SlideShare ist ein Scribd-Unternehmen logo
1 von 4
Downloaden Sie, um offline zu lesen
IJSRD - International Journal for Scientific Research & Development| Vol. 4, Issue 07, 2016 | ISSN (online): 2321-0613
All rights reserved by www.ijsrd.com 618
Assessing Cutting Fluid as a Key Parameter in Evaluating Surface Finish
for CNC Turning Operation
Swati Arvind Kekade1 V.V. Potdar2 Prof. Swapnil S.Kulkarni3
1,2
Student 3
Associate Professor
1,2
Department of Manufacturing Process Engineering
1,2
A.G.P.I.T. Solapur University 3
Advent Tool Tech India Pvt. Ltd., Pune
Abstract— The tool work piece combination of the Raw
material HCHCr with cemented carbide and CBN tools shall
be explored in this work. The parameters – Speed, Feed and
Depth of cut to be maintained for HCHCr shall be
determined in terms of optional values or levels for each of
the tool type. Historic data shall be referred from the records
of the company to understand the effect of introducing
variation in the input to realize a given output as a
‘Response’ for surface finish. Statistical data analysis shall
be performed using ANOVA, DOE and Regression to
identify the given machining parameters for the operation.
Experimental run shall be conducted for validating the values
realized through this Analytical treatment of the data.
Surface roughness tester shall be employed for determining
the response. Recommendation to be offered at the
concluding phase of this work in terms of the ‘optional set of
values’ for the process parameters.
Key words: Turning, HCHCr, Cemented Carbide, CBN,
Surface roughness, process optimization
I. INTRODUCTION
The challenge of modern machining industries is mainly
focused on the achievement of high quality, in terms of
work piece dimensional accuracy, surface finish, and high
production rate, less wear on the cutting tools, economy of
machining in terms of cost saving and increase the
performance of the product with reduced environmental
impact. Surface roughness plays an important role in many
areas and is a factor of great importance in the evaluation of
machining accuracy. The Taguchi method is statistical tool,
adopted experimentally to investigate influence of surface
roughness by cutting parameters such as cutting speed, feed
and depth of cut. The Taguchi process helps to select or to
determine the optimum cutting conditions for turning
process. Many researchers developed many mathematical
models to optimize the cutting parameters to get lowest
surface roughness by turning process. The variation in the
material hardness, alloying elements present in the work
piece material and other factors affecting surface finish and
tool wear.
II. LITERATURE REVIEW
Wojciech Zębala*, Robert Kowalczyk, Andrzej Matras [1]
The investigation shows the result analysis of the recorded
total cutting force components (Ff, Fp, Fc) during turning of
WC-Co (25 % Co) with tools made of polycrystalline
diamond PCD. Turning tests of sintered carbides (WC Co
shaft with content of 25% Co) accomplished by the inserts
with three different nose radii, each cutting test had been
carried out on the distance 54 mm at the constant depth of
cut 0.2 mm. it was noticed, the largest growth is for the
passive force component Fp. In addition, the mathematical
models for cutting force value prediction as the cutting path
increases are presented. There is presentation of the
algorithm of optimization and control of the super hard
materials turning process.
S.Sivasankarana*, P.T.Harisagarb, E.Saminathanb,
S. Siddharthb, P.Sasikumarb [2] This investigation shows
the effect of process parameters on surface roughness of
glass fiber reinforced polymer (GFRP) composite pipes. On
the basis of high speed turning centre (CNC) with poly-
crystalline diamond (PCD) tool experiments had been
conducted. The cutting speed, feed, depth of cut, and work
piece type (E-Glass mat and E-Glass woven specimen) were
considered as process parameter. The pipes used in
experiment were fabricated using the filament winding
process. It consists of 70 percent epoxy polyester resin and
30 percent glass fiber. The E-Glass woven and E-Glass mat
fiber reinforced composite material had been compared. The
mechanical properties had also conducted as per ASTM
standards. Based on the results, it has been observed that
good machinability had obtained at lower cutting speed,
feed rate, depth of cut for mat fiber reinforced GFRP pipe.
B.Singarvela*,T.Selvarajb , R.Jeyapaulc[3] In this
experimental analysis Taguchi based utility concept is used
coupled with Principal Component Analysis (PCA) on
turning of EN25 steel with CVD and PVD coated carbide
tools to estimate the optimum machining parameters. For
obtaining the simultaneous minimization of surface
roughness, cutting force and maximization of material
removal rate this method has been employed. The multi SN
ratio is calculated by the product of weight factor and SN
ratio to the performance characteristics in the utility concept.
The weight factors involved for all objectives found by
adopting the Principal component analysis. Finally the
relative significance of machining parameter in terms of
their percentage contribution had found by applying
ANOVA concept on multi SN ration.
K. Wonggasema,*, T. Wagnerb, H. Trautmanna, D.
Biermannb, C. Weihsa[4] In this investigation there is a
demonstration of an optimization of the PCA-based DI
based on empirical models of hard turning of AISI 6150
steel in which uncertainties are propagated by model errors.
The obtained results are the degree of importance of each
performance measure has been adjusted by the integration of
the covariance information into the overall performance
index .
S.J. Raykara*, D.M. D'Addonab, A.M. Manea[5]
This paper the Grey Relational Analysis (GRA) is used for
investigation of High Speed Turning of Al 7075, a high
strength aluminium alloy used for aerospace applications.
GRA is also used for Multi-objective optimization of
surface roughness, power consumption, material removal
rate and cutting time which are some important parameters
to decide the capability and suitability of high speed turning.
By machining Al 7075 at high cutting speeds the
Assessing Cutting Fluid as a Key Parameter in Evaluating Surface Finish for CNC Turning Operation
(IJSRD/Vol. 4/Issue 07/2016/148)
All rights reserved by www.ijsrd.com 619
investigation of performance of coated and uncoated carbide
cutting tool has been done. On the basis of GRA results
Suitable cutting parameters with appropriate cutting tool for
high speed turning of Al 7075 are suggested at the end.
R. Venkata Rao*, V.D. Kalyankar[6] This paper
focused on the primary objective in multi-pass turning
operations is to produce products with low cost and high
quality, with a lower number of cuts. Parameter
optimization is the very important process to achieve this
goal. It usually involves the optimal selection of cutting
speed, feed rate, depth of cut and number of passes.
Recently developed advanced optimization algorithm,
named, the teaching–learning-based optimization algorithm
had been used in this process, for parameter optimization of
a multi-pass turning operation. Two different examples
attempted previously by various researchers using different
optimization techniques, such as simulated annealing, the
genetic algorithm, the ant colony algorithm, and particle
swarm optimization, etc. had been considered here. The first
example is a multi-objective problem and the second
example is a single objective multi-constrained problem
with 20 constraints. The effectiveness of teaching–learning-
based optimization algorithm had been proved over other
algorithms.
P. Jayaramana*, L. Mahesh kumarb[7] A novel
approach was presented for the optimization of machining
parameters on turning of AA 6063 T6 aluminium alloy with
multiple responses based on orthogonal array with grey
relational analysis. Experiments were conducted on AA
6063 T6 aluminium alloy. Turning tests were carried out
using uncoated carbide insert under dry cutting condition.
Optimization of turning parameters such as cutting speed,
feed rate and depth of cut has been done considering the
multiple responses such as surface roughness (Ra),
roundness (Ø) and material removal rate (MRR). The grey
analysis was used to determine grey relational grade (GRG).
Based on the values of grey relational grade optimum levels
of parameters had identified and then by ANOVA the
significant contribution of parameters was determined.
Confirmation test was performed to validate the test result.
Experimental outcomes had proved that the responses in
turning process can be enhanced efficiently through this
fresh approach.
Yansong Guoa, Jef Loendersb, Joost Dufloua, Bert
Lauwersa, *[8] In this paper, an approach which
incorporates both energy consumption and surface
roughness was presented for optimizing the cutting
parameters in finish turning. Based on a new energy model
and a surface roughness model, derived for a given machine
tool, cutting parameters were optimized to accomplish a
precise surface finish with minimum energy consumption.
Deepak Da*Rajendra Bb[9] This paper focused on
the optimization of the process parameter such as cutting
speed, depth of cut and feed rate on surface roughness
produced on the machined component. Analysis was carried
out using Taguchi robust design principles. From the
analysis, feed rate was found to be the most influential
process parameters which influence the surface roughness
followed by cutting speed and depth of cut. Increase in feed
rate and depth of cut was found to increase the surface
roughness.
M. Durairaja,*, S. Gowri[10] This paper deals with
CNC Micro turning of Inconel 600 alloy with titanium
carbide coated tool. Machining has been done in DT-110
integrated multiprocessor micro machine tool. Micro turning
was carried out with full factorial experiments with various
combinations of cutting parameters such as speed (25,31,
and 37 m/min), feed (5,10, and 15 μm/rev) and depth of cut
(30,50 and 70 μm). For every set of experiments, the output
parameters such as the tool wear and the surface roughness
were measured. Non-linear regression model was used to
represent relationship between input and output variables
and a multi-objective optimization method based genetic
algorithm was used to optimize the cutting parameters in
turning process such as cutting speed, feed and depth of cut.
Two conflicting objectives such as tool wear and surface
roughness were simultaneously optimized.
III. PROBLEM STATEMENT
The CNC turning involves holding the work piece firmly in
the self centering chuck and rotating the work piece against
a sharp tool tip to effect material removal through chip
formation. This machining process is widely used in the
industry for achieving high material removal rate (MRR)
and a smooth finish over the part produced (Ra value). The
popular material used in the industry dealing with Die,
Molds and Fixtures uses HCHCr for parts that require high
hardness combined with good toughness to absorb shocks.
The concerned company supporting this work deals
in the tooling domain and uses the said material – HCHCr
for producing pins, shafts and other components with radial
symmetry. The process at the production shop floor arms at
high rate of production (higher MMR) with dimensional
accuracy and a good surface finish. For initiation the historic
data collected in the past for a given set of process
parameters yielding a certain output in terms of Ra value is
available with the company. This data could be referred for
analysis while additional experiments could be performed
towards the concluding phase of validation.
The process parameters - Namely, speed, feed and
the Depth of cut and the type of coolant for the operation are
identified as the prime parameters influencing the ‘MRR’
and the `Ra value’. Any variation over the values for these
parameters shall directly results in alteration over the output
realized i.e. the ‘Response’ would change based on the
degree of change in the input.
Research work needs to be undertaken to find the
optimum values for each parameter that would render a
desired Ra value for a satisfactory MRR. The tool to be used
for the experiment shall be CBN. The choice of alternative
coolant fluid shall be evaluated by administering three
values of suitable coolants.
Optional values for each type of coolant shall be
determined through this work.
IV. SCOPE
The scope of the work shall be limited to collection of input
data for the influencing process parameters – namely,
Speed, Feed and Depth of cut, performing statistical analysis
for the data, validating the results through experimentation
and recommending the best set of values that optimizes the
conflicting requirements of a higher MRR and a good
Assessing Cutting Fluid as a Key Parameter in Evaluating Surface Finish for CNC Turning Operation
(IJSRD/Vol. 4/Issue 07/2016/148)
All rights reserved by www.ijsrd.com 620
surface finish. The work shall be purchased as per the
concerned company supporting this research work.
V. OBJECTIVES
 Conduct literature survey for documenting the findings
of the various authors working on this problem area.
 Determine the process parameters through this
literature review and the inputs offered by the
supporting company.
 Design the experiment and collect the experimental
data for the responses chosen.
 Conduct analysis in the statistical domain to determine
the optimum values.
 Perform physical experiment for validating the results
determined.
 Offer recommendation with the optional value set for
the process parameters.
VI. METHODOLOGY
Analytical approach – This methodology is developed by
collecting the requisite input data from past records or
through experiments. The data is later subjected to treatment
using statistical tools like Normality plots, one-way
ANOVA, Taguchi methods or any suitable technique for
DOE and optimization. Regression equation could be
secured for estimating the anticipated results while making a
change over the input value. The trade-off for the response
can be accessed through the Analytical methodology for
finding solution.
VII. FLOW CHART FOR METHODOLOGY
Fig. 1: Flowchart for methodology
A. Physical Experimentation
The methodology is widely accepted and trusted for the
results it offers.
Typically, it is deployed as an alternative
methodology for validation of the results obtained using the
other methodologies. In our case, besides using it for
validation, it shall also offer values for responses for a given
of levels for the parameters. These responses are crucial for
being able to the analysis of the data.
B. Validation
The physical experimentation performed for the optimal
values shall offer validation for the results obtained using
statistical methods. The anticipated variation in the results
determined by individual methodologies could be the tuneof
10 to 15% by virtue of inherent variations in the
experimental setup or the ambient conditions during
machining. The results could be shared with the supporting
company upon conducting the experimental run towards
validation.
VIII. CONCLUSION (FOR STAGE 1)
The work undertakes to study the effect coolant over the
processing parameters. This type of study is unique as has
been investigated over the literature review. The coolants
are proposed upon studying the suitability of the same for
the given environment of machining. The work is expected
to bring forth optimal values based on the selection of the
coolant.
REFERENCES
[1] Wojciech Zębala*, Robert Kowalczyk, Andrzej Matras,
‘‘Analysis and Optimization of Sintered Carbides
Turning with PCD Tools”; 25th DAAAM International
Symposium on Intelligent Manufacturing and
Automation, DAAAM 2014: 283-290.
[2] [2] S.Sivasankarana*, P.T.Harisagarb, E.Saminathanb,
S. Siddharthb, P.Sasikumarb, ‘‘Effect of process
parameters in surface roughness during turning of
GFRP pipes using PCD insert tool”; 12th GLOBAL
CONGRESS ON MANUFACTURING AND
MANAGEMENT, GCMM 2014: 64-71.
[3] B.Singarvela*,T.Selvarajb , R.Jeyapaulc, ‘‘Multi
Objective Optimization in Turning of EN25 Steel Using
Taguchi Based Utility Concept Coupled With Principal
Component Analysis”; 12th GLOBAL CONGRESS
ON MANUFACTURING AND MANAGEMENT,
GCMM 2014: 158-165.
[4] K. Wonggasema,*, T. Wagnerb, H. Trautmanna, D.
Biermannb, C. Weihsa, ‘‘Multi-objective optimization
of hard turning of AISI 6150 using PCA-based
desirability index for correlated objectives”; 8th CIRP
Conference on Intelligent Computation in
Manufacturing Engineering:13-18.
[5] S.J. Raykara*, D.M. D'Addonab, A.M. Manea, ‘‘Multi-
objective optimization of high speed turning of Al 7075
using grey relational analysis”; 9th CIRP Conference on
Intelligent Computation in Manufacturing Engineering:
293-298.
[6] R. Venkata Rao*, V.D. Kalyankar, ‘‘Multi-pass turning
process parameter optimization using teaching–
learning-based optimization algorithm”; Department of
Mechanical Engineering, S.V. National Institute of
Technology, Surat-395007, India Received 10 May
2012; revised 30 August 2012; accepted 27 October
2012: 967-974.
[7] P. Jayaramana*, L. Mahesh kumarb, ‘‘Multi-response
Optimization of Machining Parameters of Turning
AA6063 T6 Aluminium Alloy using Grey Relational
Analysis in Taguchi Method”;12th GLOBAL
Assessing Cutting Fluid as a Key Parameter in Evaluating Surface Finish for CNC Turning Operation
(IJSRD/Vol. 4/Issue 07/2016/148)
All rights reserved by www.ijsrd.com 621
CONGRESS ON MANUFACTURING AND
MANAGEMENT, GCMM 2014: 197-204.
[8] Yansong Guoa, Jef Loendersb, Joost Dufloua, Bert
Lauwersa, *, ‘‘Optimization of energy consumption and
surface quality in finish turning”; 5th CIRP Conference
on High Performance Cutting 2012: 512-517.
[9] Deepak Da*Rajendra Bb, “Optimization of Machining
Parameters for Turning of Al6061 using Robust Design
Principle to minimize the surface roughness”;
International Conference on Emerging Trends in
Engineering, Science and Technology (ICETEST-
2015): 372-378.
[10]M. Durairaja,*, S. Gowri, “ Parametric Optimization for
Improved Tool Life and Surface Finish in Micro
Turning using Genetic Algorithm”; International
Conference On DESIGN AND MANUFACTURING,
IConDM 2013: 878-887.

Weitere ähnliche Inhalte

Was ist angesagt?

IRJET- A Review on Optimization of Cutting Parameters in Machining using ...
IRJET-  	  A Review on Optimization of Cutting Parameters in Machining using ...IRJET-  	  A Review on Optimization of Cutting Parameters in Machining using ...
IRJET- A Review on Optimization of Cutting Parameters in Machining using ...IRJET Journal
 
critique and summary on (1) high speed machining (2) additive manufacturing (...
critique and summary on (1) high speed machining (2) additive manufacturing (...critique and summary on (1) high speed machining (2) additive manufacturing (...
critique and summary on (1) high speed machining (2) additive manufacturing (...shanjakhrani
 
Process parameter optimization of SLM process and application of Taguchi appr...
Process parameter optimization of SLM process and application of Taguchi appr...Process parameter optimization of SLM process and application of Taguchi appr...
Process parameter optimization of SLM process and application of Taguchi appr...ijsrd.com
 
Experimental Investigation and Parametric Studies of Surface Roughness Analy...
Experimental Investigation and Parametric Studies of Surface  Roughness Analy...Experimental Investigation and Parametric Studies of Surface  Roughness Analy...
Experimental Investigation and Parametric Studies of Surface Roughness Analy...IJMER
 
A Review on Various Approach for Process Parameter Optimization of Burnishing...
A Review on Various Approach for Process Parameter Optimization of Burnishing...A Review on Various Approach for Process Parameter Optimization of Burnishing...
A Review on Various Approach for Process Parameter Optimization of Burnishing...ijsrd.com
 
Improvement in surface quality with high production rate using taguchi method...
Improvement in surface quality with high production rate using taguchi method...Improvement in surface quality with high production rate using taguchi method...
Improvement in surface quality with high production rate using taguchi method...IAEME Publication
 
Optimization of Cutting Parameters for Minimizing Cycle Time in Machining of ...
Optimization of Cutting Parameters for Minimizing Cycle Time in Machining of ...Optimization of Cutting Parameters for Minimizing Cycle Time in Machining of ...
Optimization of Cutting Parameters for Minimizing Cycle Time in Machining of ...IOSR Journals
 

Was ist angesagt? (12)

IRJET- A Review on Optimization of Cutting Parameters in Machining using ...
IRJET-  	  A Review on Optimization of Cutting Parameters in Machining using ...IRJET-  	  A Review on Optimization of Cutting Parameters in Machining using ...
IRJET- A Review on Optimization of Cutting Parameters in Machining using ...
 
critique and summary on (1) high speed machining (2) additive manufacturing (...
critique and summary on (1) high speed machining (2) additive manufacturing (...critique and summary on (1) high speed machining (2) additive manufacturing (...
critique and summary on (1) high speed machining (2) additive manufacturing (...
 
Process parameter optimization of SLM process and application of Taguchi appr...
Process parameter optimization of SLM process and application of Taguchi appr...Process parameter optimization of SLM process and application of Taguchi appr...
Process parameter optimization of SLM process and application of Taguchi appr...
 
Experimental Investigation and Parametric Studies of Surface Roughness Analy...
Experimental Investigation and Parametric Studies of Surface  Roughness Analy...Experimental Investigation and Parametric Studies of Surface  Roughness Analy...
Experimental Investigation and Parametric Studies of Surface Roughness Analy...
 
C04551318
C04551318C04551318
C04551318
 
247
247247
247
 
1 s2.0-s2212827117303487-main
1 s2.0-s2212827117303487-main1 s2.0-s2212827117303487-main
1 s2.0-s2212827117303487-main
 
30120130406023 2-3
30120130406023 2-330120130406023 2-3
30120130406023 2-3
 
N061102111
N061102111N061102111
N061102111
 
A Review on Various Approach for Process Parameter Optimization of Burnishing...
A Review on Various Approach for Process Parameter Optimization of Burnishing...A Review on Various Approach for Process Parameter Optimization of Burnishing...
A Review on Various Approach for Process Parameter Optimization of Burnishing...
 
Improvement in surface quality with high production rate using taguchi method...
Improvement in surface quality with high production rate using taguchi method...Improvement in surface quality with high production rate using taguchi method...
Improvement in surface quality with high production rate using taguchi method...
 
Optimization of Cutting Parameters for Minimizing Cycle Time in Machining of ...
Optimization of Cutting Parameters for Minimizing Cycle Time in Machining of ...Optimization of Cutting Parameters for Minimizing Cycle Time in Machining of ...
Optimization of Cutting Parameters for Minimizing Cycle Time in Machining of ...
 

Ähnlich wie Cutting fluid as a key 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 Alloyijtsrd
 
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
 
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
 
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
 
A Review on Optimization of Cutting Parameters for Improvement of Surface Rou...
A Review on Optimization of Cutting Parameters for Improvement of Surface Rou...A Review on Optimization of Cutting Parameters for Improvement of Surface Rou...
A Review on Optimization of Cutting Parameters for Improvement of Surface Rou...IRJET Journal
 
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
 
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
 
Optimization of Cutting Parameters for Minimizing Cycle Time in Machining of ...
Optimization of Cutting Parameters for Minimizing Cycle Time in Machining of ...Optimization of Cutting Parameters for Minimizing Cycle Time in Machining of ...
Optimization of Cutting Parameters for Minimizing Cycle Time in Machining of ...IOSR Journals
 
INVESTIGATION AND OPTIMIZATION OF TURNING PROCESS PARAMETER IN WET AND MQL SY...
INVESTIGATION AND OPTIMIZATION OF TURNING PROCESS PARAMETER IN WET AND MQL SY...INVESTIGATION AND OPTIMIZATION OF TURNING PROCESS PARAMETER IN WET AND MQL SY...
INVESTIGATION AND OPTIMIZATION OF TURNING PROCESS PARAMETER IN WET AND MQL SY...IAEME 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
 
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
 
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
 
IRJET- Advance Manufacturing Processes Review Part VI: Plasma ARC Machining (...
IRJET- Advance Manufacturing Processes Review Part VI: Plasma ARC Machining (...IRJET- Advance Manufacturing Processes Review Part VI: Plasma ARC Machining (...
IRJET- Advance Manufacturing Processes Review Part VI: Plasma ARC Machining (...IRJET Journal
 
OPTIMIZATION OF TURNING PROCESS PARAMETER IN DRY TURNING OF SAE52100 STEEL
OPTIMIZATION OF TURNING PROCESS PARAMETER IN DRY TURNING OF SAE52100 STEELOPTIMIZATION OF TURNING PROCESS PARAMETER IN DRY TURNING OF SAE52100 STEEL
OPTIMIZATION OF TURNING PROCESS PARAMETER IN DRY TURNING OF SAE52100 STEELIAEME Publication
 

Ähnlich wie Cutting fluid as a key parameter (20)

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
 
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
 
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...
 
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...
 
A Review on Optimization of Cutting Parameters for Improvement of Surface Rou...
A Review on Optimization of Cutting Parameters for Improvement of Surface Rou...A Review on Optimization of Cutting Parameters for Improvement of Surface Rou...
A Review on Optimization of Cutting Parameters for Improvement of Surface Rou...
 
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 ...
 
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 ...
 
G012143139
G012143139G012143139
G012143139
 
Optimization of Cutting Parameters for Minimizing Cycle Time in Machining of ...
Optimization of Cutting Parameters for Minimizing Cycle Time in Machining of ...Optimization of Cutting Parameters for Minimizing Cycle Time in Machining of ...
Optimization of Cutting Parameters for Minimizing Cycle Time in Machining of ...
 
INVESTIGATION AND OPTIMIZATION OF TURNING PROCESS PARAMETER IN WET AND MQL SY...
INVESTIGATION AND OPTIMIZATION OF TURNING PROCESS PARAMETER IN WET AND MQL SY...INVESTIGATION AND OPTIMIZATION OF TURNING PROCESS PARAMETER IN WET AND MQL SY...
INVESTIGATION AND OPTIMIZATION OF TURNING PROCESS PARAMETER IN WET AND MQL SY...
 
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...
 
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 ...
 
Ijetr042192
Ijetr042192Ijetr042192
Ijetr042192
 
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- Advance Manufacturing Processes Review Part VI: Plasma ARC Machining (...
IRJET- Advance Manufacturing Processes Review Part VI: Plasma ARC Machining (...IRJET- Advance Manufacturing Processes Review Part VI: Plasma ARC Machining (...
IRJET- Advance Manufacturing Processes Review Part VI: Plasma ARC Machining (...
 
OPTIMIZATION OF TURNING PROCESS PARAMETER IN DRY TURNING OF SAE52100 STEEL
OPTIMIZATION OF TURNING PROCESS PARAMETER IN DRY TURNING OF SAE52100 STEELOPTIMIZATION OF TURNING PROCESS PARAMETER IN DRY TURNING OF SAE52100 STEEL
OPTIMIZATION OF TURNING PROCESS PARAMETER IN DRY TURNING OF SAE52100 STEEL
 
Be33331334
Be33331334Be33331334
Be33331334
 
Q01314109117
Q01314109117Q01314109117
Q01314109117
 
Ae4103177185
Ae4103177185Ae4103177185
Ae4103177185
 

Mehr von vishwajeet potdar

International conference2017
International conference2017International conference2017
International conference2017vishwajeet potdar
 
1 i18 ijsrms0206178-v2-i6-prasad diwanji-30nov15
1 i18 ijsrms0206178-v2-i6-prasad diwanji-30nov151 i18 ijsrms0206178-v2-i6-prasad diwanji-30nov15
1 i18 ijsrms0206178-v2-i6-prasad diwanji-30nov15vishwajeet potdar
 
Research paper published ijsrms_process optimization using doe_edm_javed muja...
Research paper published ijsrms_process optimization using doe_edm_javed muja...Research paper published ijsrms_process optimization using doe_edm_javed muja...
Research paper published ijsrms_process optimization using doe_edm_javed muja...vishwajeet potdar
 
Review paper edm pallavi karande
Review paper edm pallavi karandeReview paper edm pallavi karande
Review paper edm pallavi karandevishwajeet potdar
 

Mehr von vishwajeet potdar (8)

International conference2017
International conference2017International conference2017
International conference2017
 
1 i18 ijsrms0206178-v2-i6-prasad diwanji-30nov15
1 i18 ijsrms0206178-v2-i6-prasad diwanji-30nov151 i18 ijsrms0206178-v2-i6-prasad diwanji-30nov15
1 i18 ijsrms0206178-v2-i6-prasad diwanji-30nov15
 
02 03-2016229 v3-e6-040
02 03-2016229 v3-e6-04002 03-2016229 v3-e6-040
02 03-2016229 v3-e6-040
 
20150203 ventilation-system
20150203 ventilation-system20150203 ventilation-system
20150203 ventilation-system
 
Research paper published ijsrms_process optimization using doe_edm_javed muja...
Research paper published ijsrms_process optimization using doe_edm_javed muja...Research paper published ijsrms_process optimization using doe_edm_javed muja...
Research paper published ijsrms_process optimization using doe_edm_javed muja...
 
Psk 2 paper
Psk 2 paperPsk 2 paper
Psk 2 paper
 
Review paper edm pallavi karande
Review paper edm pallavi karandeReview paper edm pallavi karande
Review paper edm pallavi karande
 
Ijsrdv4 i70362 swati_kekade
Ijsrdv4 i70362 swati_kekadeIjsrdv4 i70362 swati_kekade
Ijsrdv4 i70362 swati_kekade
 

Kürzlich hochgeladen

Autonomous emergency braking system (aeb) ppt.ppt
Autonomous emergency braking system (aeb) ppt.pptAutonomous emergency braking system (aeb) ppt.ppt
Autonomous emergency braking system (aeb) ppt.pptbibisarnayak0
 
11. Properties of Liquid Fuels in Energy Engineering.pdf
11. Properties of Liquid Fuels in Energy Engineering.pdf11. Properties of Liquid Fuels in Energy Engineering.pdf
11. Properties of Liquid Fuels in Energy Engineering.pdfHafizMudaserAhmad
 
Transport layer issues and challenges - Guide
Transport layer issues and challenges - GuideTransport layer issues and challenges - Guide
Transport layer issues and challenges - GuideGOPINATHS437943
 
Class 1 | NFPA 72 | Overview Fire Alarm System
Class 1 | NFPA 72 | Overview Fire Alarm SystemClass 1 | NFPA 72 | Overview Fire Alarm System
Class 1 | NFPA 72 | Overview Fire Alarm Systemirfanmechengr
 
DM Pillar Training Manual.ppt will be useful in deploying TPM in project
DM Pillar Training Manual.ppt will be useful in deploying TPM in projectDM Pillar Training Manual.ppt will be useful in deploying TPM in project
DM Pillar Training Manual.ppt will be useful in deploying TPM in projectssuserb6619e
 
Gurgaon ✡️9711147426✨Call In girls Gurgaon Sector 51 escort service
Gurgaon ✡️9711147426✨Call In girls Gurgaon Sector 51 escort serviceGurgaon ✡️9711147426✨Call In girls Gurgaon Sector 51 escort service
Gurgaon ✡️9711147426✨Call In girls Gurgaon Sector 51 escort servicejennyeacort
 
Ch10-Global Supply Chain - Cadena de Suministro.pdf
Ch10-Global Supply Chain - Cadena de Suministro.pdfCh10-Global Supply Chain - Cadena de Suministro.pdf
Ch10-Global Supply Chain - Cadena de Suministro.pdfChristianCDAM
 
Risk Management in Engineering Construction Project
Risk Management in Engineering Construction ProjectRisk Management in Engineering Construction Project
Risk Management in Engineering Construction ProjectErbil Polytechnic University
 
Indian Dairy Industry Present Status and.ppt
Indian Dairy Industry Present Status and.pptIndian Dairy Industry Present Status and.ppt
Indian Dairy Industry Present Status and.pptMadan Karki
 
Crystal Structure analysis and detailed information pptx
Crystal Structure analysis and detailed information pptxCrystal Structure analysis and detailed information pptx
Crystal Structure analysis and detailed information pptxachiever3003
 
Input Output Management in Operating System
Input Output Management in Operating SystemInput Output Management in Operating System
Input Output Management in Operating SystemRashmi Bhat
 
CCS355 Neural Networks & Deep Learning Unit 1 PDF notes with Question bank .pdf
CCS355 Neural Networks & Deep Learning Unit 1 PDF notes with Question bank .pdfCCS355 Neural Networks & Deep Learning Unit 1 PDF notes with Question bank .pdf
CCS355 Neural Networks & Deep Learning Unit 1 PDF notes with Question bank .pdfAsst.prof M.Gokilavani
 
THE SENDAI FRAMEWORK FOR DISASTER RISK REDUCTION
THE SENDAI FRAMEWORK FOR DISASTER RISK REDUCTIONTHE SENDAI FRAMEWORK FOR DISASTER RISK REDUCTION
THE SENDAI FRAMEWORK FOR DISASTER RISK REDUCTIONjhunlian
 
Engineering Drawing section of solid
Engineering Drawing     section of solidEngineering Drawing     section of solid
Engineering Drawing section of solidnamansinghjarodiya
 
BSNL Internship Training presentation.pptx
BSNL Internship Training presentation.pptxBSNL Internship Training presentation.pptx
BSNL Internship Training presentation.pptxNiranjanYadav41
 
multiple access in wireless communication
multiple access in wireless communicationmultiple access in wireless communication
multiple access in wireless communicationpanditadesh123
 
Configuration of IoT devices - Systems managament
Configuration of IoT devices - Systems managamentConfiguration of IoT devices - Systems managament
Configuration of IoT devices - Systems managamentBharaniDharan195623
 
Correctly Loading Incremental Data at Scale
Correctly Loading Incremental Data at ScaleCorrectly Loading Incremental Data at Scale
Correctly Loading Incremental Data at ScaleAlluxio, Inc.
 
"Exploring the Essential Functions and Design Considerations of Spillways in ...
"Exploring the Essential Functions and Design Considerations of Spillways in ..."Exploring the Essential Functions and Design Considerations of Spillways in ...
"Exploring the Essential Functions and Design Considerations of Spillways in ...Erbil Polytechnic University
 

Kürzlich hochgeladen (20)

Autonomous emergency braking system (aeb) ppt.ppt
Autonomous emergency braking system (aeb) ppt.pptAutonomous emergency braking system (aeb) ppt.ppt
Autonomous emergency braking system (aeb) ppt.ppt
 
POWER SYSTEMS-1 Complete notes examples
POWER SYSTEMS-1 Complete notes  examplesPOWER SYSTEMS-1 Complete notes  examples
POWER SYSTEMS-1 Complete notes examples
 
11. Properties of Liquid Fuels in Energy Engineering.pdf
11. Properties of Liquid Fuels in Energy Engineering.pdf11. Properties of Liquid Fuels in Energy Engineering.pdf
11. Properties of Liquid Fuels in Energy Engineering.pdf
 
Transport layer issues and challenges - Guide
Transport layer issues and challenges - GuideTransport layer issues and challenges - Guide
Transport layer issues and challenges - Guide
 
Class 1 | NFPA 72 | Overview Fire Alarm System
Class 1 | NFPA 72 | Overview Fire Alarm SystemClass 1 | NFPA 72 | Overview Fire Alarm System
Class 1 | NFPA 72 | Overview Fire Alarm System
 
DM Pillar Training Manual.ppt will be useful in deploying TPM in project
DM Pillar Training Manual.ppt will be useful in deploying TPM in projectDM Pillar Training Manual.ppt will be useful in deploying TPM in project
DM Pillar Training Manual.ppt will be useful in deploying TPM in project
 
Gurgaon ✡️9711147426✨Call In girls Gurgaon Sector 51 escort service
Gurgaon ✡️9711147426✨Call In girls Gurgaon Sector 51 escort serviceGurgaon ✡️9711147426✨Call In girls Gurgaon Sector 51 escort service
Gurgaon ✡️9711147426✨Call In girls Gurgaon Sector 51 escort service
 
Ch10-Global Supply Chain - Cadena de Suministro.pdf
Ch10-Global Supply Chain - Cadena de Suministro.pdfCh10-Global Supply Chain - Cadena de Suministro.pdf
Ch10-Global Supply Chain - Cadena de Suministro.pdf
 
Risk Management in Engineering Construction Project
Risk Management in Engineering Construction ProjectRisk Management in Engineering Construction Project
Risk Management in Engineering Construction Project
 
Indian Dairy Industry Present Status and.ppt
Indian Dairy Industry Present Status and.pptIndian Dairy Industry Present Status and.ppt
Indian Dairy Industry Present Status and.ppt
 
Crystal Structure analysis and detailed information pptx
Crystal Structure analysis and detailed information pptxCrystal Structure analysis and detailed information pptx
Crystal Structure analysis and detailed information pptx
 
Input Output Management in Operating System
Input Output Management in Operating SystemInput Output Management in Operating System
Input Output Management in Operating System
 
CCS355 Neural Networks & Deep Learning Unit 1 PDF notes with Question bank .pdf
CCS355 Neural Networks & Deep Learning Unit 1 PDF notes with Question bank .pdfCCS355 Neural Networks & Deep Learning Unit 1 PDF notes with Question bank .pdf
CCS355 Neural Networks & Deep Learning Unit 1 PDF notes with Question bank .pdf
 
THE SENDAI FRAMEWORK FOR DISASTER RISK REDUCTION
THE SENDAI FRAMEWORK FOR DISASTER RISK REDUCTIONTHE SENDAI FRAMEWORK FOR DISASTER RISK REDUCTION
THE SENDAI FRAMEWORK FOR DISASTER RISK REDUCTION
 
Engineering Drawing section of solid
Engineering Drawing     section of solidEngineering Drawing     section of solid
Engineering Drawing section of solid
 
BSNL Internship Training presentation.pptx
BSNL Internship Training presentation.pptxBSNL Internship Training presentation.pptx
BSNL Internship Training presentation.pptx
 
multiple access in wireless communication
multiple access in wireless communicationmultiple access in wireless communication
multiple access in wireless communication
 
Configuration of IoT devices - Systems managament
Configuration of IoT devices - Systems managamentConfiguration of IoT devices - Systems managament
Configuration of IoT devices - Systems managament
 
Correctly Loading Incremental Data at Scale
Correctly Loading Incremental Data at ScaleCorrectly Loading Incremental Data at Scale
Correctly Loading Incremental Data at Scale
 
"Exploring the Essential Functions and Design Considerations of Spillways in ...
"Exploring the Essential Functions and Design Considerations of Spillways in ..."Exploring the Essential Functions and Design Considerations of Spillways in ...
"Exploring the Essential Functions and Design Considerations of Spillways in ...
 

Cutting fluid as a key parameter

  • 1. IJSRD - International Journal for Scientific Research & Development| Vol. 4, Issue 07, 2016 | ISSN (online): 2321-0613 All rights reserved by www.ijsrd.com 618 Assessing Cutting Fluid as a Key Parameter in Evaluating Surface Finish for CNC Turning Operation Swati Arvind Kekade1 V.V. Potdar2 Prof. Swapnil S.Kulkarni3 1,2 Student 3 Associate Professor 1,2 Department of Manufacturing Process Engineering 1,2 A.G.P.I.T. Solapur University 3 Advent Tool Tech India Pvt. Ltd., Pune Abstract— The tool work piece combination of the Raw material HCHCr with cemented carbide and CBN tools shall be explored in this work. The parameters – Speed, Feed and Depth of cut to be maintained for HCHCr shall be determined in terms of optional values or levels for each of the tool type. Historic data shall be referred from the records of the company to understand the effect of introducing variation in the input to realize a given output as a ‘Response’ for surface finish. Statistical data analysis shall be performed using ANOVA, DOE and Regression to identify the given machining parameters for the operation. Experimental run shall be conducted for validating the values realized through this Analytical treatment of the data. Surface roughness tester shall be employed for determining the response. Recommendation to be offered at the concluding phase of this work in terms of the ‘optional set of values’ for the process parameters. Key words: Turning, HCHCr, Cemented Carbide, CBN, Surface roughness, process optimization I. INTRODUCTION The challenge of modern machining industries is mainly focused on the achievement of high quality, in terms of work piece dimensional accuracy, surface finish, and high production rate, less wear on the cutting tools, economy of machining in terms of cost saving and increase the performance of the product with reduced environmental impact. Surface roughness plays an important role in many areas and is a factor of great importance in the evaluation of machining accuracy. The Taguchi method is statistical tool, adopted experimentally to investigate influence of surface roughness by cutting parameters such as cutting speed, feed and depth of cut. The Taguchi process helps to select or to determine the optimum cutting conditions for turning process. Many researchers developed many mathematical models to optimize the cutting parameters to get lowest surface roughness by turning process. The variation in the material hardness, alloying elements present in the work piece material and other factors affecting surface finish and tool wear. II. LITERATURE REVIEW Wojciech Zębala*, Robert Kowalczyk, Andrzej Matras [1] The investigation shows the result analysis of the recorded total cutting force components (Ff, Fp, Fc) during turning of WC-Co (25 % Co) with tools made of polycrystalline diamond PCD. Turning tests of sintered carbides (WC Co shaft with content of 25% Co) accomplished by the inserts with three different nose radii, each cutting test had been carried out on the distance 54 mm at the constant depth of cut 0.2 mm. it was noticed, the largest growth is for the passive force component Fp. In addition, the mathematical models for cutting force value prediction as the cutting path increases are presented. There is presentation of the algorithm of optimization and control of the super hard materials turning process. S.Sivasankarana*, P.T.Harisagarb, E.Saminathanb, S. Siddharthb, P.Sasikumarb [2] This investigation shows the effect of process parameters on surface roughness of glass fiber reinforced polymer (GFRP) composite pipes. On the basis of high speed turning centre (CNC) with poly- crystalline diamond (PCD) tool experiments had been conducted. The cutting speed, feed, depth of cut, and work piece type (E-Glass mat and E-Glass woven specimen) were considered as process parameter. The pipes used in experiment were fabricated using the filament winding process. It consists of 70 percent epoxy polyester resin and 30 percent glass fiber. The E-Glass woven and E-Glass mat fiber reinforced composite material had been compared. The mechanical properties had also conducted as per ASTM standards. Based on the results, it has been observed that good machinability had obtained at lower cutting speed, feed rate, depth of cut for mat fiber reinforced GFRP pipe. B.Singarvela*,T.Selvarajb , R.Jeyapaulc[3] In this experimental analysis Taguchi based utility concept is used coupled with Principal Component Analysis (PCA) on turning of EN25 steel with CVD and PVD coated carbide tools to estimate the optimum machining parameters. For obtaining the simultaneous minimization of surface roughness, cutting force and maximization of material removal rate this method has been employed. The multi SN ratio is calculated by the product of weight factor and SN ratio to the performance characteristics in the utility concept. The weight factors involved for all objectives found by adopting the Principal component analysis. Finally the relative significance of machining parameter in terms of their percentage contribution had found by applying ANOVA concept on multi SN ration. K. Wonggasema,*, T. Wagnerb, H. Trautmanna, D. Biermannb, C. Weihsa[4] In this investigation there is a demonstration of an optimization of the PCA-based DI based on empirical models of hard turning of AISI 6150 steel in which uncertainties are propagated by model errors. The obtained results are the degree of importance of each performance measure has been adjusted by the integration of the covariance information into the overall performance index . S.J. Raykara*, D.M. D'Addonab, A.M. Manea[5] This paper the Grey Relational Analysis (GRA) is used for investigation of High Speed Turning of Al 7075, a high strength aluminium alloy used for aerospace applications. GRA is also used for Multi-objective optimization of surface roughness, power consumption, material removal rate and cutting time which are some important parameters to decide the capability and suitability of high speed turning. By machining Al 7075 at high cutting speeds the
  • 2. Assessing Cutting Fluid as a Key Parameter in Evaluating Surface Finish for CNC Turning Operation (IJSRD/Vol. 4/Issue 07/2016/148) All rights reserved by www.ijsrd.com 619 investigation of performance of coated and uncoated carbide cutting tool has been done. On the basis of GRA results Suitable cutting parameters with appropriate cutting tool for high speed turning of Al 7075 are suggested at the end. R. Venkata Rao*, V.D. Kalyankar[6] This paper focused on the primary objective in multi-pass turning operations is to produce products with low cost and high quality, with a lower number of cuts. Parameter optimization is the very important process to achieve this goal. It usually involves the optimal selection of cutting speed, feed rate, depth of cut and number of passes. Recently developed advanced optimization algorithm, named, the teaching–learning-based optimization algorithm had been used in this process, for parameter optimization of a multi-pass turning operation. Two different examples attempted previously by various researchers using different optimization techniques, such as simulated annealing, the genetic algorithm, the ant colony algorithm, and particle swarm optimization, etc. had been considered here. The first example is a multi-objective problem and the second example is a single objective multi-constrained problem with 20 constraints. The effectiveness of teaching–learning- based optimization algorithm had been proved over other algorithms. P. Jayaramana*, L. Mahesh kumarb[7] A novel approach was presented for the optimization of machining parameters on turning of AA 6063 T6 aluminium alloy with multiple responses based on orthogonal array with grey relational analysis. Experiments were conducted on AA 6063 T6 aluminium alloy. Turning tests were carried out using uncoated carbide insert under dry cutting condition. Optimization of turning parameters such as cutting speed, feed rate and depth of cut has been done considering the multiple responses such as surface roughness (Ra), roundness (Ø) and material removal rate (MRR). The grey analysis was used to determine grey relational grade (GRG). Based on the values of grey relational grade optimum levels of parameters had identified and then by ANOVA the significant contribution of parameters was determined. Confirmation test was performed to validate the test result. Experimental outcomes had proved that the responses in turning process can be enhanced efficiently through this fresh approach. Yansong Guoa, Jef Loendersb, Joost Dufloua, Bert Lauwersa, *[8] In this paper, an approach which incorporates both energy consumption and surface roughness was presented for optimizing the cutting parameters in finish turning. Based on a new energy model and a surface roughness model, derived for a given machine tool, cutting parameters were optimized to accomplish a precise surface finish with minimum energy consumption. Deepak Da*Rajendra Bb[9] This paper focused on the optimization of the process parameter such as cutting speed, depth of cut and feed rate on surface roughness produced on the machined component. Analysis was carried out using Taguchi robust design principles. From the analysis, feed rate was found to be the most influential process parameters which influence the surface roughness followed by cutting speed and depth of cut. Increase in feed rate and depth of cut was found to increase the surface roughness. M. Durairaja,*, S. Gowri[10] This paper deals with CNC Micro turning of Inconel 600 alloy with titanium carbide coated tool. Machining has been done in DT-110 integrated multiprocessor micro machine tool. Micro turning was carried out with full factorial experiments with various combinations of cutting parameters such as speed (25,31, and 37 m/min), feed (5,10, and 15 μm/rev) and depth of cut (30,50 and 70 μm). For every set of experiments, the output parameters such as the tool wear and the surface roughness were measured. Non-linear regression model was used to represent relationship between input and output variables and a multi-objective optimization method based genetic algorithm was used to optimize the cutting parameters in turning process such as cutting speed, feed and depth of cut. Two conflicting objectives such as tool wear and surface roughness were simultaneously optimized. III. PROBLEM STATEMENT The CNC turning involves holding the work piece firmly in the self centering chuck and rotating the work piece against a sharp tool tip to effect material removal through chip formation. This machining process is widely used in the industry for achieving high material removal rate (MRR) and a smooth finish over the part produced (Ra value). The popular material used in the industry dealing with Die, Molds and Fixtures uses HCHCr for parts that require high hardness combined with good toughness to absorb shocks. The concerned company supporting this work deals in the tooling domain and uses the said material – HCHCr for producing pins, shafts and other components with radial symmetry. The process at the production shop floor arms at high rate of production (higher MMR) with dimensional accuracy and a good surface finish. For initiation the historic data collected in the past for a given set of process parameters yielding a certain output in terms of Ra value is available with the company. This data could be referred for analysis while additional experiments could be performed towards the concluding phase of validation. The process parameters - Namely, speed, feed and the Depth of cut and the type of coolant for the operation are identified as the prime parameters influencing the ‘MRR’ and the `Ra value’. Any variation over the values for these parameters shall directly results in alteration over the output realized i.e. the ‘Response’ would change based on the degree of change in the input. Research work needs to be undertaken to find the optimum values for each parameter that would render a desired Ra value for a satisfactory MRR. The tool to be used for the experiment shall be CBN. The choice of alternative coolant fluid shall be evaluated by administering three values of suitable coolants. Optional values for each type of coolant shall be determined through this work. IV. SCOPE The scope of the work shall be limited to collection of input data for the influencing process parameters – namely, Speed, Feed and Depth of cut, performing statistical analysis for the data, validating the results through experimentation and recommending the best set of values that optimizes the conflicting requirements of a higher MRR and a good
  • 3. Assessing Cutting Fluid as a Key Parameter in Evaluating Surface Finish for CNC Turning Operation (IJSRD/Vol. 4/Issue 07/2016/148) All rights reserved by www.ijsrd.com 620 surface finish. The work shall be purchased as per the concerned company supporting this research work. V. OBJECTIVES  Conduct literature survey for documenting the findings of the various authors working on this problem area.  Determine the process parameters through this literature review and the inputs offered by the supporting company.  Design the experiment and collect the experimental data for the responses chosen.  Conduct analysis in the statistical domain to determine the optimum values.  Perform physical experiment for validating the results determined.  Offer recommendation with the optional value set for the process parameters. VI. METHODOLOGY Analytical approach – This methodology is developed by collecting the requisite input data from past records or through experiments. The data is later subjected to treatment using statistical tools like Normality plots, one-way ANOVA, Taguchi methods or any suitable technique for DOE and optimization. Regression equation could be secured for estimating the anticipated results while making a change over the input value. The trade-off for the response can be accessed through the Analytical methodology for finding solution. VII. FLOW CHART FOR METHODOLOGY Fig. 1: Flowchart for methodology A. Physical Experimentation The methodology is widely accepted and trusted for the results it offers. Typically, it is deployed as an alternative methodology for validation of the results obtained using the other methodologies. In our case, besides using it for validation, it shall also offer values for responses for a given of levels for the parameters. These responses are crucial for being able to the analysis of the data. B. Validation The physical experimentation performed for the optimal values shall offer validation for the results obtained using statistical methods. The anticipated variation in the results determined by individual methodologies could be the tuneof 10 to 15% by virtue of inherent variations in the experimental setup or the ambient conditions during machining. The results could be shared with the supporting company upon conducting the experimental run towards validation. VIII. CONCLUSION (FOR STAGE 1) The work undertakes to study the effect coolant over the processing parameters. This type of study is unique as has been investigated over the literature review. The coolants are proposed upon studying the suitability of the same for the given environment of machining. The work is expected to bring forth optimal values based on the selection of the coolant. REFERENCES [1] Wojciech Zębala*, Robert Kowalczyk, Andrzej Matras, ‘‘Analysis and Optimization of Sintered Carbides Turning with PCD Tools”; 25th DAAAM International Symposium on Intelligent Manufacturing and Automation, DAAAM 2014: 283-290. [2] [2] S.Sivasankarana*, P.T.Harisagarb, E.Saminathanb, S. Siddharthb, P.Sasikumarb, ‘‘Effect of process parameters in surface roughness during turning of GFRP pipes using PCD insert tool”; 12th GLOBAL CONGRESS ON MANUFACTURING AND MANAGEMENT, GCMM 2014: 64-71. [3] B.Singarvela*,T.Selvarajb , R.Jeyapaulc, ‘‘Multi Objective Optimization in Turning of EN25 Steel Using Taguchi Based Utility Concept Coupled With Principal Component Analysis”; 12th GLOBAL CONGRESS ON MANUFACTURING AND MANAGEMENT, GCMM 2014: 158-165. [4] K. Wonggasema,*, T. Wagnerb, H. Trautmanna, D. Biermannb, C. Weihsa, ‘‘Multi-objective optimization of hard turning of AISI 6150 using PCA-based desirability index for correlated objectives”; 8th CIRP Conference on Intelligent Computation in Manufacturing Engineering:13-18. [5] S.J. Raykara*, D.M. D'Addonab, A.M. Manea, ‘‘Multi- objective optimization of high speed turning of Al 7075 using grey relational analysis”; 9th CIRP Conference on Intelligent Computation in Manufacturing Engineering: 293-298. [6] R. Venkata Rao*, V.D. Kalyankar, ‘‘Multi-pass turning process parameter optimization using teaching– learning-based optimization algorithm”; Department of Mechanical Engineering, S.V. National Institute of Technology, Surat-395007, India Received 10 May 2012; revised 30 August 2012; accepted 27 October 2012: 967-974. [7] P. Jayaramana*, L. Mahesh kumarb, ‘‘Multi-response Optimization of Machining Parameters of Turning AA6063 T6 Aluminium Alloy using Grey Relational Analysis in Taguchi Method”;12th GLOBAL
  • 4. Assessing Cutting Fluid as a Key Parameter in Evaluating Surface Finish for CNC Turning Operation (IJSRD/Vol. 4/Issue 07/2016/148) All rights reserved by www.ijsrd.com 621 CONGRESS ON MANUFACTURING AND MANAGEMENT, GCMM 2014: 197-204. [8] Yansong Guoa, Jef Loendersb, Joost Dufloua, Bert Lauwersa, *, ‘‘Optimization of energy consumption and surface quality in finish turning”; 5th CIRP Conference on High Performance Cutting 2012: 512-517. [9] Deepak Da*Rajendra Bb, “Optimization of Machining Parameters for Turning of Al6061 using Robust Design Principle to minimize the surface roughness”; International Conference on Emerging Trends in Engineering, Science and Technology (ICETEST- 2015): 372-378. [10]M. Durairaja,*, S. Gowri, “ Parametric Optimization for Improved Tool Life and Surface Finish in Micro Turning using Genetic Algorithm”; International Conference On DESIGN AND MANUFACTURING, IConDM 2013: 878-887.