INVESTIGATION AND OPTIMIZATION OF TURNING PROCESS PARAMETER IN WET AND MQL SYSTEM ON EN31

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),

ISSN 0976 – 6359(Online), Volume 5, Issue 7, July (2014), pp. 134-143 © IAEME

134

INVESTIGATION AND OPTIMIZATION OF TURNING PROCESS

PARAMETER IN WET AND MQL SYSTEM ON EN31

S. S. Acharya1, R. L. Karwande

2

1(M.E.Student, Dept. of Mechanical, M.S.S.C.E.T., Jalna, Maharashtra, India)

2(Associate Proffesor, Department of Mechanical, M.S.S.C.E.T., Jalna, Maharashtra, India)

ABSTRACT

The big challenge of the mass production firms is concentrated for achieving high quality

products with good dimensionability with high productivity, less wear on the cutting insert, less use

of cutting fluid, within less time. This paper present dissertation work of an investigation of turning

process parameters on hard EN 31 material, for optimization of surface roughness, material removal

rate, machining time in wet and minimum quantity lubrication system. The experiment is carried out

by considering four controllable input variables namely cutting speed, feed rate, depth of cut and

insert nose radius in the presence of wet & MQL system. This experiment also present the relation

between chip formations and controllable variables along with chip thickness, chip colors & chip

velocity from which its effect on insert wear, quality of product can be easily found out, because of

chip morphology gives indirectly the effect of it on the insert wear. In this dissertation work

minimum quantity lubrication system is used for reducing the cutting zone temperature properly and

very fastly. Finally comparison is carried out between wet and minimum quantity lubrication system

from which one can easily identify which system is better for higher productivity along with high

surface finish. This work also present the productivity (MRR) concept in production. The design of

experiment and optimization of surface roughness, material removal rate, machining time is carried

out by using response surface methodology (RSM). Central composite design method is used (CCD)

for the total experimental design work and its analysis and also for optimization of turning process

parameter by which wastage of the machining time, power can be avoided.

Keywords: CCD, Mass Production, Minimum Quantity Lubrication, MRR, RSM.

INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING

AND TECHNOLOGY (IJMET)

ISSN 0976 – 6340 (Print)

ISSN 0976 – 6359 (Online)

Volume 5, Issue 7, July (2014), pp. 134-143

© IAEME: www.iaeme.com/IJMET.asp

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1. INTRODUCTION

In India machine tool industry made very great progress, but there main drawback is that they

not running the machine tools at their optimum operating conditions so that there is loss of man

power, material, time, quality along with productivity also. It has long been recognized that

conditions during cutting such as feed rate, depth of cut, cutting speed, nose radius should be

selected to optimize the economics of machining operations. In machine tool field turning hardened

steel is valuable process. Cutting hardened steel is an interesting topic of today’s industrial

production and scientific research. Turning process for hard steel is preferable thing compared to

grinding process & now days this process is alternative to many finishing processes such as grinding.

A major factor leading to the use of hard turning in place of grinding has been the development of

cubic boron nitride (CBN) cutting tool insert, which enable machining of high-strength materials

with a geometrically defined cutting edge. The main advantage of precision hard turning over

grinding include lower production costs, higher productivity, greater flexibility, elimination of

grinding fluids, and enhanced work piece quality.

In turning process, single-point cutting tool that is nothing but insert can complete the entire

machining process in a single fixture, thereby reduced setup times as well as lower costs. Also there

is many optionable things to improve the turning process rather than grinding process. In recent there

is big problem for all industrialist for achieving high quality products with more productivity within

less machining time which affects on surface roughness during turning of hardened steel. Even rough

surfaces wear more quickly & have high friction coefficient than smooth surfaces. As the surface

roughness increases then customer demand & quality of product goes on decreasing. So that there

should be bridge between quality and productivity. In short there should be such optimum condition

on which tool wear rate is minimum, maximum productivity with maximum quality within less time.

Generally hard turning requires large quantities of coolants and lubricants. The cost associated with

storage and disposal of coolants and lubricants increases the total cost of production considerably.

Conventional cutting fluid application fails to penetrate the chip-tool interface and thus cannot

remove heat effectively due to which there is loss of surface finish and also loss of tool life. To

overcome this problem there are some solutions. Some of these alternatives are dry machining and

machining with minimal fluid application. Dry machining is now of great interest and actually, they

meet with success in the field of environmentally friendly manufacturing. However, they are

sometimes less effective when higher machining efficiently, better surface finish quality and serve

cutting conditions are required. Minimal fluid application refers to the use of cutting fluids of only a

minute amount typically of flow rate of 50 to 500 ml/hour. The concept of minimal fluid application

sometimes referred to as near dry lubrication or micro lubrication.

So, to reduce the surface roughness, machining time, and to increase the MRR in finished

hard turning process I used minimum quantity lubrication system which also affect on chip handling

process and tool wear.

2. LITERATURE REVIEW

D.V. Lohar have evaluated the performance of MQL system during turning on hard AISI

4340 material by using Taguchi method. They have used the feed rate, cutting speed, depth of cut as

process parameter for analysis of cutting forces, surface roughness, cutting temperature & tool wear.

They have found that cutting force & temperature is less in MQL system Compared to the dry & wet

lubrication system. The surface finish is also high in case of MQL system. [1]

Y.B. Kumbhar investigated tool life and surface roughness optimization of PVD TiAlN/TiN

coated carbide inserts in semi hard turning of hardened EN31 alloy steel under dry cutting conditions



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using Taguchi method. They have concluded that the feed rate was the most influential factor on the

surface roughness and tool life. [2]

Ravinder Tonk, have investigated the effects of the parametric variations in turning process

of En31 alloy steel. Taguchi's robust design methodology has been used for statistical planning of the

experiments. Experiments were conducted on conventional lathe machine in a completely random

manner to minimize the effect of noise factors present while turning EN31 under different

experimental conditions. The analysis of results shows that input parameter setting of cutting tool as

carbide, cutting condition as dry, spindle speed at 230 rpm, feed at 0.25mm/rev and depth of cut at

0.3 mm has given the optimum results for the thrust force and input parameter setting of cutting tool

as HSS, cutting fluid as soluble oil, spindle speed at 230 rpm, feed at 0.25 mm/rev and depth of cut at

0.3 mm have been given the optimum results for the feed force when EN31 was turned on lathe. [3]

M. A. H. Mithu et al have evaluated the effect of minimum quantity lubrication on turning AISI 9310

alloy steel using vegetable oil based cutting fluid. They have found that chip-tool interface

temperature as well as tool wear gets reduced. [4]

Nikhil Ranjan Dhar evaluated the performance of MQL system on tool wear, surface

roughness and dimensional deviation in turning AISI-4340 steel by using cutting speed, feed rate,

depth of cut as controllable variables. They improved the tool life in MQL system.[5]

C. R. Barik studied the parametric effect & optimization of surface roughness of EN 31

material in dry turning. They concluded that feed rate has more effect on surface roughness. [6]

L. B. Abhang investigated the effect of MQL during turning of EN 31 alloy steel for analysis of

cutting temperature, cutting force, surface roughness. They found that quality of product as well as

tool life get improved.[7]

C. Ramudu have analyzed and optimized the turning process parameters using design of

experiments & response surface methodology on EN 24 steel. [8]

L. B. Abhang have created model and analyzed it for surface roughness in machining EN 31

steel using response surface methodology. They have found that surface roughness increases with

increase in feed rate and decreases with increase in cutting velocity. [9]

A. Del Prete studied & optimized the turning process parameter by using the genetic

algorithm & simulated annealing on super-nickel alloy for maximizing the MRR & analysis of thrust

force.[10]

Ashish Bhateja conducted there project work for Optimization of Different Performance

Parameters i.e. Surface Roughness, Tool Wear Rate & Material Removal Rate with the Selection of

Various Process Parameters Such as Speed Rate, Feed Rate, Specimen Wear , Depth Of Cut in CNC

Turning of EN24 Alloy Steel.[11]

R. Venkata Rao have optimized the multipass turning process parameter for surface

roughness by using Teaching–learning based optimization algorithm. [12]

Miroslav Neslusan et al have evaluated analysis of chip formation during hard turning

through acoustic emission. The results show that AE signals and accelerometers can be used to

monitor the dynamic character of plastic deformation in the cutting zone in hard turning. [13]

2.1 Literature Outcome From the literature review, it is observed that less research work has been seen for En31

Alloy Steel in CNC turning by the use of MQL system. Also very less work has been reported for

optimization of surface roughness, material removal rate & machining time on En 31 material. Also

optimization of turning process parameter in MQL system & comparison it with wet turning is to be

reported to much less. Chip chart is also new concept that has reported for EN 31 material in MQL

system and wet system is too much less from which one can easily predict tool life, surface finish.

Again no one can do an experiment by varying all controllable parameter in single experiment. I



137

conduct experiments by varying each variable that is cutting speed, feed rate, depth of cut & insert

nose radius along with lubrication environment in each and every experiment.

3. EXPERIMENTAL CONDITION

3.1 Work Piece The work piece material is EN-31 steel in the form of round bars of 45 mm diameter and

length of 70 mm axial cutting length. The composition of material is 0.9-1.2% C, 0.10-0.35% Si, o.3-

0.75% Mn, 1-1.6% Cr, 0.025% Co, 0.05% S and 0.05% P. Application of this material with its

properties are used to make axels, gears, camshafts, driving pinion and link components for

transportation and energy products as well as many applications in general mechanical engineering.

The work piece of EN-31 was firstly hardened followed by oil quenching at a temperature of 850°C

to achieve a hardness of 60 HRC throughout. A rough turning pass was conducted initially to

eliminate the run out of the work piece, after that diameter obtained for experimentation is

approximately 25 mm.

3.2 Cutting Tool CBN insert CNMG 120408, CNMG 120404, CNMG 120401.2 is selected along with tool

holder PCLNL 2525R12

3.3 MQL Setup

Blasocut oil is supplied as jet of cutting fluid with compressed air at 5 bar pressure. The

supply is directed at tool-work piece contact zone using nozzle. For proper combination of lubricant

and compressed air mixing chamber is used.

3.4 Process Variables

Cutting speed, feed rate, depth of cut, wet & MQL system, Insert nose radius. All this

parameter are used at there lowest and highest level by considering machine specification.

LEVEL CUTTING SPEED

(m/min)

FEED RATE

(mm/rev)

DEPTH OF

CUT(mm)

NOSE

RADIOS(mm)

LOW 100 0.1 0.1 0.4

MEDIUM 190 0.25 0.5 0.8

HIGH 280 0.4 1.0 1.2



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3.5 Response Variables

Material removal rate, surface roughness, chip morphology.

3.6 Research Methodology In that dissertation work to carry out design & experiment analysis I used, RSM (Response

surface methodology) (ccd-method) in Minitab16.

4. ANALYSIS OF SURFACE ROUGHNESS

Surface finish is an another valuable index of machinability as the performance and service

life of the machined component are often affected not only by its surface finish but also on presence

of surface or subsurface micro cracks. The surface roughness in wet lubrication and minimum

quantity lubrication system is analyzed and measured by using Taylor Hobson Make, Surface finish

tester which having least count 0.01, stroke length 0.25mm, 0.8mm, 2.5mm and 9.99µm as range.

The surface roughness was taken perpendicular to the turning direction. In this work an average

surface roughness (Ra) values were measured by taking average of the three readings. The following

table shows the readings of surface roughness obtained in wet and MQL system at different level of

feed rate, cutting speed, depth of cut and inserts nose radios.

RUN

ORD. N.R. C.S F.R D.O.C. S.R.(1)(WET) S.R.(2)(MQL)

1 0.4 190 0.00227 0.55 0.93 0.89

2 0.8 38.6386 0.25 0.55 7.7 7.3

3 0.8 190 0.25 1.30681 3.5 3.4

4 1.2 280 0.4 1 1.78 1.68

5 0.4 280 0.1 1 0.82 0.78

6 1.2 100 0.4 0.1 0.68 0.58

7 0.8 190 0.25 0.55 1.38 1.36

8 0.8 190 0.25 0.20681 2.32 2.02

9 0.8 190 0.25 0.55 1.6 1.4

10 0.8 190 0.25 0.55 1.6 1.4

11 0.4 190 0.50227 0.55 3.14 3.1

12 0.8 190 0.25 0.55 1.34 1.2

13 0.4 100 0.1 0.1 1.2 1.08

14 1.2 280 0.4 0.1 0.76 0.68

15 1.2 100 0.4 1 1.34 1.3

16 0.4 100 0.1 1 3.46 3.35

17 0.4 280 0.1 0.1 1.32 1.2

18 0.8 341.361 0.25 0.55 5.7 5.6

19 0.8 190 0.25 0.55 1.48 1.32

20 0.8 190 0.25 0.55 1.48 1.32



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4.1 Response Optimizer For optimizing the surface roughness response optimizer is used in response surface design.

The obtained results are as follows.

Predicted Responses Experimental Responses

S.R.(2)= 0.5726 S.R.(2)= 0.58

From this analysis it is clear that model no. 6 is better for surface finish which having cutting

speed 100 m/min, feed rate 0.4, depth of cut 0.1 and nose radios is 1.2.

EX.

No.

N.R

. C.S F.R D.O.C. WET LUBRICATION SYSTEM MQL

SHAPE THICK. COLOR SHAPE THICK. COLOR

1 0.4 190 0.0022 0.55 RIBON O.15 Faint Gray THIN &

STRAIGHT 0.13 Faint Golden

2 0.4 280 0.1 1 TUBULOR 0.7 Silver SHORT

TUBULOR 0.6

Golden &

silver

3 0.4 190 0.502 0.55 RIBON 0.6 Dark Gray HELICAL 0.58 Faint Gray

4 0.4 100 0.1 0.1 HELICAL 0.21 Faint silver HELICAL 0.19 Golden

5 0.4 100 0.1 1 RIBON 0.63 Dark Gray HELICAL 0.6 Gray

6 0.4 280 0.1 0.1 HELICAL 0.11 Faint Gray HALF TURN 0.09 Silver

7 0.8 38.6 0.25 0.55 THICK HELICAL 0.99 Burnt Blue HELICAL 0.95 Blue

8 0.8 190 0.25 1.31 SPIRAL 0.91 Gray SPIRAL 0.88 Faint Gray

9 0.8 190 0.25 0.55 RIBON 0.19 Faint Gray TUBULOR 0.16 Faint Golden

10 0.8 190 0.25 0.21 HELICAL 0.12 Silver HELICAL 0.1 Faint Golden




14 0.8 341 0.25 0.55 TUBULOR 0.13 Burnt Blue TUBULOR 0.11 Blue



17 1.2 280 0.4 1 SAW TOOTHED

RIBON 0.8 Dark Gray TUBULOR 0.78 Silver

18 1.2 100 0.4 0.1 SAW TOOTHED

RIBON 0.67 Dark Gray HALF TURN 0.63 Golden

19 1.2 280 0.4 0.1 SAW TOOTHED

RIBON 0.28 Dark Gray

SHORT

HALF TURNED

0.25 Golden

20 1.2 100 0.4 1 LONG TUBULOR 0.9 Silver SHORT

TUBULOR 0.88 Silver



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5. STUDY OF CHIP SHAPE, COLOR AND THICKNESS IN WET AND MQL SYSTEM

The chip color, thickness, shape, directly and indirectly indicate the nature of chip-tool

interaction influenced by the machining environment, also chip morphology helpful for determining

the sources of surface finish & tool wear. The chip samples were collected during each and every cut

at cutting speed, feed rate, depth of cut & nose radius. The thickness of chip is measured by using

digital verniar caliper. The color and shape is observed under microscope.

From this analysis it is clear that chip thickness is reduced in case of MQL system. A lower

chip thickness implies better lubrication at the chip-tool interface and formation of chips of thinner

sections i.e. if the chip thickness decreases, the process efficiency goes up and also thinner and

shorter chips can easily handle. Also there is change in color of chips which indirectly indicate heat

transfer during operation is very quickly due to compressed air. Also minimum quantity lubricant

metal cutting is a near to dry machining and clean machining processes, the chips are not

contaminated to cutting oil, minimize recycling cost. In addition there is less tool wear, higher

cutting speed and less BUE which improve the work surface finish in more amounts.

Chip breaking improvement in case no. (2) in MQL Long ribbon type chip in

case no.(1)(WET system)

Half turn golden chip in MQL System in case no. (18)

6. ANALYSIS OF MATERIAL REMOVAL RATE

Material removal rate is nothing but production term usually measured in cubic inches per

minute. To achieve higher productivity it is necessary to increase this rate which will obviously get a

part done quicker and therefore possibly for less money even also within less cycle time, but

increasing the material removal rate is often accompanied by increases in tool wear, poor surface

finishes, poor tolerances, and other problems. Optimizing the machining process is a very difficult

problem. Initial and final weights of work pieces are noted using digital weighing machine.



141

Machining time is also recorded. Following equations are used to calculate the response Material

Removal Rate (MRR):

6.1 Response Optimization For maximizing the MRR response optimizer is used at target 120250mm

3/min.

Predicted Responses MRR 187394

Global Solution

N.R. 1.2mm

C.S. 341.361m/min

F.R. 0.5022

D.O.C. 1.3

From this predicted responses and experimental responses it is clear that too much difference

is there between both the amounts. But at high levels machine get vibrates.

SR.NO. N.R. C.S F.R D.O.C. MRR

1 0.4 190 0.00227 0.55 271.914

2 0.8 38.6386 0.25 0.55 5278.64

3 0.8 190 0.25 1.30681 68655.4

4 1.2 280 0.4 1 120250

5 0.4 280 0.1 1 29266

6 1.2 100 0.4 0.1 4114.9

7 0.8 190 0.25 0.55 26598.2

8 0.8 190 0.25 0.20681 9890

9 0.8 190 0.25 0.55 25990

10 0.8 190 0.25 0.55 25600

11 0.4 190 0.50227 0.55 50610

12 0.8 190 0.25 0.55 24790

13 0.4 100 0.1 0.1 939.06

14 1.2 280 0.4 0.1 10461

15 1.2 100 0.4 1 36742.5

16 0.4 100 0.1 1 8910

17 0.4 280 0.1 0.1 2453.8

18 0.8 341.361 0.25 0.55 40702.4

19 0.8 190 0.25 0.55 22245.5

20 0.8 190 0.25 0.55 22178.6



142

F.R

D.O.C.

0.50.40.30.20.1

1.2

1.0

0.8

0.6

0.4

0.2

N.R. 0.8

C.S 190

Hold Values

>

–

–

–

–

< 0

0 20000

20000 40000

40000 60000

60000 80000

80000

MRR

Contour Plot of MRR vs D.O.C., F.R

7. CONCLUSION

From the analysis of surface roughness, material removal rate and chip morphology in wet

and MQL system it is clear that MQL system is better than wet system as follows.

1) By using MQL system near about maximum 18.18% chip thickness is reduced which is helpful

for reducing the surface roughness and also for reducing the tool wear.

2) MQL system improve the surface finish by maximum 14.70% with better dimensionability.

3) From the investigation it is clear that increase in feed rate increases the surface roughness,

increase in cutting speed decreases the surface roughness this is because due to higher cutting

temperature made the material ahead of tool softer and plastic. Also increase in insert nose

radios there is decrease in surface roughness.

4) As MQL system required less coolant due to which cutting tools and the work-piece will

remain clean which also save the recycling cost of lubricant oil.

5) MQL system enable improvement in MRR (Productivity) by allowing higher feed rate and

higher cutting speed.

8. REFERENCES

[1] D.V.Lohar, “Performance Evaluation of Minimum Quantity Lubrication (MQL) using CBN

Tool during Hard Turning of AISI 4340 and its Comparison with Dry and Wet Turning”

Bonfring International Journal of Industrial Engineering and Management Science, Vol. 3,

No. 3, September 2013.

[2] Y.B. Kumbhar, “Tool Life And Surface Roughness Optimization Of PVD TiAlN/TiN

Multilayer Coated Carbide Inserts In Semi Hard Turning Of Hardened EN31 Alloy Steel

Under Dry Cutting Conditions”, International Journal of Advanced Engineering Research and

Studies E-ISSN 2249–8974.

[3] Ravinder Tonk, “Investigation of the Effects of the Parametric Variations in Turning Process

of En31 Alloy”, International Journal on Emerging Technologies 3(1): 160-164(2012) ISSN

No. 0975-8364.

[4] M.A.H. Mithu, “Effects of minimum quantity lubrication on turning AISI 9310 alloy steel

using vegetable oil based cutting fluid, Journal of Materials Processing Technology 209

(2009) 5573–5583.

[5] Nikhil Ranjan Dhar, “Effect of Minimum Quantity Lubrication (MQL) on Tool Wear,

Surface Roughness and Dimensional Deviation in Turning AISI-4340 Steel, G.U. Journal of

Science 20(2): 23-32(2007).

[6] C.R. Barik, “Parametric Effect and Optimization of Surface Roughness of EN 31 In CNC

Dry Turning”, International Journal of Lean Thinking Volume 3, Issue 2 (December 2012).



143

[7] L B Abhang, “Experimental Investigation of Minimum Quantity Lubricants in Alloy Steel

Turning”, International Journal of Engineering Science and Technology, Vol. 2(7), 2010,

3045-3053.

[8] C. Ramudu, “Analysis and Optimization of Turning Process Parameters using Design of

Experiments”, International Journal of Engineering Research and Applications (IJERA)

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[9] L.B. Abhang, “Modeling and Analysis for Surface roughness in Machining EN-31 steel using

Response Surface Methodology” International Journal of Applied Research in Mechanical

Engineering, Volume-1, Issue-1, 2011.

[10] A. Del Prete, “Super-Nickel Orthogonal Turning Operations Optimization”, 14th CIRP

Conference on Modeling of Machining Operations (CIRP CMMO).

[11] Ashish Bhateja, “Optimization of Different Performance Parameters i.e. Surface Roughness,

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[13] Miroslav Neslusan, “Analysis of Chip Formation During Hard Turning Through Acoustic

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[14] Sreekala P and Visweswararao K, “A Methodology for Chip Breaker Design at Low Feed

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[15] Rahul Davis, “A Parameteric Design Study of Surface Roughness in Dry Turning Operation

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[16] Sumedh. S. Pathak and Dr. M. S. Kadam, “Development of RSM Based Model for

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[17] Vishal Francis, Ravi.S.Singh, Nikita Singh, Ali.R.Rizvi and Santosh Kumar, “Application of

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Documents

INVESTIGATION AND OPTIMIZATION OF TURNING PROCESS PARAMETER IN WET AND MQL SYSTEM ON EN31