SlideShare a Scribd company logo
1 of 4
Harish Kumar, Mohd. Abbas, Dr. Aas Mohammad, Hasan Zakir Jafri / International Journal
of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 3, May-Jun 2013, pp.331-334
331 | P a g e
Optimization of cutting parameters in CNC Turning
Harish Kumar1
, Mohd. Abbas 2
, Dr. Aas Mohammad3
, Hasan Zakir Jafri4
1, Research Scholar, Al-Falah School of Engineering & Technology
2,4, Assistant Professor, Department of Mechanical Engineering, Al-Falah School of Engineering &
Technology Village Dhauj, Faridabad, Haryana
3, Associate Professor, Department of Mechanical Engineering, Faculty of Engineering & Technology, Jamia
Millia Islamia, New Delhi
ABSTRACT
Many manufacturing industries involve
machining operations. In metal cutting the
turning process is one of the most fundamental
cutting processes used. Surface finish and
dimensional tolerance, are used to determine and
evaluate the quality of a product, and are major
quality attributes of a turned product. In this
paper experimental work has been carried out
for the optimization of input parameters for the
improvement of quality of the product of turning
operation on CNC machine. Feed Rate, Spindle
speed & depth of cut are taken as the input
parameters and the dimensional tolerances as
output parameter. In the present work L9 Array
has been used in design of experiment for
optimization of input parameters. This paper
attempts to introduce and thus verifies
experimentally as to how the Taguchi parameter
design could be used in identifying the significant
processing parameters and optimizing the
surface roughness in the turning operation. The
present work shows that spindle speed is the key
factor for minimizing the dimensional variation
for minimizing the surface roughness.
Keywords - Turning operation, Taguchi Method,
Dimensional Tolerance.
INTRODUCTION
Turning is the machining operation that produces
cylindrical parts. In its basic form, it can be defined
as the machining of an external surface:
 With the work piece rotating.
 With a single-point cutting tool, and
 With the cutting tool feeding parallel to the axis
of the work piece and at a distance that will
remove the outer surface of the work.
Turning is carried out on a lathe that
provides the power to turn the work piece at a given
rotational speed and to feed the cutting tool at
specified rate and depth of cut. Therefore three
cutting parameters namely cutting speed, feed and
depth of cut need to be determined in a turning
operation.
Whenever two machined surfaces come in
contact with one and the other, the quality of the
mating parts plays an important role in the
performance and wear of the mating parts. The
height, shape, arrangement and direction of these
surface irregularities on the work piece depend upon
a number of factors such as:
A) The machining variables which include
a) Cutting speed.
b) Feed.
c) Depth of cut.
d) Cutting tool wears, and
e) Several other parameters
Optimization refers to the art and science of
allocating scarce resources to the best possible
effect. The Taguchi method is a well known
technique that provides a systematic and efficient
methodology for design and process optimization.
This is due to the advantage of the design of
experiment using Taguchi’s technique that includes
simplification of experimental plan and feasibility of
study of interaction between different parameters.
Analysis of Variance (ANOVA) is then used to
determine which process parameter is statistically
significant and the contribution of each parameter
towards the output characteristic.
I. Literature Review: The most readily controlled
factors in a turning operation are feed rate, cutting
speed, and depth of cut; each of which may have an
effect on surface finish. Spindle speed and depth of
cut were found to have differing levels of effect in
each study, often playing a stronger role as part of
an interaction. The controlled parameters in a
turning operation that under normal conditions
affect surface finish most profoundly are feed rate
and cutting speed [1]. Recent studies that explore
the effect of setup and input parameters on surface
finish all find that there is a direct effect of feed rate
and spindle speed’s effect is generally nonlinear and
often interactive with other parameters, and that
depth of cut can have some effect due to heat
generation or chatter [2, 3, 4, and 5]. Several studies
exist which explore the effect of feed rate, spindle
speed, and depth of cut on surface finish [6, 7, 5, 8].
These studies all supported the idea that feed rate
has a strong influence on surface finish. Feng and
Wang (2002) [9] investigated for the prediction of
surface roughness in finish turning operation by
developing an empirical model through considering
working parameters. Kirby et al. (2004) [1]
Harish Kumar, Mohd. Abbas, Dr. Aas Mohammad, Hasan Zakir Jafri / International Journal
of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 3, May-Jun 2013, pp.331-334
332 | P a g e
developed the prediction model for surface
roughness in turning operation. Rafi & Islam [10]
present experimental and analytical results of an
investigation into dimensional accuracy and surface
finish achievable in dry turning. Tzeng Yih-Fang
[11] has taken a set of optimal turning parameters
for producing high dimensional precision and
accuracy in the computerized numerical control
turning process was developed. Shoukry [12] has
also performed an experiment to evaluate The
effects of speed, feed and depth of cut on the
dimensional accuracy of aluminum bars turned on a
lathe. Gilbert (1950) [13] studied the optimization of
machining parameters in turning with respect to
maximum production rate and minimum production
cost as criteria. Armarego & Brown (1969) [14]
investigated unconstrained machine-parameter
optimization using differential calculus. Brewer &
Rueda (1963) [15] carried out simplified optimum
analysis for non-ferrous materials. Petropoulos
(1973) [16] investigated optimal selection of
machining rate variables, viz. cutting speed and feed
rate, by geometric programming. Chanin et al
(1990) [17] remarked that Japanese companies such
as Nippon, Denso, NEC, and Fugitsu have become
world economic competitors by using Taguchi’s
approach which has potential for saving
experimental time and cost on product or process
development, as well as quality improvement. The
problem was to find an optimum set of conditions
that were to produce minimum surface roughness
and minimum dimensional tolerance.
Based on the above literature review there are two
aspects of this work. The first is to demonstrate a
systematic approach of using Taguchi parameter
design of process control of individual CNC turning
machine.
The second is to demonstrate the use of
Taguchi parameter design in order to identify the
optimum dimensional tolerance performance with a
particular combination of cutting parameters in a
CNC turning operation.
II. Problem Description:
The machining process on a CNC lathe is
programmed by speed, feed rate and cutting depth,
which are frequently determined based on the job
shop experiences. However, the machine
performance and the product characteristics are not
guaranteed to be acceptable. Therefore, the optimum
turning conditions have to be accomplished.
With all the viewpoints, this study proposes
an optimization approach using orthogonal array
and ANOVA, S/N ratios to optimize precision CNC
turning conditions.
III. Parameter Identification:
The input parameters which affect the
aforementioned output parameters are numerous
such as:
a) Cutting speed
b) Feed rate.
c) Depth of cut.
d) Side cutting edge angle
e) Type of power.
f) Cutting tool material.
g) Working temperature.
h) Operator.
i) Make of the CNC machine.
j) Noise.
In order to identity the process parameters,
affecting the selected machining quality
characteristic of turned parts, an Ishikawa cause-
effect diagram was constructed as shown in figure 1.
Fig. 1 Ishikawa cause-effect diagram of a turning
process
Selection of input parameters:
The following process parameters were selected for the
present work:
Cutting speed – (A),
Feed rate – (B),
Depth of cut – (C),
Tool material – HSS,
Work material – MS 1010,
Environment – Dry cutting.
In combination, speed, feed and depth of
cut were the primary factors investigated while the
secondary factors were not considered in the present
study.
In this study, L9(33
) orthogonal array of
Taguchi experiment was selected for three
parameters (speed, feed, depth of cut) with three
levels for optimizing the multi-objective (surface
roughness and dimensional tolerance) in precision
turning on an CNC lathe. Through the examination
of surface roughness (Ra) and the calculation of
dimensional tolerance; the multiple objectives are
then obtained. The multiple objectives can
additionally be integrated and introduced as the S/N
(signal to noise) ratio into the Taguchi experiment.
The mean effects for S/N ratios are moreover
analyzed to achieve the optimum turning
parameters. Through the verification results, it is
shown that both surface roughness and dimensional
tolerance from present optimum parameters are
greatly improved. Turning operation experiments
Harish Kumar, Mohd. Abbas, Dr. Aas Mohammad, Hasan Zakir Jafri / International Journal
of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 3, May-Jun 2013, pp.331-334
333 | P a g e
were carried out on a CNC lathe that provides the
power to turn the work piece at a given rotational
speed and to feed to the cutting tool at specified rate
and depth of cut. Therefore three cutting parameters
namely cutting speed, feed and depth of cut need to
be optimized.
Therefore, three parameters (i.e. Speed,
Feed & Depth of cut) as the input parameters and
the dimensional tolerances & surface roughness as
the output parameters are taken in the present
experimental setup.
The feasible space for the cutting parameters
was defined by varying the turning speed in the
range 1000-1600rpm, feed in the range 0.02-
0.04mm/rev. and depth of cut from 0.25 to 0.35mm.
Three levels of each cutting parameters were
selected as shown in table 1. Selected cutting
parameters were fed with the help of in-built control
panel of the CNC machine itself.
Parameters
Symbols
Units
Level–1
Level-2
Level–3
Speed A Rpm 1600 1300 1000
Feed B mm/rev. 0.04 0.03 0.02
Depth of cut C Mm 0.35 0.3 0.25
Table 1: Parameters and their levels
1. Results:
The results have shown in the tables below
Exp.No
.
PROCESS PARAMETER LEVELS
Speed
A
Feed rate
B
Depth of cut
C
1 1600 0.04 0.35
2 1600 0.03 0.30
3 1600 0.02 0.25
4 1300 0.04 0.25
5 1300 0.03 0.35
6 1300 0.02 0.30
7 1000 0.04 0.30
8 1000 0.03 0.25
9 1000 0.02 0.35
Table 2: Experimental Layout Using an L-9
Orthogonal Array
Nine experiments are conducted for the
above mentioned nine sets of parameters (speed,
feed rate & depth of cut) and in each experiment 20
numbers of pieces are made and are checked with
air gauge for dimensional tolerance and for surface
roughness the pieces are tested. The average value
of dimensional tolerance and surface roughness in
microns are listed in table 2.
EXPNO.
Factor Results
SPEED
(A)
FEED
(B)
DEPTH
OF
CUT
(C)
DIMENSIONAL
TOLERANCE
(MICRONS)
1 1600 0.04 0.35 2.192
2 1600 0.03 0.3 2.311
3 1600 0.02 0.25 2.157
4 1300 0.04 0.25 2.642
5 1300 0.03 0.35 2.810
6 1300 0.02 0.3 3.200
7 1000 0.04 0.3 2.186
8 1000 0.03 0.25 2.402
9 1000 0.02 0.35 2.900
Table 3: Experimental Results
The influence of the parameters are listed below
Sr. No Factor DOF %P
1 A 2 59.99 %
2 B 2 23.18%
3 C 2 8.13%
Table 4: Analysis of Dimensional Tolerance
CONCLUSION
The above work, experimently verify that
the Taguchi approch gives us the optimal parameters
in the CNC turnning prcess using High Speed Steel
cutting tools the optimum set of speed, feed rate
and depth of cut and the most affecting parameters
having the impact of 59.9% is Speed.
References:
[1] Kirby E. D., Zhang Z. and Chen J. C.,
(2004), “Development of An
Accelerometer based surface roughness
Prediction System in Turning Operation
Using Multiple Regression Techniques”,
Journal of Industrial Technology, Volume
20, Number 4, pp. 1-8.)
[2] Feng, C. X., & Wang, X. F., 2003, Surface
roughness predictive modeling: neural
networks versus regression. IIE
Transactions vol. 35(1), 11-27
[3] Gökkayaa, H., & Nalbant, M., 2007, The
effects of cutting tool geometry and
processing parameters on the surface
roughness of AISI 1030 steel, Materials &
Design, vol. 28(2), 717-721
[4] Lalwani, D.I. (2008). Experimental
investigations of cutting parameters
influence on cutting forces and surface
roughness in finish hard turning of
MDN250 steel. Journal of Materials
Processing Technology, 206(1-3), 167-179.
[5] Özel, T., Hsu, T.-K., & Zeren, E. (2005).
Effects of cutting edge geometry,
workpiece hardness, feed rate and cutting
speed on surface roughness and forces in
Harish Kumar, Mohd. Abbas, Dr. Aas Mohammad, Hasan Zakir Jafri / International Journal
of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 3, May-Jun 2013, pp.331-334
334 | P a g e
finish turning of hardened AISI H13 steel.
International Journal of Advanced
Manufacturing Technology, 25(3-4), 262-
269.
[6] Benardos, P.G. & Vosniakos, G.C.,
Prediction of surface roughness in CNC
face milling using neural networks and
Taguchi’s design of experiments, Robotics
and computer integrated manufacturing,
vol. 18 , 343-354.
[7] Dhavlikar, M.N., Kulkarni, M.S. &
Mariappan, V., 2003, Combined Taguchi
and dual response method for optimization
of a centerless grindinrningg operation,
Journal of Materials Processing
Technology, vol. 132, 90-94.
[8] Vernon, A., & Özel, T. (2003). Factors
affecting surface roughness in finish hard
turning. Paper presented at the 17th
International Conference on Production
Research, Blacksburg, Virginia.)
[9] Feng C. X. & Wang X., 2002,
Development of Empirical Models for
Surface Roughness Prediction in Finish
Turning, International Journal of Advanced
Manufacturing Technology, Vol. 20, 348-
356
[10] N. H. Rafai & M. N. Islam (2009) “An
Investigation Into Dimensional Accuracy
And Surface Finish Achievable In Dry
Turning” Machining Science and
Technology, Volume 13, Issue 4 October
2009 , pages 571 - 589 )
[11] Tzeng Yih-fong 2006“Parameter design
optimization of computerized numerical
control turning tool steels for high
dimensional precision and accuracy”
Materials & Design, Volume 27, Issue 8,
2006, Pages 665-675
[12] Shouckry, A.S. , 1982 “The effect of
cutting conditions on dimensional
accuracy” Wear, Volume 80, Issue 2, 16
August 1982, Pages 197-205.
[13] Gilbert, W. W., 1950, Economics of
machining. In Machining-Theory and
practice. Am. Soc. Met., 476-480
[14] Armarego, E. J. A., Brown, R. H., 1969,
The machining of metals (Englewood
Cliffs, NJ: Prentice Hall) ASME 1952
Research committee on metal cutting data
and bibliography. Manual on cutting of
metals with single point tools 2nd edn.
[15] Brewer, R. C. & Rueda, R., 1963, a
simplified approach to the optimum
selection of machining parameters. Eng.
Dig. Vol. 24 (9), 133-150
[16] Petropoulos P G 1973 Optimal selection of
machining rate variable by geometric
programming. Int. J. Prod. Res. 11: 305–
314
[17] Chanin, M. N., Kuei, C.H. & Lin, C., 1990,
Using Taguchi design, regression analysis
and simulation to study maintenance float
systems. Int. J. Prod. Res., vol. 28, 1939-
1953

More Related Content

What's hot

Taguchi based Optimization of Cutting Parameters Affecting Surface Roughness ...
Taguchi based Optimization of Cutting Parameters Affecting Surface Roughness ...Taguchi based Optimization of Cutting Parameters Affecting Surface Roughness ...
Taguchi based Optimization of Cutting Parameters Affecting Surface Roughness ...IRJET Journal
 
Experimental Analysis of Material Removal Rate in Drilling of 41Cr4 by a Tagu...
Experimental Analysis of Material Removal Rate in Drilling of 41Cr4 by a Tagu...Experimental Analysis of Material Removal Rate in Drilling of 41Cr4 by a Tagu...
Experimental Analysis of Material Removal Rate in Drilling of 41Cr4 by a Tagu...IJERA Editor
 
Analysis of process parameters in dry machining of en 31 steel by grey relati...
Analysis of process parameters in dry machining of en 31 steel by grey relati...Analysis of process parameters in dry machining of en 31 steel by grey relati...
Analysis of process parameters in dry machining of en 31 steel by grey relati...IAEME Publication
 
Investigations of machining parameters on surface roughness in cnc milling u...
Investigations of machining parameters on surface roughness in cnc  milling u...Investigations of machining parameters on surface roughness in cnc  milling u...
Investigations of machining parameters on surface roughness in cnc milling u...Alexander Decker
 
optimization of process parameters for cnc turning using taguchi methods for ...
optimization of process parameters for cnc turning using taguchi methods for ...optimization of process parameters for cnc turning using taguchi methods for ...
optimization of process parameters for cnc turning using taguchi methods for ...INFOGAIN PUBLICATION
 
Effect of process factors on surface roughness in dip cryogenic machining of ...
Effect of process factors on surface roughness in dip cryogenic machining of ...Effect of process factors on surface roughness in dip cryogenic machining of ...
Effect of process factors on surface roughness in dip cryogenic machining of ...eSAT Journals
 
A Review on Experimental Investigation of Machining Parameters during CNC Mac...
A Review on Experimental Investigation of Machining Parameters during CNC Mac...A Review on Experimental Investigation of Machining Parameters during CNC Mac...
A Review on Experimental Investigation of Machining Parameters during CNC Mac...IJERA Editor
 
IRJET- A Review on: Parametric Study for Optimization of CNC Turning Process ...
IRJET- A Review on: Parametric Study for Optimization of CNC Turning Process ...IRJET- A Review on: Parametric Study for Optimization of CNC Turning Process ...
IRJET- A Review on: Parametric Study for Optimization of CNC Turning Process ...IRJET Journal
 
Optimization of surface roughness in high speed end milling operation using
Optimization of surface roughness in  high speed end milling operation usingOptimization of surface roughness in  high speed end milling operation using
Optimization of surface roughness in high speed end milling operation usingIAEME Publication
 
Content Comparative Investigation on Tool Wear During End Milling of AISI H13...
Content Comparative Investigation on Tool Wear During End Milling of AISI H13...Content Comparative Investigation on Tool Wear During End Milling of AISI H13...
Content Comparative Investigation on Tool Wear During End Milling of AISI H13...journalBEEI
 
Optimization of Process Parameters for CNC Turning using Taguchi Methods for ...
Optimization of Process Parameters for CNC Turning using Taguchi Methods for ...Optimization of Process Parameters for CNC Turning using Taguchi Methods for ...
Optimization of Process Parameters for CNC Turning using Taguchi Methods for ...IRJET Journal
 

What's hot (16)

Taguchi based Optimization of Cutting Parameters Affecting Surface Roughness ...
Taguchi based Optimization of Cutting Parameters Affecting Surface Roughness ...Taguchi based Optimization of Cutting Parameters Affecting Surface Roughness ...
Taguchi based Optimization of Cutting Parameters Affecting Surface Roughness ...
 
Experimental Analysis of Material Removal Rate in Drilling of 41Cr4 by a Tagu...
Experimental Analysis of Material Removal Rate in Drilling of 41Cr4 by a Tagu...Experimental Analysis of Material Removal Rate in Drilling of 41Cr4 by a Tagu...
Experimental Analysis of Material Removal Rate in Drilling of 41Cr4 by a Tagu...
 
I43065157
I43065157I43065157
I43065157
 
T4201135138
T4201135138T4201135138
T4201135138
 
Analysis of process parameters in dry machining of en 31 steel by grey relati...
Analysis of process parameters in dry machining of en 31 steel by grey relati...Analysis of process parameters in dry machining of en 31 steel by grey relati...
Analysis of process parameters in dry machining of en 31 steel by grey relati...
 
K046026973
K046026973K046026973
K046026973
 
Investigations of machining parameters on surface roughness in cnc milling u...
Investigations of machining parameters on surface roughness in cnc  milling u...Investigations of machining parameters on surface roughness in cnc  milling u...
Investigations of machining parameters on surface roughness in cnc milling u...
 
Gt3511931198
Gt3511931198Gt3511931198
Gt3511931198
 
optimization of process parameters for cnc turning using taguchi methods for ...
optimization of process parameters for cnc turning using taguchi methods for ...optimization of process parameters for cnc turning using taguchi methods for ...
optimization of process parameters for cnc turning using taguchi methods for ...
 
IJSRDV2I1020
IJSRDV2I1020IJSRDV2I1020
IJSRDV2I1020
 
Effect of process factors on surface roughness in dip cryogenic machining of ...
Effect of process factors on surface roughness in dip cryogenic machining of ...Effect of process factors on surface roughness in dip cryogenic machining of ...
Effect of process factors on surface roughness in dip cryogenic machining of ...
 
A Review on Experimental Investigation of Machining Parameters during CNC Mac...
A Review on Experimental Investigation of Machining Parameters during CNC Mac...A Review on Experimental Investigation of Machining Parameters during CNC Mac...
A Review on Experimental Investigation of Machining Parameters during CNC Mac...
 
IRJET- A Review on: Parametric Study for Optimization of CNC Turning Process ...
IRJET- A Review on: Parametric Study for Optimization of CNC Turning Process ...IRJET- A Review on: Parametric Study for Optimization of CNC Turning Process ...
IRJET- A Review on: Parametric Study for Optimization of CNC Turning Process ...
 
Optimization of surface roughness in high speed end milling operation using
Optimization of surface roughness in  high speed end milling operation usingOptimization of surface roughness in  high speed end milling operation using
Optimization of surface roughness in high speed end milling operation using
 
Content Comparative Investigation on Tool Wear During End Milling of AISI H13...
Content Comparative Investigation on Tool Wear During End Milling of AISI H13...Content Comparative Investigation on Tool Wear During End Milling of AISI H13...
Content Comparative Investigation on Tool Wear During End Milling of AISI H13...
 
Optimization of Process Parameters for CNC Turning using Taguchi Methods for ...
Optimization of Process Parameters for CNC Turning using Taguchi Methods for ...Optimization of Process Parameters for CNC Turning using Taguchi Methods for ...
Optimization of Process Parameters for CNC Turning using Taguchi Methods for ...
 

Viewers also liked

Ecuaciones
EcuacionesEcuaciones
Ecuacionesmarlene
 
Anton Chistyakov. Personal Shopper
Anton Chistyakov. Personal ShopperAnton Chistyakov. Personal Shopper
Anton Chistyakov. Personal ShopperAntonChistyakov
 
6B - Jesus e kaique
6B - Jesus e kaique6B - Jesus e kaique
6B - Jesus e kaiqueviannota
 
Plan De Trabajo Contingencia Act
Plan De Trabajo Contingencia    ActPlan De Trabajo Contingencia    Act
Plan De Trabajo Contingencia Actcirculodeobreros
 
6B - Isabely e karolina n 10 n 18
6B - Isabely  e karolina  n 10  n 186B - Isabely  e karolina  n 10  n 18
6B - Isabely e karolina n 10 n 18viannota
 
KNOCKOUT! 2010
KNOCKOUT! 2010KNOCKOUT! 2010
KNOCKOUT! 2010RV4AV
 
Carrera Solidaria
Carrera SolidariaCarrera Solidaria
Carrera Solidariaddgm13
 
6A - Fabricio erick
6A - Fabricio erick6A - Fabricio erick
6A - Fabricio erickviannota
 
Marta e María
Marta e MaríaMarta e María
Marta e Maríadiruab
 
Paraiso natural
Paraiso naturalParaiso natural
Paraiso naturalgogloba
 
Tudo que você precisa saber sobre a Próstata
Tudo que você precisa saber sobre a PróstataTudo que você precisa saber sobre a Próstata
Tudo que você precisa saber sobre a PróstataOracy Filho
 

Viewers also liked (20)

Az33298300
Az33298300Az33298300
Az33298300
 
Bj33356362
Bj33356362Bj33356362
Bj33356362
 
Oj3425062509
Oj3425062509Oj3425062509
Oj3425062509
 
Ecuaciones
EcuacionesEcuaciones
Ecuaciones
 
Anton Chistyakov. Personal Shopper
Anton Chistyakov. Personal ShopperAnton Chistyakov. Personal Shopper
Anton Chistyakov. Personal Shopper
 
6B - Jesus e kaique
6B - Jesus e kaique6B - Jesus e kaique
6B - Jesus e kaique
 
Plan De Trabajo Contingencia Act
Plan De Trabajo Contingencia    ActPlan De Trabajo Contingencia    Act
Plan De Trabajo Contingencia Act
 
6B - Isabely e karolina n 10 n 18
6B - Isabely  e karolina  n 10  n 186B - Isabely  e karolina  n 10  n 18
6B - Isabely e karolina n 10 n 18
 
Lead on
Lead onLead on
Lead on
 
KNOCKOUT! 2010
KNOCKOUT! 2010KNOCKOUT! 2010
KNOCKOUT! 2010
 
Carrera Solidaria
Carrera SolidariaCarrera Solidaria
Carrera Solidaria
 
6A - Fabricio erick
6A - Fabricio erick6A - Fabricio erick
6A - Fabricio erick
 
Simfònics al Palau 2009-2010
Simfònics al Palau 2009-2010Simfònics al Palau 2009-2010
Simfònics al Palau 2009-2010
 
Marta e María
Marta e MaríaMarta e María
Marta e María
 
Paraiso natural
Paraiso naturalParaiso natural
Paraiso natural
 
Alex j
Alex jAlex j
Alex j
 
Tudo que você precisa saber sobre a Próstata
Tudo que você precisa saber sobre a PróstataTudo que você precisa saber sobre a Próstata
Tudo que você precisa saber sobre a Próstata
 
Membrana Celular
Membrana CelularMembrana Celular
Membrana Celular
 
Comunicado De Prensa
Comunicado De PrensaComunicado De Prensa
Comunicado De Prensa
 
Textos alunos para jornal (1)
Textos alunos para jornal (1)Textos alunos para jornal (1)
Textos alunos para jornal (1)
 

Similar to Be33331334

Experimental Investigation and Parametric Studies of Surface Roughness Analys...
Experimental Investigation and Parametric Studies of Surface Roughness Analys...Experimental Investigation and Parametric Studies of Surface Roughness Analys...
Experimental Investigation and Parametric Studies of Surface Roughness Analys...IJMER
 
Effects of the Cutting Conditions and Vibration on the Surface Roughness of E...
Effects of the Cutting Conditions and Vibration on the Surface Roughness of E...Effects of the Cutting Conditions and Vibration on the Surface Roughness of E...
Effects of the Cutting Conditions and Vibration on the Surface Roughness of E...IRJET Journal
 
Optimization of surface roughness in high speed end milling operation using
Optimization of surface roughness in  high speed end milling operation usingOptimization of surface roughness in  high speed end milling operation using
Optimization of surface roughness in high speed end milling operation usingIAEME Publication
 
A literature review on optimization of cutting parameters for surface roughne...
A literature review on optimization of cutting parameters for surface roughne...A literature review on optimization of cutting parameters for surface roughne...
A literature review on optimization of cutting parameters for surface roughne...IJERD Editor
 
Optimization of input parameters of cnc turning operation for the given comp
Optimization of input parameters of cnc turning operation for the given compOptimization of input parameters of cnc turning operation for the given comp
Optimization of input parameters of cnc turning operation for the given compIAEME Publication
 
Optimization of Turning Parameters Using Taguchi Method
Optimization of Turning Parameters Using Taguchi MethodOptimization of Turning Parameters Using Taguchi Method
Optimization of Turning Parameters Using Taguchi MethodIJMER
 
Taguchi Method for Optimization of Cutting Parameters in Turning Operations
Taguchi Method for Optimization of Cutting Parameters in Turning OperationsTaguchi Method for Optimization of Cutting Parameters in Turning Operations
Taguchi Method for Optimization of Cutting Parameters in Turning OperationsIDES Editor
 
Optimization of Turning Parameters Using Taguchi Method
Optimization of Turning Parameters Using Taguchi MethodOptimization of Turning Parameters Using Taguchi Method
Optimization of Turning Parameters Using Taguchi MethodIJMER
 
Milling machining GFRP composites using grey relational analysis and the resp...
Milling machining GFRP composites using grey relational analysis and the resp...Milling machining GFRP composites using grey relational analysis and the resp...
Milling machining GFRP composites using grey relational analysis and the resp...IRJET Journal
 
Determining the Influence of Various Cutting Parameters on Surface Roughness ...
Determining the Influence of Various Cutting Parameters on Surface Roughness ...Determining the Influence of Various Cutting Parameters on Surface Roughness ...
Determining the Influence of Various Cutting Parameters on Surface Roughness ...IOSR Journals
 
Turning parameters optimization for surface roughness by taguchi method
Turning parameters optimization for surface roughness by taguchi methodTurning parameters optimization for surface roughness by taguchi method
Turning parameters optimization for surface roughness by taguchi methodIAEME Publication
 
Process Parametric Optimization of CNC Vertical Milling Machine Using Taguchi...
Process Parametric Optimization of CNC Vertical Milling Machine Using Taguchi...Process Parametric Optimization of CNC Vertical Milling Machine Using Taguchi...
Process Parametric Optimization of CNC Vertical Milling Machine Using Taguchi...IOSR Journals
 
IRJET- Research Review on Multi-Objective Optimization of Machining Parameter...
IRJET- Research Review on Multi-Objective Optimization of Machining Parameter...IRJET- Research Review on Multi-Objective Optimization of Machining Parameter...
IRJET- Research Review on Multi-Objective Optimization of Machining Parameter...IRJET Journal
 
Experimental Investigation of Machining Parameters for Aluminum 6061 T6 Alloy
Experimental Investigation of Machining Parameters for Aluminum 6061 T6 AlloyExperimental Investigation of Machining Parameters for Aluminum 6061 T6 Alloy
Experimental Investigation of Machining Parameters for Aluminum 6061 T6 Alloyijtsrd
 
tcStatistical and regression analysis of vibration of carbon steel cutting to...
tcStatistical and regression analysis of vibration of carbon steel cutting to...tcStatistical and regression analysis of vibration of carbon steel cutting to...
tcStatistical and regression analysis of vibration of carbon steel cutting to...ijmech
 
IRJET- Multi-Objective Optimization of Machining Parameters by using Response...
IRJET- Multi-Objective Optimization of Machining Parameters by using Response...IRJET- Multi-Objective Optimization of Machining Parameters by using Response...
IRJET- Multi-Objective Optimization of Machining Parameters by using Response...IRJET Journal
 
“Optimization of Cutting Parameters for Turning AISI 316 Stainless Steel Base...
“Optimization of Cutting Parameters for Turning AISI 316 Stainless Steel Base...“Optimization of Cutting Parameters for Turning AISI 316 Stainless Steel Base...
“Optimization of Cutting Parameters for Turning AISI 316 Stainless Steel Base...IOSRJMCE
 
Analysis and Optimization Of Boring Process Parameters By Using Taguchi Metho...
Analysis and Optimization Of Boring Process Parameters By Using Taguchi Metho...Analysis and Optimization Of Boring Process Parameters By Using Taguchi Metho...
Analysis and Optimization Of Boring Process Parameters By Using Taguchi Metho...inventionjournals
 

Similar to Be33331334 (20)

Experimental Investigation and Parametric Studies of Surface Roughness Analys...
Experimental Investigation and Parametric Studies of Surface Roughness Analys...Experimental Investigation and Parametric Studies of Surface Roughness Analys...
Experimental Investigation and Parametric Studies of Surface Roughness Analys...
 
G012663743
G012663743G012663743
G012663743
 
Effects of the Cutting Conditions and Vibration on the Surface Roughness of E...
Effects of the Cutting Conditions and Vibration on the Surface Roughness of E...Effects of the Cutting Conditions and Vibration on the Surface Roughness of E...
Effects of the Cutting Conditions and Vibration on the Surface Roughness of E...
 
Optimization of surface roughness in high speed end milling operation using
Optimization of surface roughness in  high speed end milling operation usingOptimization of surface roughness in  high speed end milling operation using
Optimization of surface roughness in high speed end milling operation using
 
Ijetr042192
Ijetr042192Ijetr042192
Ijetr042192
 
A literature review on optimization of cutting parameters for surface roughne...
A literature review on optimization of cutting parameters for surface roughne...A literature review on optimization of cutting parameters for surface roughne...
A literature review on optimization of cutting parameters for surface roughne...
 
Optimization of input parameters of cnc turning operation for the given comp
Optimization of input parameters of cnc turning operation for the given compOptimization of input parameters of cnc turning operation for the given comp
Optimization of input parameters of cnc turning operation for the given comp
 
Optimization of Turning Parameters Using Taguchi Method
Optimization of Turning Parameters Using Taguchi MethodOptimization of Turning Parameters Using Taguchi Method
Optimization of Turning Parameters Using Taguchi Method
 
Taguchi Method for Optimization of Cutting Parameters in Turning Operations
Taguchi Method for Optimization of Cutting Parameters in Turning OperationsTaguchi Method for Optimization of Cutting Parameters in Turning Operations
Taguchi Method for Optimization of Cutting Parameters in Turning Operations
 
Optimization of Turning Parameters Using Taguchi Method
Optimization of Turning Parameters Using Taguchi MethodOptimization of Turning Parameters Using Taguchi Method
Optimization of Turning Parameters Using Taguchi Method
 
Milling machining GFRP composites using grey relational analysis and the resp...
Milling machining GFRP composites using grey relational analysis and the resp...Milling machining GFRP composites using grey relational analysis and the resp...
Milling machining GFRP composites using grey relational analysis and the resp...
 
Determining the Influence of Various Cutting Parameters on Surface Roughness ...
Determining the Influence of Various Cutting Parameters on Surface Roughness ...Determining the Influence of Various Cutting Parameters on Surface Roughness ...
Determining the Influence of Various Cutting Parameters on Surface Roughness ...
 
Turning parameters optimization for surface roughness by taguchi method
Turning parameters optimization for surface roughness by taguchi methodTurning parameters optimization for surface roughness by taguchi method
Turning parameters optimization for surface roughness by taguchi method
 
Process Parametric Optimization of CNC Vertical Milling Machine Using Taguchi...
Process Parametric Optimization of CNC Vertical Milling Machine Using Taguchi...Process Parametric Optimization of CNC Vertical Milling Machine Using Taguchi...
Process Parametric Optimization of CNC Vertical Milling Machine Using Taguchi...
 
IRJET- Research Review on Multi-Objective Optimization of Machining Parameter...
IRJET- Research Review on Multi-Objective Optimization of Machining Parameter...IRJET- Research Review on Multi-Objective Optimization of Machining Parameter...
IRJET- Research Review on Multi-Objective Optimization of Machining Parameter...
 
Experimental Investigation of Machining Parameters for Aluminum 6061 T6 Alloy
Experimental Investigation of Machining Parameters for Aluminum 6061 T6 AlloyExperimental Investigation of Machining Parameters for Aluminum 6061 T6 Alloy
Experimental Investigation of Machining Parameters for Aluminum 6061 T6 Alloy
 
tcStatistical and regression analysis of vibration of carbon steel cutting to...
tcStatistical and regression analysis of vibration of carbon steel cutting to...tcStatistical and regression analysis of vibration of carbon steel cutting to...
tcStatistical and regression analysis of vibration of carbon steel cutting to...
 
IRJET- Multi-Objective Optimization of Machining Parameters by using Response...
IRJET- Multi-Objective Optimization of Machining Parameters by using Response...IRJET- Multi-Objective Optimization of Machining Parameters by using Response...
IRJET- Multi-Objective Optimization of Machining Parameters by using Response...
 
“Optimization of Cutting Parameters for Turning AISI 316 Stainless Steel Base...
“Optimization of Cutting Parameters for Turning AISI 316 Stainless Steel Base...“Optimization of Cutting Parameters for Turning AISI 316 Stainless Steel Base...
“Optimization of Cutting Parameters for Turning AISI 316 Stainless Steel Base...
 
Analysis and Optimization Of Boring Process Parameters By Using Taguchi Metho...
Analysis and Optimization Of Boring Process Parameters By Using Taguchi Metho...Analysis and Optimization Of Boring Process Parameters By Using Taguchi Metho...
Analysis and Optimization Of Boring Process Parameters By Using Taguchi Metho...
 

Recently uploaded

SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024Lorenzo Miniero
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 3652toLead Limited
 
Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfAddepto
 
Install Stable Diffusion in windows machine
Install Stable Diffusion in windows machineInstall Stable Diffusion in windows machine
Install Stable Diffusion in windows machinePadma Pradeep
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr BaganFwdays
 
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Patryk Bandurski
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Mark Simos
 
Pigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food ManufacturingPigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food ManufacturingPigging Solutions
 
Story boards and shot lists for my a level piece
Story boards and shot lists for my a level pieceStory boards and shot lists for my a level piece
Story boards and shot lists for my a level piececharlottematthew16
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek SchlawackFwdays
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii SoldatenkoFwdays
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brandgvaughan
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebUiPathCommunity
 
Understanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitectureUnderstanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitecturePixlogix Infotech
 
Vertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering TipsVertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering TipsMiki Katsuragi
 
Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Enterprise Knowledge
 
Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxhariprasad279825
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLScyllaDB
 

Recently uploaded (20)

SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
Tech-Forward - Achieving Business Readiness For Copilot in Microsoft 365
 
Gen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdfGen AI in Business - Global Trends Report 2024.pdf
Gen AI in Business - Global Trends Report 2024.pdf
 
Install Stable Diffusion in windows machine
Install Stable Diffusion in windows machineInstall Stable Diffusion in windows machine
Install Stable Diffusion in windows machine
 
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan
 
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
 
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
Tampa BSides - Chef's Tour of Microsoft Security Adoption Framework (SAF)
 
Pigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food ManufacturingPigging Solutions in Pet Food Manufacturing
Pigging Solutions in Pet Food Manufacturing
 
Story boards and shot lists for my a level piece
Story boards and shot lists for my a level pieceStory boards and shot lists for my a level piece
Story boards and shot lists for my a level piece
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
 
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko
 
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your BrandWordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brand
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio Web
 
Understanding the Laravel MVC Architecture
Understanding the Laravel MVC ArchitectureUnderstanding the Laravel MVC Architecture
Understanding the Laravel MVC Architecture
 
Vertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering TipsVertex AI Gemini Prompt Engineering Tips
Vertex AI Gemini Prompt Engineering Tips
 
Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024
 
Artificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptxArtificial intelligence in cctv survelliance.pptx
Artificial intelligence in cctv survelliance.pptx
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQL
 

Be33331334

  • 1. Harish Kumar, Mohd. Abbas, Dr. Aas Mohammad, Hasan Zakir Jafri / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 3, May-Jun 2013, pp.331-334 331 | P a g e Optimization of cutting parameters in CNC Turning Harish Kumar1 , Mohd. Abbas 2 , Dr. Aas Mohammad3 , Hasan Zakir Jafri4 1, Research Scholar, Al-Falah School of Engineering & Technology 2,4, Assistant Professor, Department of Mechanical Engineering, Al-Falah School of Engineering & Technology Village Dhauj, Faridabad, Haryana 3, Associate Professor, Department of Mechanical Engineering, Faculty of Engineering & Technology, Jamia Millia Islamia, New Delhi ABSTRACT Many manufacturing industries involve machining operations. In metal cutting the turning process is one of the most fundamental cutting processes used. Surface finish and dimensional tolerance, are used to determine and evaluate the quality of a product, and are major quality attributes of a turned product. In this paper experimental work has been carried out for the optimization of input parameters for the improvement of quality of the product of turning operation on CNC machine. Feed Rate, Spindle speed & depth of cut are taken as the input parameters and the dimensional tolerances as output parameter. In the present work L9 Array has been used in design of experiment for optimization of input parameters. This paper attempts to introduce and thus verifies experimentally as to how the Taguchi parameter design could be used in identifying the significant processing parameters and optimizing the surface roughness in the turning operation. The present work shows that spindle speed is the key factor for minimizing the dimensional variation for minimizing the surface roughness. Keywords - Turning operation, Taguchi Method, Dimensional Tolerance. INTRODUCTION Turning is the machining operation that produces cylindrical parts. In its basic form, it can be defined as the machining of an external surface:  With the work piece rotating.  With a single-point cutting tool, and  With the cutting tool feeding parallel to the axis of the work piece and at a distance that will remove the outer surface of the work. Turning is carried out on a lathe that provides the power to turn the work piece at a given rotational speed and to feed the cutting tool at specified rate and depth of cut. Therefore three cutting parameters namely cutting speed, feed and depth of cut need to be determined in a turning operation. Whenever two machined surfaces come in contact with one and the other, the quality of the mating parts plays an important role in the performance and wear of the mating parts. The height, shape, arrangement and direction of these surface irregularities on the work piece depend upon a number of factors such as: A) The machining variables which include a) Cutting speed. b) Feed. c) Depth of cut. d) Cutting tool wears, and e) Several other parameters Optimization refers to the art and science of allocating scarce resources to the best possible effect. The Taguchi method is a well known technique that provides a systematic and efficient methodology for design and process optimization. This is due to the advantage of the design of experiment using Taguchi’s technique that includes simplification of experimental plan and feasibility of study of interaction between different parameters. Analysis of Variance (ANOVA) is then used to determine which process parameter is statistically significant and the contribution of each parameter towards the output characteristic. I. Literature Review: The most readily controlled factors in a turning operation are feed rate, cutting speed, and depth of cut; each of which may have an effect on surface finish. Spindle speed and depth of cut were found to have differing levels of effect in each study, often playing a stronger role as part of an interaction. The controlled parameters in a turning operation that under normal conditions affect surface finish most profoundly are feed rate and cutting speed [1]. Recent studies that explore the effect of setup and input parameters on surface finish all find that there is a direct effect of feed rate and spindle speed’s effect is generally nonlinear and often interactive with other parameters, and that depth of cut can have some effect due to heat generation or chatter [2, 3, 4, and 5]. Several studies exist which explore the effect of feed rate, spindle speed, and depth of cut on surface finish [6, 7, 5, 8]. These studies all supported the idea that feed rate has a strong influence on surface finish. Feng and Wang (2002) [9] investigated for the prediction of surface roughness in finish turning operation by developing an empirical model through considering working parameters. Kirby et al. (2004) [1]
  • 2. Harish Kumar, Mohd. Abbas, Dr. Aas Mohammad, Hasan Zakir Jafri / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 3, May-Jun 2013, pp.331-334 332 | P a g e developed the prediction model for surface roughness in turning operation. Rafi & Islam [10] present experimental and analytical results of an investigation into dimensional accuracy and surface finish achievable in dry turning. Tzeng Yih-Fang [11] has taken a set of optimal turning parameters for producing high dimensional precision and accuracy in the computerized numerical control turning process was developed. Shoukry [12] has also performed an experiment to evaluate The effects of speed, feed and depth of cut on the dimensional accuracy of aluminum bars turned on a lathe. Gilbert (1950) [13] studied the optimization of machining parameters in turning with respect to maximum production rate and minimum production cost as criteria. Armarego & Brown (1969) [14] investigated unconstrained machine-parameter optimization using differential calculus. Brewer & Rueda (1963) [15] carried out simplified optimum analysis for non-ferrous materials. Petropoulos (1973) [16] investigated optimal selection of machining rate variables, viz. cutting speed and feed rate, by geometric programming. Chanin et al (1990) [17] remarked that Japanese companies such as Nippon, Denso, NEC, and Fugitsu have become world economic competitors by using Taguchi’s approach which has potential for saving experimental time and cost on product or process development, as well as quality improvement. The problem was to find an optimum set of conditions that were to produce minimum surface roughness and minimum dimensional tolerance. Based on the above literature review there are two aspects of this work. The first is to demonstrate a systematic approach of using Taguchi parameter design of process control of individual CNC turning machine. The second is to demonstrate the use of Taguchi parameter design in order to identify the optimum dimensional tolerance performance with a particular combination of cutting parameters in a CNC turning operation. II. Problem Description: The machining process on a CNC lathe is programmed by speed, feed rate and cutting depth, which are frequently determined based on the job shop experiences. However, the machine performance and the product characteristics are not guaranteed to be acceptable. Therefore, the optimum turning conditions have to be accomplished. With all the viewpoints, this study proposes an optimization approach using orthogonal array and ANOVA, S/N ratios to optimize precision CNC turning conditions. III. Parameter Identification: The input parameters which affect the aforementioned output parameters are numerous such as: a) Cutting speed b) Feed rate. c) Depth of cut. d) Side cutting edge angle e) Type of power. f) Cutting tool material. g) Working temperature. h) Operator. i) Make of the CNC machine. j) Noise. In order to identity the process parameters, affecting the selected machining quality characteristic of turned parts, an Ishikawa cause- effect diagram was constructed as shown in figure 1. Fig. 1 Ishikawa cause-effect diagram of a turning process Selection of input parameters: The following process parameters were selected for the present work: Cutting speed – (A), Feed rate – (B), Depth of cut – (C), Tool material – HSS, Work material – MS 1010, Environment – Dry cutting. In combination, speed, feed and depth of cut were the primary factors investigated while the secondary factors were not considered in the present study. In this study, L9(33 ) orthogonal array of Taguchi experiment was selected for three parameters (speed, feed, depth of cut) with three levels for optimizing the multi-objective (surface roughness and dimensional tolerance) in precision turning on an CNC lathe. Through the examination of surface roughness (Ra) and the calculation of dimensional tolerance; the multiple objectives are then obtained. The multiple objectives can additionally be integrated and introduced as the S/N (signal to noise) ratio into the Taguchi experiment. The mean effects for S/N ratios are moreover analyzed to achieve the optimum turning parameters. Through the verification results, it is shown that both surface roughness and dimensional tolerance from present optimum parameters are greatly improved. Turning operation experiments
  • 3. Harish Kumar, Mohd. Abbas, Dr. Aas Mohammad, Hasan Zakir Jafri / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 3, May-Jun 2013, pp.331-334 333 | P a g e were carried out on a CNC lathe that provides the power to turn the work piece at a given rotational speed and to feed to the cutting tool at specified rate and depth of cut. Therefore three cutting parameters namely cutting speed, feed and depth of cut need to be optimized. Therefore, three parameters (i.e. Speed, Feed & Depth of cut) as the input parameters and the dimensional tolerances & surface roughness as the output parameters are taken in the present experimental setup. The feasible space for the cutting parameters was defined by varying the turning speed in the range 1000-1600rpm, feed in the range 0.02- 0.04mm/rev. and depth of cut from 0.25 to 0.35mm. Three levels of each cutting parameters were selected as shown in table 1. Selected cutting parameters were fed with the help of in-built control panel of the CNC machine itself. Parameters Symbols Units Level–1 Level-2 Level–3 Speed A Rpm 1600 1300 1000 Feed B mm/rev. 0.04 0.03 0.02 Depth of cut C Mm 0.35 0.3 0.25 Table 1: Parameters and their levels 1. Results: The results have shown in the tables below Exp.No . PROCESS PARAMETER LEVELS Speed A Feed rate B Depth of cut C 1 1600 0.04 0.35 2 1600 0.03 0.30 3 1600 0.02 0.25 4 1300 0.04 0.25 5 1300 0.03 0.35 6 1300 0.02 0.30 7 1000 0.04 0.30 8 1000 0.03 0.25 9 1000 0.02 0.35 Table 2: Experimental Layout Using an L-9 Orthogonal Array Nine experiments are conducted for the above mentioned nine sets of parameters (speed, feed rate & depth of cut) and in each experiment 20 numbers of pieces are made and are checked with air gauge for dimensional tolerance and for surface roughness the pieces are tested. The average value of dimensional tolerance and surface roughness in microns are listed in table 2. EXPNO. Factor Results SPEED (A) FEED (B) DEPTH OF CUT (C) DIMENSIONAL TOLERANCE (MICRONS) 1 1600 0.04 0.35 2.192 2 1600 0.03 0.3 2.311 3 1600 0.02 0.25 2.157 4 1300 0.04 0.25 2.642 5 1300 0.03 0.35 2.810 6 1300 0.02 0.3 3.200 7 1000 0.04 0.3 2.186 8 1000 0.03 0.25 2.402 9 1000 0.02 0.35 2.900 Table 3: Experimental Results The influence of the parameters are listed below Sr. No Factor DOF %P 1 A 2 59.99 % 2 B 2 23.18% 3 C 2 8.13% Table 4: Analysis of Dimensional Tolerance CONCLUSION The above work, experimently verify that the Taguchi approch gives us the optimal parameters in the CNC turnning prcess using High Speed Steel cutting tools the optimum set of speed, feed rate and depth of cut and the most affecting parameters having the impact of 59.9% is Speed. References: [1] Kirby E. D., Zhang Z. and Chen J. C., (2004), “Development of An Accelerometer based surface roughness Prediction System in Turning Operation Using Multiple Regression Techniques”, Journal of Industrial Technology, Volume 20, Number 4, pp. 1-8.) [2] Feng, C. X., & Wang, X. F., 2003, Surface roughness predictive modeling: neural networks versus regression. IIE Transactions vol. 35(1), 11-27 [3] Gökkayaa, H., & Nalbant, M., 2007, The effects of cutting tool geometry and processing parameters on the surface roughness of AISI 1030 steel, Materials & Design, vol. 28(2), 717-721 [4] Lalwani, D.I. (2008). Experimental investigations of cutting parameters influence on cutting forces and surface roughness in finish hard turning of MDN250 steel. Journal of Materials Processing Technology, 206(1-3), 167-179. [5] Özel, T., Hsu, T.-K., & Zeren, E. (2005). Effects of cutting edge geometry, workpiece hardness, feed rate and cutting speed on surface roughness and forces in
  • 4. Harish Kumar, Mohd. Abbas, Dr. Aas Mohammad, Hasan Zakir Jafri / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 3, May-Jun 2013, pp.331-334 334 | P a g e finish turning of hardened AISI H13 steel. International Journal of Advanced Manufacturing Technology, 25(3-4), 262- 269. [6] Benardos, P.G. & Vosniakos, G.C., Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments, Robotics and computer integrated manufacturing, vol. 18 , 343-354. [7] Dhavlikar, M.N., Kulkarni, M.S. & Mariappan, V., 2003, Combined Taguchi and dual response method for optimization of a centerless grindinrningg operation, Journal of Materials Processing Technology, vol. 132, 90-94. [8] Vernon, A., & Özel, T. (2003). Factors affecting surface roughness in finish hard turning. Paper presented at the 17th International Conference on Production Research, Blacksburg, Virginia.) [9] Feng C. X. & Wang X., 2002, Development of Empirical Models for Surface Roughness Prediction in Finish Turning, International Journal of Advanced Manufacturing Technology, Vol. 20, 348- 356 [10] N. H. Rafai & M. N. Islam (2009) “An Investigation Into Dimensional Accuracy And Surface Finish Achievable In Dry Turning” Machining Science and Technology, Volume 13, Issue 4 October 2009 , pages 571 - 589 ) [11] Tzeng Yih-fong 2006“Parameter design optimization of computerized numerical control turning tool steels for high dimensional precision and accuracy” Materials & Design, Volume 27, Issue 8, 2006, Pages 665-675 [12] Shouckry, A.S. , 1982 “The effect of cutting conditions on dimensional accuracy” Wear, Volume 80, Issue 2, 16 August 1982, Pages 197-205. [13] Gilbert, W. W., 1950, Economics of machining. In Machining-Theory and practice. Am. Soc. Met., 476-480 [14] Armarego, E. J. A., Brown, R. H., 1969, The machining of metals (Englewood Cliffs, NJ: Prentice Hall) ASME 1952 Research committee on metal cutting data and bibliography. Manual on cutting of metals with single point tools 2nd edn. [15] Brewer, R. C. & Rueda, R., 1963, a simplified approach to the optimum selection of machining parameters. Eng. Dig. Vol. 24 (9), 133-150 [16] Petropoulos P G 1973 Optimal selection of machining rate variable by geometric programming. Int. J. Prod. Res. 11: 305– 314 [17] Chanin, M. N., Kuei, C.H. & Lin, C., 1990, Using Taguchi design, regression analysis and simulation to study maintenance float systems. Int. J. Prod. Res., vol. 28, 1939- 1953