milling

1. Prajapati et al, International Journal of Advanced Engineering Research and Studies E-ISSN2249–8974
IJAERS/Vol. II/ Issue I/Oct.-Dec.,2012/100-103
Review Article
A REVIEW ON OPTIMIZATION OF PROCESS PARAMETERS
FOR SURFACE ROUGHNESS AND MRR FOR S.S. 316.ON CNC
MACHINE
1
Navneet K Prajapati, 2
S.M. Patel
Address for Correspondence
1,
PG Fellow, 2
Associate Professor
S.P.B.Patel Engineering College, Mehsana, Gujarat
ABSTRACT:
In machining operations, achieving desired surface quality features of the machined product, is really a challenging job on
cnc machine. Because, these quality features are highly correlated and are expected to be influenced directly or indirectly by
the direct effect of process parameters. However, the extents of significant influence of the process parameters like speed,
feed, and depth of cut are different for different responses. Therefore, optimization of surface roughness and mrr is a multi-
factor, multi-objective optimization problem. Therefore, to solve such a multi-objective optimization problem, it is felt
necessary to identify the optimal parametric combination, following which all objectives could be optimized simultaneously.
In this context, it is essential to convert all the objective functions into an equivalent single objective function or overall
representative function to meet desired multi-quality features of the machined surface. The required multi-quality features
may or may not be conflicting in nature. The representative single objective function, thus calculated, would be optimized
finally. In the present work, Design of Experiment (DOE) with full factorial design has been explored to produce 27
specimens on SS 316 by straight turning operation. Collected data related to surface roughness have been utilized for
optimization by using grey relational analysis.
KEYWORDS: CNC, Grey relational analysis, Surface Finish, MRR.
1. INTRODUCTION
The challenge of modern machining industries is
mainly focused on the achievement of high quality,
in term of work piece dimensional accuracy, surface
finish. Surface texture is concerned with the
geometric irregularities of the surface of a solid
material which is defined in terms of surface
roughness, waviness, lay and flaws. Surface
roughness consists of the fine irregularities of the
surface texture, including feed marks generated by
the machining process. The quality of a surface is
significantly important factor in evaluating the
productivity of machine tool and machined parts. The
surface roughness of machined parts is a significant
design specification that is known to have
considerable influence on properties such as wear
resistance and fatigue strength. It is one of the most
important measures in finishing cutting operations.
In manufacturing industries, manufacturers focused
on the quality and Productivity of the product. To
increase the productivity of the product, computer
numerically machine tools have been implemented
during the past decades. Surface roughness is one of
the most important parameters to determine the
quality of product. The mechanism behind the
formation of surface roughness is very dynamic,
Complicated, and process dependent. Several factors
will influence the final surface roughness in a CNC
turning operations such as controllable factors
(spindle speed, feed rate and depth of cut).Principal
surface method is suitable to find the best
combination of independent variables which is
spindle speed, feed rate, and the depth of cut in order
to achieve desired surface roughness. Unfortunately,
Principal surface method is obtained from a statistical
analysis which has to collect large sample of data.
Realizing that matter, full factorial method is state of
the art intelligent method that has possibility to
enhance the prediction of surface roughness.
Input parameter: Speed, Depth of cut, Feed rate
Output parameter: Surface roughness, mrr
The CNC system has a computer in it, which controls
the functions. In the conventional system the control
is hard wired and therefore any modifications or
addition in facility call for many changes in the
controller which may or may not be possible due to
limitations of basic configurations. As compared to
this in a cnc system a bare minimum of electronic
hardware is used while software is used for the basic
function. That is why it is sometimes termed as
software control. This assists in adding extra facilities
conveniently without much problem and cost. Since
these computers are dedicated type, they need
comparatively much less storage and with the present
cost and high reliability.
A typical CNC system consists of the following six
elements
• Part program
• Program input device
• Machine control unit
• Drive system
• Machine tool
• Feedback system
Figure 1. Major components of a numerical
control machine tool
2. LITERATURE SURVEY
Many investigators have suggested various methods
to explain the effect of process parameter on mrr and
surface roughness.
Mr. Ballal Yuvaraj P, et al, [1] were carried out
“Application of taguchi method for design of
experiments in turning gray cast iron”. They describe
use and steps of Taguchi design of experiments and
orthogonal array to find a specific range and
combinations of turning parameters like cutting speed
,feed rate and depth of cut to achieve optimal values
of response variables like surface finish, tool wear,

2. Prajapati et al, International Journal of Advanced Engineering Research and Studies E-ISSN2249–8974
IJAERS/Vol. II/ Issue I/Oct.-Dec.,2012/100-103
material removal rate in turning of Brake drum of FG
260 gray cast iron Material. Three parameters namely
feed rare, spindle speed and depth of cut are varied to
study their effect on surface finish, tool wear and mrr.
They carried work on simple turn 5075 CNC lathe
with CNMA 120408 as a tool material. They carried
out worked with Minitab and ANOVA software for
effect analysis. They selected L27 orthogonal array
for taguchi design. Taguchi parameter design offers a
simple, systematic approach and can reduce number
of experiment to optimize design for performance,
quality and manufacturing cost. It is a scientifically
disciplined mechanism for evaluating and
implementing improvements in products, processes,
materials, equipments and facilitie.
Kamaraj Chandrasekaran, et al, [2] were carried
out “Computer Numerical Control Turning on
AISI410 with Single and Nano Multilayered Coated
Carbide Tools under Dry Conditions”. They carried
out work with carbide tools coated with multilayered
TiCN+Al2O3, multilayered Ti (C, N, B), single
layered (Ti, Al) N, and Nano multilayered B-Tic are
used for the turning study on AISI410, under dry
conditions on cnc. Different cutting parameters,
namely, cutting speed, feed rate, and depth of the cut
are used for the optimal setting of the parameters on
turning AISI410. Experiments were carried out using
the Taguchi's L 27 orthogonal array. The effect of
cutting parameters on surface roughness (SR) was
evaluated and optimal setting conditions were
determined for minimization of SR. Analysis of
variance (ANOVA) was used for identifying the
significant parameters affecting the response. They
concluded from the results of ANOVA, the feed rate
and cutting speed are the significant cutting
parameters affecting the SR with Ti (C, N, B), (Ti,
Al) N, and B-Tic, the feed rate and depth of cut are
the significant cutting parameters affecting the SR
with TiCN+Al2O3,a minimum SR value was obtained
using multilayered B-Tic carbide tools rather than
TiCN+Al2O3, (C,N,B) and (Ti,Al)N.
M.Janardhan, et al,[3] were carried out
“Determination and Optimization of cylindrical
Grinding process parameters Using taguchi method
and Regression analysis”. A set of experiments were
conducted on cylindrical grinding machine on EN8
material. The Experiments were conducted on CNC
cylindrical grinding machine with L9 Orthogonal
array with input machining variables as work speed,
feed rate and depth of cut. Empirical models are
developed using design of experiments and response
surface methodology. The adequacy of the developed
model is tested with ANNOVA. MINITAB15
software is used is for analysis of response graphs of
average values and S/N ratios. From the Pareto
analysis it is evident that the feed rate played vital
role on output responses surface roughness and metal
removal rate (MRR) than other process parameters.
The developed model can be used by the different
manufacturing firms to select right combination of
machining parameters to achieve an optimal metal
removal rate (MRR) and surface roughness (Ra).The
results reveals that feed rate, depth of cut are
influences predominantly on the output responses
metal removal rate (MRR) and surface roughness
(Ra).The predicted optimal values for MRR, Ra for
Cylindrical grinding process are 62.05 gm/min and
0.816 µm respectively. The results are further
confirmed by conducting confirmation experiments.
Kamal Hassan, et al, [4] were carried out
“Experimental investigation of Material removal rate
in CNC turning using Taguchi method”. This study
investigates the effects of process Parameters on
Material Removal Rate (MRR) in turning of C34000.
Medium brass alloy (C34000) of Ø: 19 mm, length:
280 mm were used for the turning experiments in the
present study. The turning tests were carried out to
determine the material removal rate under various
turning parameters. GC1035 coated carbide tool were
used for experimental investigations. The single
response optimization problems i.e. optimization of
MRR is solved by using Taguchi method. The
optimization of MRR is done using twenty seven
experimental runs based on L’27 orthogonal array of
the Taguchi method are performed to derive objective
functions to be optimized within the experimental
domain When the MRR is optimized alone the MRR
comes out to be 8.91. The optimum levels of process
parameters for simultaneous optimization of MRR
have been identified. Optimal results were verified
through confirmation experiments. The Material
removal rate is mainly affected by cutting speed and
feed rate. With the increase in cutting speed the
material removal rate is increases & as the feed rate
increases the material removal rate is increases. .
From ANOVA analysis, parameters making
significant effect on material removal rate feed rate,
and interaction between feed rate & cutting speed
were found to be significant to Material removal rate
for reducing the variation. The parameters considered
in the experiments are optimized to attain maximum
material removal rate. The best setting of input
process parameters for defect free turning (maximum
material removal rate) within the selected range is as:
i) Cutting speed i.e. 55m/min .ii) Feed rate i.e.
0.35mm/rev. iii) Depth of cut should be 0.2mm.
Ishan B Shah, et al, [5] were carried out
“Optimization of Cutting Tool Life on CNC Milling
Machine through Design of Experiments-A Suitable
Approach – An overview”. This paper discuss of the
literature review of Optimization of tool life in
milling using Design of experiment implemented to
model the end milling process that are using solid
carbide flat end mill as the cutting tool and stainless
steels s.s-304 as material due to predict the resulting
of Tool life. Data is collected from CNC milling
machines were run by 8 samples of experiments
using DOE approach that generate table design in
MINITAB packages. The inputs of the model consist
of feed, cutting speed and depth of cut while the
output from the model is Tool life calculated by
Taylor’s life equation. The model is validated
through a comparison of the experimental values with
their predicted counterparts. The optimization of the
tool life is studied to compare the relationship of the
parameters involve. Experimental results show that in
milling operations, Use of Low depth of cut, Low
cutting speed and high feed rate are recommended to
obtain better Tool life for the specific Range. The
following additional experimental results also being
achieved through the experiment and they are:
Improvement in tool life = 28%.. Increment in

3. Prajapati et al, International Journal of Advanced Engineering Research and Studies E-ISSN2249–8974
IJAERS/Vol. II/ Issue I/Oct.-Dec.,2012/100-103
productivity = 110 part/tool. Total cost reduction =
9.38 rs/part.
H. Yanda, et al, [6] were carried out “Optimization
of material removal rate, surface roughness and tool
life on conventional dry turning of fcd700”. They
investigate the effect of the cutting speed, feed rate
and depth of cut on material removal rate (MRR),
surface roughness, and tool life in conventional
turning of ductile cast iron FCD700 grade using TiN
coated cutting tool in dry condition. The machining
condition parameters were the cutting speed of 220,
300 and 360 m/min, feed rate of 0.2, 0.3 and 0.5
mm/rev, while the depth of cut (DOC) was kept
constant at 2 mm. The effect of cutting condition
(cutting speed and feed rate) on MRR, surface
roughness, and tool life were studied and analyzed.
Experiments were conducted based on the Taguchi
design of experiments (DOE) with orthogonal L9
array, and then followed by optimization of the
results using Analysis of Variance (ANOVA) to find
the maximum MRR, minimum surface roughness,
and maximum tool life. The optimum MRR was
obtained when setting the cutting speed and feed rate
at high values, but the optimum tool life was reached
when the cutting speed and feed rate were set as low
as possible. Low surface finish was obtained at high
cutting speed and low feed rate. Therefore time and
cost saving are significant especially is real industry
application, and yet reliable prediction is obtained by
conducting machining simulation using FEM
software Deform 3D. The results obtained for MRR
using the proposed simulation model were in a good
agreement with the experiments.
Figure 2 Main effects of cutting speed and feed rate
parameters in the S/N ratio for MRR
M. Naga Phani Sastry,et al, [7] were carried out
“Optimization of Performance Measures in CNC
Turning using Design of Experiments(RSM) ”.The
investigated the effect of turning process parameters
(cutting speed, feed rate, and depth of cut) on the
metal removal rate and surface roughness as
responses or output parameters. Response surface
methodology (R.S.M), which is a part of DOE, is
used to determine and present the cause and effect of
the relationship between true mean response and
input control variables influencing the response as a
two or three dimensional surface. R.S.M has been
used for designing a three factor with three level
central composite factors design in order to construct
statistical models capable of accurate prediction of
responses. The results obtained showed that the
application of R.S.M can predict the effect of
machining parameters on MRR and surface
roughness. A case study in straight CNC turning of
aluminum bar using HSS tool is being considered.
The study aimed at evaluating the best process
environment which could simultaneously satisfy
requirements of both quality and as well as
productivity. The predicted optimal setting ensured
minimization of surface roughness and maximization
of MRR (Material Removal Rate).Optimal result was
verified through confirmatory test.
J.S.Senthilkumaar , et al, [8] were carried out
“Selection of machining parameters based on the
Analysis of surface roughness and flank wear in
finish Turning and facing of inconel 718 using
taguchi Technique”. Single pass finish turning and
facing operations were conducted in dry cutting
condition in order to investigate the performance and
study the wear mechanism of uncoated carbide tools
on Inconel 718 in the form of cylindrical bar stock of
diameter 38 mm. The experiments were conducted on
the L16 ACE designer CNC lathe with constant
speed capability. Uncoated carbide inserts as per ISO
specification SNMG 120408-QM H13A were
clamped onto a tool holder with a designation of
DSKNL 2020K 12 IMP for facing operation and
DBSNR 2020K 12 for turning operation. Cutting
experiments were conducted as per the full factorial
design under dry cutting conditions. The effects of
the machining parameters on the performance
measures surface roughness and flank wear were
investigated. The relationship between the machining
parameters and the performance measures were
established using the non-linear regression analysis.
Taguchi’s optimization analysis indicates that the
factors level, its significance to influence the surface
roughness and flank wear for the tuning and facing
processes. Confirmation tests were conducted at an
optimal condition to make a comparison between the
experimental results foreseen from the mentioned
correlations. Based on Taguchi design of experiments
and analysis, the cutting speed is the main factor that
has the highest influence on surface roughness as
well as flank wear of turning and facing processes.
Optimal machining parameters for minimum surface
roughness were determined. The percentage error
between experimental and predicted result is 8.69%
and 8.49% in turning and facing process respectively.
Optimal machining parameters for minimum flank
wear, the percentage error between experimental and
predicted result is 4.67% for turning process and
2.63% for facing process. Based on the Taguchi’s
optimization analysis for the turning process the
cutting speed and depth of cut are the dominant
factors whereas in facing process cutting speed and
feed are dominant factors which affecting the
performance measures.
3. CONCLUSION
From the above literature survey we find that there
are many research done on optimization techniques
for process parameter for surface roughness and
material removal rate. But I found that there are very
few research done on SS316 stainless steel so we
want to do research on this material. We like to use
gray relational analysis for optimization.
4. REFERENCES:
1. Mr. Ballal Yuvaraj P., Dr. Inamdar K.H.,Mr. Patil P.V.,
Application Of Taguchi Method For Design Of
Experiments In Turning Gray Cast Iron, International
Journal of Engineering Research and Applications
(IJERA) ISSN: 2248-9622, Vol. 2, Issue 3, May-Jun
2012, pp.1391-1397

4. Prajapati et al, International Journal of Advanced Engineering Research and Studies E-ISSN2249–8974
IJAERS/Vol. II/ Issue I/Oct.-Dec.,2012/100-103
2. Kamaraj Chandrasekaran, Perumal Marimuthu, K Raja,
Computer Numerical Control Turning on AISI410 with
Single and Nano Multilayered Coated Carbide Tools
under Dry Conditions, Year : 2012 , Volume : 2 ,
Issue : 2 , Page : 75-81.
3. M.Janardhan ,Dr. A. Gopala Krishna, Determination
And Optimization Of Cylindrical Grinding Process
Parameters Using Taguchi Method And Regression
Analysis, International Journal Of Engineering Science
And Technology (Ijest),Issn : 0975-5462 Vol. 3 No. 7
July 2011 5659
4. Kamal Hassan, Anish Kumar, M.P.Garg, Experimental
investigation of Material removal rate in CNC turning
using Taguchi method, International Journal of
Engineering Research and Applications (IJERA) ISSN:
2248-9622,Vol. 2, Issue 2,Mar-Apr 2012, pp.1581-
1590
5. Ishan B Shah, Kishore. R. Gawande, Optimization of
Cutting Tool Life on CNC Milling Machine Through
Design Of Experimnets-A Suitable Approach – An
overview, International Journal of Engineering and
Advanced Technology (IJEAT),ISSN: 2249 – 8958,
Volume-1, Issue-4, April 2012
6. H. Yanda, J.A. Ghani, M.N.A.M. Rodzi, K. Othman
And C.H.C. Haron, Optimization Of Material Removal
Rate, Surface Roughness And Tool Life On
Conventional Dry Turning Of Fcd700, International
Journal Of Mechanical And Materials Engineering
(Ijmme), Vol.5 (2010), No.2, 182-190
7. M. Naga Phani Sastry and. K. Devaki Devi,
Optimization of Performance Measures in CNC
Turning using Design of Experiments(RSM), Science
Insights: An International Journal 2011; 1 (1): 1-5
8. J.S.Senthilkumaar; P.Selvarani And Rm.Arunachalam,
Selection Of Machining Parameters Based On The
Analysis Of Surface Roughness And Flank Wear In
Finish Turning And Facing Of Inconel 718 Using
Taguchi Technique, Emirates Journal For Engineering
Research, 15 (2), 7-14 (2010)