulated cutting
fluid was applied on critical locations in the form of high velocity narrow pulsed jet
during surface milling of AISI4340 steel with a hardness of 45 HRC by a fluid
application system that can deliver cutting fluid through fluid application nozzles and
offer better rake face lubrication. The scheme is environment friendly and can be easily
implemented on the shop floor.
Surface roughness (Ra) is widely used as an index to determine the surface finish
in the machining process. Surface roughness has become one of the important output
parameters for many years and one of the important design features in many situations
such as parts subject to fatigue loads, precision fits, fastener holes and aesthetic

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requirements. In addition to the tolerances, surface roughness imposes one of the most
critical constraints for selection of machines and cutting parameters in process planning
[13]. For achieving the desired surface finish, it is necessary to understand the
mechanisms of the material removal and the kinetics of machining processes affecting the
performance of the cutting tool [14]. Earlier work in this research showed the fluid
application parameters such as pressure at the fluid injector, frequency of pulsing and the
rate of fluid application affects the surface roughness to a larger extent [11]. The
traditional ‘one-factor at a time’ technique used for optimizing a multivariable system is
not only time consuming but often misses easily the alternative effects between the
components. Also, this method requires carrying out a number of experiments to
determine the optimum levels, which are false at most of the times. These drawbacks of
single factor optimization process can be eliminated by optimizing all the affecting
parameters collectively by central composite design (CCD) using Response Surface
Methodology (RSM). For prediction, the response surface Method is practical,
economical and relatively easy to use when compared to other types of optimization
techniques [15]. In the present work, a mathematical model has been developed to
predict the surface roughness of machined work piece using response surface method.
Analysis of variance (ANOVA) is used to check the validity of the model developed.
1.1 Selection of work material
Through hardenable AISI4340 steel was selected as work material. It was
hardened to 45 HRC by heat treatment. It is a general purpose steel having wide range
of applications in automobile and allied industries by virtue of its good hardenability.
Plates of 125 mm length, 75 mm breadth and 20 mm thickness were used for the present
investigation. The composition of the work material is shown in Table 1.
Table 1 Chemical composition of work material
Element %
C 0.38 – 0.43
Cr 0.7 – 0.9
Mn 0.6 – 0.8
Mo 0.2 – 0.3
Ni 1.65 – 2.0
P 0.035 max
Si 0.15 – 0.3
S 0.04 max
Fe Balance

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1.2 Selection of cutting tool
Carbide inserts with the specification AXMT 0903 PER-EML TT8020 of
TaeguTec was used in the investigation along with a tool holder with the specification
TE90AX 220-09-L.
1.3 Formulation of cutting fluid
Since the quantity of cutting fluid used is extremely small, a specially formulated
cutting fluid was employed in this investigation. The base was a commercially available
mineral oil and the formulation contained other ingredients [16]. It acted as an oil in
water emulsion.
1.4 Fluid application system

Figure 1 Schematic view of the minimal fluid applicator
A special test rig was developed for this purpose [3]. It consists of a P-4 fuel
pump (Bosch make) coupled to an infinitely variable electric drive. An injector nozzle of
single hole type with a specification DN0SD151 with a spray angle of 0º was used in the
investigation. The test rig facilitated independent variation of pressure at fluid injector
(P), frequency of pulsing (F) and the rate of fluid application (Q). The system can deliver
cutting fluid through four outlets simultaneously so that cutting fluid could be applied to
more than one location or more than one machine tool at the same time. By selecting
proper settings the rate of fluid application could be made as small as 0.25ml/min. The
frequency of pulsing is determined by the speed of rotation of the DC variable speed
motor that drives the fluid pump.
The fluid applicator delivers cutting fluid at a rate of one pulse per revolution.
This facility enables application of less amount of cutting fluid per pulse. For example, if
Q is the rate of fluid application in ml/min and F is the frequency of pulsing in
pulses/min, fluid applied per pulse is given by Q/F. Pulsing jet aids in fluid minimization

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without compromising the velocity of individual particles as the pressure at the fluid
injector remains constant. By increasing the frequency, the rate of fluid delivered per
pulse can be controlled. For example if Q is 1 ml/min and F is 1000 pulses/min and the
pressure at the fluid nozzle is set at 100 bar, then fluid delivered per pulse is equal to
1/1000 = 0.001 ml while the velocity of the individual fluid particles will be
approximately equal to about 70 m/sec [10]. A schematic view of the fluid applicator is
shown in Figure 1.
Special fixtures were designed as in Figure 2 so that the injector nozzle could be
located in any desired position without interfering the tool or work during actual cutting.

Figure 2 Fixtures for locating the fluid injector
2. SCHEME OF INVESTIGATION
The experiments were designed based on five-level factorial central composite
rotatable design with full replications. The design matrix is shown in Table 3.
Experiments were carried out on an HMT (model: FN1U) milling machine. Surface
finish was measured using a stylus type Perthometer (Mahr make). The cutting speed,
feed and depth of cut were set in the semi finish milling range for the tool-work
combinations. The cutting parameters such as cutting speed, feed rate and depth of cut
were kept constant at 45 m/min, 0.14 mm/tooth and 0.4 mm respectively [17].
In order to achieve the desired objective, the investigations were planned in the
following sequence:
1. Identifying the predominant factors which are having influence on surface
roughness and finding the upper and lower limits of the chosen factors.
2. Developing the experimental design matrix.
3. Conducting the experiments as per the design matrix and recording the responses.
4. Developing the mathematical model, calculating the coefficients of the model and
testing the significance of the coefficients.

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5. Checking the adequacy of the developed model by ANOVA method and
6. Validating the mathematical model by experimentation.
2.1 Identifying the predominant factors which are having influence on
surface roughness and finding the upper and lower limits of the chosen
factors
Surface roughness of the work piece is an important attribute of quality in any
machining operation. During machining many factors affects the surface finish. Based
on the previous research work [11], it was found that in addition to the machining
parameters such as cutting speed, feed rate and depth of cut the fluid application
parameters also influence the quality of the surface generated. Apart from machining
parameters, the independently controllable predominant fluid application parameters that
influence the surface finish of the work piece were identified as:
1. pressure at the fluid injector (P)
2. Frequency of pulsing (F)
3. Quantity of application of cutting fluid.
Preliminary experiments were carried out to fix the lower and upper limits of
these factors. Accordingly, pressure at the fluid injector was fixed between 50 and 100
bar. In line with this factor, the frequency of pulsing was fixed between 250 and 750
pulses /min and the rate of application of cutting fluid was fixed between 2 and 10
ml/min. The upper limit of the factor was coded as +1.682 and the lower limit as -1.682.
The coded values for intermediate values were calculated from the following
relationship:

Where is the required coded value of a variable X; and X is any value of the
variable from to . The selected process parameters with their limits, units and
notations are given in Table 2.
Table 2 Process control parameters and their limits
Limits
Process parameters Units Notations -1.682 -1 0 1 1.682
Pressure at fluid injector bar P 50 60 75 90 100
Frequency of pulsing Pulses/min F 250 350 500 650 750
Quantity of cutting fluid
ml/min Q 2 3.5 6 8.5 10
application

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2.2 Developing the experimental design matrix
A five level, three-factors, central composite rotatable factorial design [18],
consisting of 20 sets of coded conditions is shown in Table. 3. The design matrix
comprises a full factorial design 2³ [=8] plus six star points and six center points. All
fluid application parameters at the intermediate level (0) constitute center points and
combinations at either its lowest (-1.682) or highest (+1.682) level with the other two
parameters at the intermediate level constituting the star points. Thus the 20
experimental runs allowed the estimation of the linear, quadratic and two-way interactive
effects of the process parameters on the surface roughness.
Table 3 Design matrix and observed values of surface roughness
Design Matrix Ra in microns
S. No P F Q
1 -1 -1 -1 0.675
2 1 -1 -1 0.591
3 -1 1 -1 0.880
4 1 1 -1 0.627
5 -1 -1 1 0.817
6 1 -1 1 0.514
7 -1 1 1 0.870
8 1 1 1 0.564
9 -1.682 0 0 0.840
10 1.682 0 0 0.400
11 0 -1.682 0 0.615
12 0 1.682 0 0.711
13 0 0 -1.682 0.851
14 0 0 1.682 0.820
15 0 0 0 0.527
16 0 0 0 0.545
17 0 0 0 0.516
18 0 0 0 0.521
19 0 0 0 0.532
20 0 0 0 0.536
2.3 Conducting the experiments as per the design matrix and recording the
responses
The experiments were conducted as per the design matrix at random, to avoid the
possibility of systematic errors. The average roughness (Ra) is mostly used in industries,
is taken as the response for this study. The surface roughness was measured using a stylus
type Perthometer (Mahr make). Table 3 presents a record of the surface finish for each
experiment. In this table, for experimental runs 15 to 20, even though the experimental
setup and all machining conditions remain the same, the responses varied slightly. This is

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due to the effect of unknown and unpredictable variables called noise factors, which crept
into the experiments. To account for the impact of these unknown factors of the response,
replicated runs were included in the design matrix.
2.4 Developing the mathematical model, Calculating the coefficients and
testing the coefficients
The response function representing surface roughness can be expressed as Ra = f
(P, F, Q) and the relationship selected being a second-order response surface. The
function is as follows [19]

Ra = b₀ + b₁ P + b₂ F + b₃ Q + b₁₁ P² + b₂₂ F² + b₃₃ Q² + b₁₂ PF + b₁₃ PQ + b₂₃ FQ

Where coefficients b1, b2 and b3 are linear terms, coefficients b₁₁, b₂₂ and b₃₃ are second-

order terms, and coefficients b₁₂, b₁₃ and b₂₃ are interaction terms. MINITAB software

(version 13.1) software package was used to calculate these coefficients and the results
obtained are shown in Table 4.
Table 4 Estimated values of regression coefficients
S. Term Regression Standard p-
No coefficient error value
1 Constant 0.530 0.011 0.000
2 P -0.123 0.007 0.000
3 F 0.037 0.007 0.001
4 Q -0.004 0.007 0.046
5 P*P 0.027 0.007 0.004
6 F*F 0.042 0.007 0.000
7 Q*Q 0.103 0.007 0.000
8 F*P -0.022 0.010 0.049
9 Q*P -0.034 0.010 0.005
10 Q*F -0.017 0.010 0.103
The regression model developed for predicting surface finish (Ra) is shown by the
following equation (1).
Surface finish Ra = 0.530 - 0.123 P + 0.037 F - 0.004 Q + 0.027 P² + 0.042F² + 0.103 Q²
- 0.022 PF - 0.034 PQ - 0.017 FQ. ……………….(1)
2.5 Checking the adequacy of the developed model by ANOVA technique.
The model was examined for lack of fit, adequacy and efficiency. Table 5
presents the ANOVA summary of the model developed. The model is highly significant
as indicated by the p-value (p<0.001). The goodness of the fit of the model was checked
by the coefficient of determination (R²). The value of R² is always between 0 and 1. The
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closer the R² value to 1, the stronger will be the model and better will be its predictions
[19, 20]. In this case, the value of the coefficient of determination (R² = 0.982) indicates
that 98.2% of the variability in the response could be explained by the model. In addition
to this, the value of the adjusted coefficient of determination (Adj. R² = 0.967) is also
very high to advocate for a higher significance of the model.
Table 5 ANOVA summary for the model
Source of Sum of Degrees of Mean F-ratio p- R²
variation squares freedom squares value
Regression 0.413 9 0.046 62.198 0.000 0.982
Residual 0.007 10 0.001
A p-value less than 0.05 indicated the significant model terms. The regression
analysis of the experimental design presented in Table. 5 demonstrated that the linear
model terms (P, F and Q), quadratic model terms (P², F², and Q²) and interactive model
terms (F*P, Q*P) are significant (p<0.05) and the interactive model term Q*F is
insignificant (p>0.05). After dropping out the non-significant terms from Table. 4, the
model can be expresses by the equation (2):
Surface roughness Ra = 0.530 - 0.123 P + 0.037 F - 0.004 Q + 0.027 P² + 0.042F² + 0.103
Q² - 0.022 PF - 0.034 PQ. …………. (2)
Studentized residuals were calculated to check the adequacy of the model.
Residual represents the difference between the observed value of a response and the
value that is fitted under the hypothesized model. Any observation with a studentized
residual value greater than 3 was considered as outlier. It is found that except
experimental run no. 1 with a studentized residual of -6.819, other values were well
within the acceptable range.

2.6 Validating the mathematical model
Validity of the developed models was tested by drawing scatter diagrams that
shows the observed and predicted values of surface roughness. Fig. 3 shows the
representative scatter diagram. Test runs were conducted to determine the accuracy of the
model conformity. A comparison was made between predicted and actual values. The
results obtained show that the model is sufficiently accurate as indicated by the R² value
which is as high as 0.976.

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Figure 3 Scatter diagram for surface roughness (Ra)
3. RESULTS AND DISCUSSIONS
The mathematical model as in equation (2) can be used to predict the surface
roughness (Ra) by substituting the values of the respective process parameters. The
surface roughness calculated from the final model for each set of coded values of fluid
application parameters are represented graphically in Figure 4, Figure 5, Figure 6 and
Figure 7. These plots show the convincing trends between cause and effect. The direct
and interaction effects are discussed below.

3.1 Direct Effect of pressure at the fluid injector on Surface roughness
Figure 4 represents the direct effect of pressure at the fluid injector (P) on surface
roughness (Ra). From the Figure, it is clear that surface finish increases with increase in
pressure at the fluid injector.

Figure 4 Direct effect of pressure at the fluid injector on surface roughness
The pressure at the fluid injector should be kept at high level (100 bar)
corresponding to an exit velocity of 50 m/s to achieve better surface finish. The exit
velocity of the fluid particles from the nozzle is directly proportional to the pressure at
the fluid injector whereas the size of fluid particle is inversely proportional to the

21


pressure [21]. The cutting force is directly related to the chip friction on the rake face.
Any attempt to reduce friction on the rake face can bring forth lower cutting force, lower
energy consumption and better surface finish. Hence when the pressure at the injector is
high, the fluid particles will have higher velocity and smaller size which help them to
penetrate into the tool-chip interface [3] leading to better lubrication at the contact
surfaces and hence better surface finish.
3.2 Direct Effect of frequency of pulsing on Surface roughness
Figure 5 represents the direct effect of frequency of pulsing (F) on surface
roughness (Ra). From the Figure 5, it is clear that surface roughness first decreases with
increase in frequency of pulsing and then it increases.

Figure 5 Direct effect of frequency of pulsing on surface roughness
It is observed that frequency of fluid application in the range of 400 to 500
pulses/min favored better surface finish. It is reported that the frictional forces between
two sliding surfaces can be reduced considerably by rapidly fluctuating the width of the
lubricant filled gap separating them [22].
When a pulsing jet is used, the width of the lubricant filled gap between the tool
rake face and the chip fluctuates with a frequency equal to the frequency of pulsing of the
fluid jet. The width will be maximum when the fluid slug falls at the gap and will be
minimum when no particles fall on the gap during the pulsing cycle. This process
continues as the fluid particles fall in the gap between the chip and the tool intermittently.
When the frequency of pulsing is 750 pulses/min, the quantity of fluid delivered per pulse
will be very less when compared to 500 pulses/min for any fixed rate of fluid application.
Hence the fluctuation in the width of the liquid film between the tool and the chip is less
appreciable. A minimum quantity of cutting fluid should be delivered per pulse to get

22


appreciable fluctuation in the width. This leads to presence of fresh fluid droplets in to
the tool chip interface unlike in the case where a stagnant layer of cutting fluid present if
a continuous jet was employed [23]. The presence of fresh fluid droplets facilitates better
filling of the gap on the tool chip interface thereby providing better lubrication and
enhanced cooling as the droplets evaporate.
When the frequency of pulsing is very high, the individual particles will be small
and may lack in kinetic energy to penetrate in to the tool chip interface. This leads to less
fluid particles reaching the rake face and hence less efficient rake face lubrication. It is
also to be noted that the pulsing nature of the fluid delivery vanishes when the frequency
of pulsing is very high and the fluid delivery tends to resemble a continuous jet, devoid of
all the aforesaid advantages claimed for a pulsing jet.
3.3 Direct Effect of quantity of cutting fluid
Figure 6 represents the direct effect of Quantity of cutting fluid (Q) on surface
roughness (Ra). From the Figure, it is clear that surface roughness decreases with
increase in the quantity of cutting fluid and then increases.

Figure 5 Direct effect of frequency of pulsing on surface roughness
It is observed that the quantity of cutting fluid about 6 ml/min favored better
surface finish. According to the empirical relationship developed by Hiroyasu and
Kadota [21], the mean diameter D for a droplet size cutting fluid injection is given by

D = K(∆P)-0.135 ρ0.121V0.131
Where ∆P is the mean pressure drop, ρ is the density of the medium in which
injection of fluid takes place, V is the quantity of fluid delivered per pulse and K is a

23


constant. With lower delivery rates, droplet size decreases. When the size of the droplets
is small, they can easily enter into the tool-chip interface and provide better rake face
lubrication but when the size is too small, the kinetic energy of the fluid particles will be
very less and the particles need a minimum kinetic energy to reach the tool-chip
interface. It appears that a rate of fluid application of 6 ml/min favors the best
penetration from the point of view of the individual size of the fluid droplets and the
kinetic energy. If the rate of fluid application is greater than 6 ml/min, the fluid particles
will have higher kinetic energy but their sizes may not be favorable for their easy
penetration into the tool-chip interface. When the rate of fluid application is much less
than 6 ml/min, the size of individual particles may favor their passage into the tool-chip
interface but they may not have sufficient kinetic energy owing to their smaller size.
When the rate of flow is about 6 ml/min, it appears that both the size and the kinetic
energy favors easy penetration of fluid particles into the tool-chip interface thereby
providing better rake face lubrication and hence better surface finish. It is also to be
noted that the pulsing nature of the fluid delivery is affected when the quantity of cutting
fluid increases.
3.4 Interaction Effect of pressure at the fluid injector and Frequency of
pulsing on Surface roughness

Figure 6 Interaction effect of pressure at the fluid injector and frequency of pulsing
on surface roughness
The response surface plot shown in Figure 6 shows the interaction effect of
pressure at the fluid injector and frequency of pulsing on surface roughness while the
quantity of cutting fluid was maintained at 6 ml/min. From the contour of the surface, it
is noted that surface roughness (Ra) is maximum (1.0565 µm) when pressure at the fluid

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injector was at lower (-1.682) level and frequency of pulsing at higher level (+1.682), and
the surface roughness (Ra) was minimum (0.4 µm) when pressure at the fluid injector
was at higher (+1.682) level and frequency of pulsing at intermediate level (0).
3.5 Interaction Effect of pressure at the fluid injector and quantity of cutting
fluid on Surface roughness
The response surface plot shown in Figure 7 shows the interaction effect of
pressure at the fluid injector and quantity of cutting fluid on Surface roughness while the
frequency of pulsing was maintained at 500 pulses/min.

Figure 7 Interaction effect of pressure at the fluid injector and quantity of cutting fluid
on surface roughness
From the contour surface, it is noted that surface roughness (Ra) is maximum
(1.194 µm) when pressure at the fluid injector was at lower (-1.682) level and quantity of
cutting fluid at higher level (+1.682), and the surface roughness (Ra) was minimum
(0.394 µm) when pressure at the fluid injector was at higher (+1.682) level and frequency
of pulsing at intermediate level (0) for a constant value of frequency of pulsing at 500
pulses/min.
The optimal conditions obtained form the analysis in coded form are P = 1.682, F
= 0.0003 and Q = 0.297. The real values are pressure at the fluid injector at 100 bar,
Frequency of pulsing at 500 pulses/min and the quantity of cutting fluid at 5.2936
ml/min. The minimum surface roughness (Ra) that can be achieved when the pressure at
the injector is kept at 100 bar, frequency of pulsing at 500 pulses/min and the rate of fluid
application at 6.706 ml/min is 0.3904 µm. Cutting experiments were conducted to
validate the prediction and from Table 6 it is evident that the value of surface finish as
predicted by the model matched well with the experimental result. Table 6 presents the

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comparison of the optimum surface finish as predicted by the model with the
experimental results.
Table 6 Comparison of the predicted surface finish with experimental value
Frequency of Quantity of Ra in microns
Sl Pressure at the %
pulsing cutting fluid Observed Predicted error
No. injector (bar)
(Pulses/min) (ml/min)
1. 100 500 6.706 0.4100 0.3904 4.7
4 CONCLUSIONS
Mathematical model for surface roughness has been developed to correlate the
important fluid application parameters in machining of hardened AISI4340 steel. The
experimental plan used is of rotatable central composite design. The three important input
variables considered for the present research study is pressure at the fluid injector,
frequency of pulsing and quantity of cutting fluid application. The influences of the fluid
application parameters on surface roughness have been analyzed based on the
mathematical model developed. The study leads to the following conclusions.
1. The surface roughness decreases with the increase of pressure at the fluid injector.
2. The surface roughness decreases with increase in frequency of pulsing up to
certain level (about 500 pulses/min) and then increases with the increase of
frequency of pulsing.
3. The surface roughness decreases with increase in the quantity of cutting fluid up
to certain level (about 6.7 ml/min) and then increases with the increase in
quantity of cutting fluid.
4. It was found that the predictions of the RSM matched well with the experimental
results.
Acknowledgement
The authors thank the authorities of the Centre for Research in Design and
Manufacturing of the Karunya University for facilitating this project and M/s Taugetec
India (P) Ltd. for supplying cutting tools at concessional rates.
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Optimization of surface finish during milling of hardened aisi4340 steel with minimal pulsed jet of fluid application using response surface methodology no restriction

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Optimization of surface finish during milling of hardened aisi4340 steel with minimal pulsed jet of fluid application using response surface methodology no restriction