ANALYSIS OF PROCESS PARAMETERS IN DRY MACHINING OF EN-31 STEEL by GREY RELATIONAL ANALYSIS

International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online), Volume 6, Issue 5, May (2015), pp. 01-08 © IAEME
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ANALYSIS OF PROCESS PARAMETERS IN DRY
MACHINING OF EN-31 STEEL by GREY RELATIONAL
ANALYSIS
SACHIN C BORSE
Deogiri Institute of Engineering and Management Studies, Aurangabad, Maharashtra, India
ABSTRACT
This paper presents the optimization of surface roughness & material removal rate in dry
turning of EN-31 steel.Carbide inserts were used for machining of EN-31 to study effects of process
parameters [Cutting speed (S), Feed (F) and depth of cut (d)]. These models can be effectively used
to predict the surface roughness (Ra) of the workpiece.
The big challenge of the Micro, small& medium industries in India for achieving high quality
products with increased productivity.Paper presentswork of an investigation of turning process
parameters on EN-31 material, for optimization of surface roughness, material removal rate.The
experiment is carried out by considering three controllable input variables namely cutting speed, feed
rate, and depth of cut.The design of experiment and optimization of surface roughness is carried out
by using Taguchi L9 orthogonal array & Grey Relational analysis.
Keywords: EN-31, Surface roughness (Ra), Speed (S), Feed (F), Depth of cut (d)
1. INTRODUCTION
One of the most important methods in production of metal parts is machining. Turning is the
mostwidely used machining processes that may result in high precision and quality and
increasedproductivity. However, the quality of final product and its production cost heavily depend
ofprocess parameters values. Single purpose control and process optimization can't satisfy
economicdemands such as reducing time and costs with maintaining quality at the same time. This is
becausequality improvement usually increases production costs and thus productivity decreases. The
use oftraditional optimization methods such as differential measures and enumeration of all
possiblesolutions is not very efficient and accurate.
Machining by turning involves the use of a lathe and is used primarily to produce cylindrical
or conical parts. It is valuable to increase tool life, to improve surface roughness, to reduce cutting
force and material removal rate in turning operations through an optimization study. Among these
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ISSN 0976 - 6480 (Print)
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four characteristics, surface roughness and material removal rate play the most important roles in the
performance of a turning process. Cutting speed, feed rate, depth of cut, tool-workpiece material,
tool geometry, and coolant conditions are the turning parameters which highly affect the
performance measures. In order to improve machining efficiency, reduce the machining cost, and
improve the quality of machined parts, it is necessary to select the most appropriate machining
conditions.
In Micro, small& medium industries (MSME)in India have made very great progress
[13],main drawback with MSME industries is the optimum operating parameters of the machines. 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 is valuable process. Machining ofsteel is an interesting topic of today’s industrial
production and scientific research. Turning process for 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 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 turning over grinding include
lower production costs, higher productivity, greater flexibility, elimination of grinding fluids, and
enhanced work piece quality.
In this article, a multi objective optimization model for CNC turning of EN-31 Steel. The
multiple performance characteristicsinclude material removal rate (MRR) and surface roughness
(SR). Three important machiningparameters; namely cutting speed, feed rate and depth of cut are
considered as the input processparameters. The analysis of variance (ANOVA) is also conducted to
estimate the relative effect ofeach process parameters.
2. LITERATURE REVIEW
The experimental investigations conducted by Dilbag Singh and P. Venkateswara Rao with
mixed ceramic inserts made up of aluminum oxide and titanium carbo nitride (SNGA) exhibited the
effect of cutting conditions and tool geometry on surface roughness in finished hard turning of EN-
31 steel. The primary influential factors that affect the surface finish are cutting velocity, feed,
effective rake angle and nose radius; dominant factor being feed followed by nose radius and others
[1]
S.K. Choudhury, I.V.K. Appa Rao presented a new approach for improving the cutting tool
life by using optimal values of velocity and feed throughout the cutting process. The experimental
results showed an improvement in tool life by 30%. [2]
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. [3]
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
using Taguchi method. They have concluded that the feed rate was the most influential factor on the
surface roughness and tool life. [4]
IlhanAsiltürk, Harun Akkus focused on optimizing turning parameters based on the Taguchi
method to minimize surface roughness by using hardened AISI 4140 (51 HRC) with coated carbide
cutting tools. Results of this study indicate that the feed rate has the most significant effect on
surface roughness. In addition, the effects of two factor interactions of the feed rate-cutting speed
and depth of cut-cutting speed appear to be important [5]

11
RavinderTonk, 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.
[6]
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. [7]
Nikhil RanjanDhar 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. [8]
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. [9] 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. [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.[13]
A.D.Jewalikar conducted the experiments for Hard Part Turning for the optimization of the
Hard Part Turning for Bohler K110 material in dry machining.For Surface Roughness (Ra) Cutting
speed is the dominant factor followed by feed and depth of cut. Therefore regression model can be
effectively used to predict the surface roughness within the specified range of cutting parameters
which were used while investigation. [14]
From the literature review, it is observed that less research work has been seen for En31
Alloy Steel in CNC turning in dry cutting system.
3. EXPERIMENTAL CONDITION
Many factors affect the surface roughness in turning process. The important machining
parameters include feed rate (F), depth of cut (D) and cutting speed (S).
The composition of material isas follows;
Experimental work was carried out on CNC turning machine (HAAS-SL20). A round bar (ø
50 mm × L 50 mm) of EN-31 steel was turned for each parameter combination tested. The cutting
was performed by using turning inserts (CNGA 120408 THM) by WIDIA CVD coated with Ti(C,
N)/TiN/Al2O3 which could provide higher heat resistance, under dry conditions. The objective of
the experiments was to secure the advantageous outcomes such as minimum surface roughness, less
heat generation, minimum tool wear, better geometrical accuracy and compressive stresses favorable
C Si Mn Cr Co S P
0.9-1.2%, 0.10-0.35% 0.30.75% 1-1.6% 0.025% 0.05% 0.05%

12
for carbide edges. Measurements of surface roughness were conducted in order to characterize the
process and determine the optimal operation conditions. For every operation a cut of 25 mm was
taken. Also for every operation new insert was used. After each cut, the surface roughness was
measured on the surface table with the help of surface roughness tester (Taylor Hobson) having cut
off length0.8 mm and evaluation length 25 mm.Three spots on each turned work piece were used to
measure the surface roughness of the cut. The measured values of surface roughness for 9
experiments are presented in Table 2. A well-planned design of experiment can substantially reduce
the number of experiments.
CNC Lathe machine –SL 20 Taylor Hobson Surface Tester
The grey system theory proposed by Denghasbeen proven to be useful for dealing with poor,
incomplete and uncertain information. The greyrelational analysis is based on the grey system theory
and can be used to solve complicated interrelationshipsamong multiple performancecharacteristics
effectively.
3.2 Process Variables
Cutting speed, feed rate, and depth of cut, MRR. All these parameter are used at their lowest
and highest level by considering machine specification.
Sr.No LEVEL
CUTTING SPEED
(mm/min)
FEED RATE
(mm/rev)
DEPTH OF CUT(mm)
1 LOW 100 0.1 0.1
2 MEDIUM 200 0.25 0.5
3 HIGH 300 0.4 1.0
3.3 Response Variables
Surface roughness
4. ANALYSIS OF SURFACE ROUGHNESS
The following table shows the readings of surface roughness obtained in dry system at
different level of feed rate, cutting speed, depth of cut.

13
RUN ORD. C.S F.R D.O.C. S.R
1 100 0.10 0.10 0.80
2 100 0.25 0.50 1.2
3 100 0.40 1.00 5.6
4 200 0.10 0.50 0.68
5 200 0.25 1.00 0.89
6 200 0.40 0.10 2.32
7 300 0.10 1.00 1.42
8 300 0.25 0.10 1.08
9 300 0.40 0.50 3.40
4.1 Response Optimizer
Data pre-processing is normally required, since the range and unit in one data sequence may
differ fromothers. It is also necessary when the sequence scatterrange is too large, or when the
directions of the targetin the sequences are different. In this study, a linearnormalization of the
experimental results for surface roughness wereperformed in the range between zero and one,
whichis also called the grey relational generation.The normalized data processing for SR
corresponding to lower-the-better criterion can
be expressed as:
=
; = 1,2, … . −
; = 1,2, … . − ; = 1,2, … .
Where Xi (k) is the value after the grey relationalgeneration, min yi (k) is the smallest value
of yi (k) forthe kth response, and the max yi (k) is the largest valueof yi (k) for the kth response.
The Grey relational generation is given in Table 4.
Sr.
No
speed feed doc Ra
S/N
Ratio
Normalise
d S/n Ratio
Deviation
Sequence
Grey
RlnCoeff-
Ra
GRG Ranking
1 100 0.1 0.1 0.8 1.94 0.0771 0.9229 0.3514 0.5873 2
2 100 0.25 0.5 1.2 -1.58 0.2694 0.7306 0.4063 0.5517 5
3 100 0.4 1 5.6 -14.96 1.0000 0.0000 1.0000 0.3333 9
4 200 0.1 0.5 0.68 3.35 0.0000 1.0000 0.3333 0.6000 1
5 200 0.25 1 0.89 1.01 0.1276 0.8724 0.3643 0.5785 3
6 200 0.4 0.1 2.32 -7.31 0.5821 0.4179 0.5447 0.4786 7
7 300 0.1 1 1.42 -3.05 0.3492 0.6508 0.4345 0.5351 6
8 300 0.25 0.1 1.08 -0.67 0.2194 0.7806 0.3904 0.5615 4
9 300 0.4 0.5 3.4 -10.63 0.7633 0.2367 0.6787 0.4242 8
Basically, the larger normalized results correspond tothe better performance and the best-
normalized resultshould be equal to one. Next, the grey relationalcoefficient is calculated to express
the relationshipbetween the ideal (best) and actual normalized experimental results.
The grey relational coefficient can be calculated as:
=
Δ + Δ
Δ + Δ

14
Δ =
∀ ∈ ∀
" − "
Denotes the sequence and yj (k) denotes the comparability sequence. ζ is distinguishing or
identified coefficient. The value of ζ is the smaller and the distinguished ability is the larger. ζ = 0.5
is generally used.
After averaging the grey relational coefficients (Table 4), the grey relational grade can be
expressed as
# =
1
$
%&
Parameters Level I Level II Level III Max-Min Rank
speed (A) 0.4908 0.5524 0.5069 0.0616 2
feed (B) 0.5741 0.5639 0.412 0.1621 1
Doc (C) 0.5425 0.5253 0.4823 0.0602 3
7. CONCLUSION
The experimental results show that the optimalcutting parameters are high cutting speed 200
mm/min,lower feed 0.25 mm/rev and lower depth of cut 0.1 mm, gives the l surfaceroughness (SR)
within the range of experiments based on the averagegrey relational grade.
8. ACKNOWLEDGEMENT
The author is very much thankful to the Indo German Tool Room (IGTR), Aurangabad for
their technical support during the experimentation
8. REFERENCES
1. Dilbag Singh. P. VenkateswaraRao, ‘A surface roughness prediction model for hard turning
processes, International Journalof Advanced Manufacturing Technology. (2007), Vol.32, pp.
1115–1124
2. S.K.choudhary, I.V.K. AppaRao: “Optimization of cutting parameters for maximizing tool
life”, International Journal of Machine Tools and Manufacture. (1999), Vol.39, pp. 343–353.
3. 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.
4. 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.
5. IlhanAsiltürkHarunAkkus, “Determining the effect of cutting parameters on surface roughness
in hard turning using the Taguchi method”, Elsevier, Measurement (2011), Vol.44, pp 1697–
1704
6. RavinderTonk, “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.

15
7. 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.
8. Nikhil RanjanDhar, “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).
9. 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).
10. 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.
11. C. Ramudu, “Analysis and Optimization of Turning Process Parameters using Design of
Experiments”, International Journal of Engineering Research and Applications (IJERA) ISSN:
2248-9622, Vol. 2, Issue 6, November- December 2012.
12. 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.
13. Ajay Dattatraya Jewalikar and Dr.AbhijeetShelke, “The Main Perceived Benefits
Associatedwith HSE Management Systems Certification in MSME Tool Rooms Post
QualityManagement System Certification”, International Journal of Management (IJM),
Volume 4,Issue 3, 2013, pp. 125 - 134, ISSN Print: 0976-6502, ISSN Online: 0976-6510.
14. Ajay Dattatraya Jewalikar and Vaibhav Joshi and SachinBorse, “HPT Process Parameter
Optimization in Dry Turning of Bohler K110 Steel” 4th International Conference on Materials
Processing and Characterization (ICMPC 2015), Paper ID MT-164 (Materials Today
Proceedings),
15. Kalpakjain S, Chmid S (2000) Manufacturing engineering and technology, int fourth edition.
Prentice Hall, New Jersey, pp 536–681
16. Sachin C Borse, “Optimization of Turning Process Parameter In Dry Turning of Sae52100
Steel” International Journal of Mechanical Engineering & Technology (IJMET), Volume 5,
Issue 12, 2014, pp. 1 - 8, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.

ANALYSIS OF PROCESS PARAMETERS IN DRY MACHINING OF EN-31 STEEL by GREY RELATIONAL ANALYSIS

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ANALYSIS OF PROCESS PARAMETERS IN DRY MACHINING OF EN-31 STEEL by GREY RELATIONAL ANALYSIS