Optimization of surface finish during milling of hardened aisi4340 steel with minimal pulsed jet of...

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

ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 2, Number 1, Jan - Feb (2011), © IAEME

12

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: [email protected]

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

International Journal of Advanced Research in Engineering

and Technology (IJARET), ISSN 0976 – 6480(Print)

ISSN 0976 – 6499(Online) Volume 2

Number 1, Jan - Feb (2011), pp. 12-28

© IAEME, http://www.iaeme.com/ijaret.html

IJARET

© IAEME



13

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



14

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



15

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



16

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.



17

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

application ml/min Q 2 3.5 6 8.5 10



18

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



19

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 F

² + b₃₃ Q

² + 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.

No

Term Regression

coefficient

Standard

error

p-

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 + 0.042F

² + 0.103 Q

²

- 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



20

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

variation

Sum of

squares

Degrees of

freedom

Mean

squares

F-ratio p-

value

R²

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 + 0.042F

² + 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.



21

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



22

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



23

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

ρ0.121

V0.131

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



24

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



25

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



26

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

Sl

No.

Pressure at the

injector (bar)

Frequency of

pulsing

(Pulses/min)

Quantity of

cutting fluid

(ml/min)

Ra in microns %

error Observed Predicted

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.

References

1. R.S. Marano, J.M. Smolinnski, C.W.M. Esingulari, Polymer additives as mist

suppressants in metal cutting fluids, J. Soc. Tribol. Lubr. Eng. (1997) 25–32.

2. F. Klocke, G. Eisenblatter, Dry cutting, Annals of the CIRP, 46 (2) 1997, 519 – 526.



27

3. A. S Varadarajan, P.K. Philip, B. Ramamoorthy, Investigations on hard turning with

minimal cutting fluid application(HTMF) and its comparison with dry and wet

turning, International Journal of Machine tool and Manufacture (2001) 193 – 200.

4. A.S Varadarajan, P.K. Philip, B. Ramamoorthy, Neural Network Assisted

Performance Prediction in Hard Turning with Minimal Quantities of Cooling

Lubricants, Proceedings of the 14th International Conference, CAD/CAM, Robotics

and Factories of the Future PSG College of Technology, Coimbatore, India, pp.

(1998) 654-658.

5. Varadarajan, A.S., P.K. Philip, B. Ramamoorthy, Investigations on Hard Turning

with Minimal Pulsed jet of Cutting Fluid, Proceedings of the International seminar on

Manufacturing Technology beyond 2000, Bangalore, India (1999) pp173-179.

6. A. Attansio, M. Gelfi, C. Giardimi, C. Remino, Minimal Quantity Lubrication in

Turning: Effect on tool wear, Int. Journal of wear, 260(2006) 333-338.

7. R.Wertheim, A. Ber, Rotberg, Influence of high pressure flushing through the rake

face of the cutting tool, Ann. CIRP 41 (1992) 101–106.

8. M.A. Chepe, P.K. Philip, Cutting fluid injection at tool chip interface to improve

machining performance, J. Inst. Eng. (India) 75 (1994) 25–30.

9. M. Mazurkiowicz, Z. Kubala, J. Chow, Metal machining with high pressure water jet

cooling assistance new possibility, J. Eng. Ind. 111 (1989), 7–12.

10. P.K. Philip, A. S Varadarajan, B. Ramamoorthy, Influence of cutting fluid

composition and delivery variables on performance in hard turning using minimal

fluid in pulsed jet form, Journal of the Institution of Engineers (India), vol 82,

(2001)12–19.

11. Anil Raj, K. Leo Dev Wins, Robinson Gnanadurai, A. S Varadarajan, Investigations

on hard milling with minimal fluid application, International conference on frontiers

in Design and Manufacturing, Karunya University, Coimbatore (2008) pp183-187.

12. T. Thepsonthi, M. Hamdi, K. Mitsui, Investigation into minimal-cutting fluid

application in high-speed milling of hardened steel using carbide mills, International

Journal of Machine Tools and Manufacture, 49 (2009) 156 – 162.



28

13. Jack Feng CX, Wang X, Development of empirical models for surface roughness

prediction in finish turning, International Journal of advanced Manufacturing

technology, 2002;20:348-356.

14. Sreejith PS, Krishnamoorthy R, Malhotra SK, Narayanasamy K, Evaluation of PCD

tool performance during machining of carbon / phenolic ablative composites, Journal

of Material Processing Technology, 2000; 104:53 – 58.

15. Sahin Yusuf, Riza Motorcu A, Surface roughness prediction model in machining of

carbon steel by PVD coated cutting tools, American Journal of Applied Science,

2004; 1(1): 12-17.

16. Varadarajan, A.S, B. Ramamoorthy, P.K. Philip, Formulation of a Cutting fluid for

Hard Turning with Minimal Fluid Application, 20 th AIMTDR conference at Birla

institute of Technology Ranchi, India, (2002) pp89-95.

17. K. Leo Dev Wins, A.S Varadarajan, Optimization of operating parameters during

surface milling of hardened AISI4340 steel with minimal pulsed jet fluid application,

Journal of Manufacturing Technology Today, Vol.8, Issue 12, Dec 2009, p3-10.

18. Murugan, N., and Parmar, R. S., Effects of MIG process parameters on the surfacing

of stainless steel, Journal of Materials Processing Technology, 1994, 41: 381–398.

19. Cochran, W. G., and Cox, G. M, Experimental Designs, 2nd

Edition, John Wiley &

Sons, Inc, New York, 1957, 346 – 354.

20. Box G E P, Hunter W G & Hunter J S, Statistics for experiments, John Wiley & Sons,

Inc, New York, 1978.

21. Hiroyasu, T Kadota, Fuel drop size distribution in diesel combustion chamber, SAE

paper 740715, SAE Transactions, vol. 83 (1974) 715 – 721.

22. Uzi Landman, "FRUSTRATED" lubricant molecules offer new strategy for reducing

friction in mechanical devices, Georgia Tech – Research news,

http://gtresearchnews.gatech.edu/newsrelease/FRICTION.html, July 1998.

23. Alexender, R, A.S. Varadarajan , P.K. Philip, Hard Turning with Minimum Cutting

Fluid –A Viable green Alternative on the shop floor, Proceedings of the 18th All

India Manufacturing Technology Design and Research Conference, vol.1,

Kharagpur, (1998) pp 183-187.

Date post:	20-Jan-2015
Category:	Documents
Upload:	iaemedu
View:	341 times
Download:	0 times

Optimization of surface finish during milling of hardened aisi4340 steel with minimal pulsed jet of...

Documents