SlideShare a Scribd company logo
1 of 4
Download to read offline
IJSRD - International Journal for Scientific Research & Development| Vol. 3, Issue 10, 2015 | ISSN (online): 2321-0613
All rights reserved by www.ijsrd.com 1037
Evaluation the Effect of Machining Parameters on Mrr of Mild Steel
Mr. Sandeep Boora1 Mr. Sandeep Kumar2
1
Student 2
H.O.D
1,2
Department of Mechanical Engineering
1,2
MIET, India
Abstract— Turning metal removal is the removal of metal
outside diameter of a rotating cylindrical workpiece. As it is
used to reduce the diameter. of the work piece generally to a
specified dimension and to produce a smooth finish on the
metal. Often, the work piece is turned so that the sections
have different diameters. As is the machining operation that
produces cylindrical. In its basic form, it can be defined as
an outer surface machining: Based on the findings of the
Pilot study, actual experimentation work will be designed
and input machining parameters and their values finalized.
The results are expected to show that the response variables
(output parameters) strongly influenced by the control
factors (input parameters). So, the results which are obtained
after experimentation analyzed and modeled for their
application in manufacturing industry It is concluded that
for MRR be maximum factor Cutting speed has to be at high
level 3, Feed has to be at high level & D.O.C has to be at
high level.
Key words: Taguchi Design, Orthogonal Array, Turning,
Cutting Speed, Feed
I. INTRODUCTION
Kohli and Dixit (2005) [1] proposed a method based on the
neural network with the acceleration of the radial vibration
of the tool holder as feedback. The backpropagation
algorithm is used to train the network model for predicting
the surface roughness in turning process. The methodology
was validated for turning dry and wet steel using high speed
steel and carbide tool and noted that the proposed
methodology was able to make an accurate prediction of
surface roughness using small formation of size and testing
data sets. Pal and Chakraborty (2005) [2] studied to develop
a model of neural network back-propagation for prediction
of surface roughness in turning operation and is used mild
steel work pieces with high speed steel as the cutting tool to
carry out a number of experiments. The authors used speed,
feed, depth of cut and cutting forces as inputs to the neural
network model for prediction of surface roughness. The
work resulted in the intended surface roughness was very
close to the experimental value.Sing and Kumar (2006) [3]
studied the optimization of the feed force through optimum
adjustment of process parameters namely speed, feed and
depth of cut in steel processing with carbide inserts EN24
TiC coated tungsten. The authors used the approach Taguchi
design parameters and concluded that the effect of feed and
cutting depth variation in feed force affected more as
compared to the speed. Ahmed (2006) [4] developed the
methodology for obtaining the optimum process parameters
for prediction of surface roughness in turning Al. To
develop the empirical model nonlinear regression analysis
with logarithmic transformation of data it was applied. The
model developed showed little mistakes and satisfactory
results. The study concluded that low feed rate was good to
produce low surface roughness and high speed couldproduce
high surface quality within the experimental domain. Abburi
and Dixit (2006) [5] they developed based on knowledge for
predicting surface roughness in turning process system.
Fuzzy set theory and neural networks were used for this
purpose. The authors developed rule to predict the surface
roughness for the process variables as well as for the
prediction of process variables for a given surface
roughness. Zhong et al. (2006) [6] predicted the surface
roughness of machined surfaces using nets with seven
innings namely degree insertion tool, workpiece materials,
cutting edge radius, angle, depth of cut, spindle speed and
feed rate . Kumanan et al. (2006) [7] proposed a
methodology for predicting the machining forces using
multilayer perceptron trained by genetic algorithm (GA).
The data obtained from experimental results of a turning
process is explored to train artificial neural networks
proposed (RNAs) with three inputs for output machining
forces. ANN optimal weights were obtained using GA
search. This hybrid function replacing made of GA and
ANN found computationally efficient and accurate to
predict the forces of machining for the machining conditions
of entry. Mahmoud and Abdelkarim (2006) [8] studied in
turning operation using high speed steel (HSS) cutting tool
450 at an angle of approach. This tool proved he could carry
out the cutting operation at higher speeds and longer service
life of the traditional tool with a rake angle of 90 degrees.
The study ultimately determines the optimum cutting speed
for high production speed and minimum cost and tool,
production time and operating costs. Doniavi et al. (2007)
[9] using the response surface methodology (RSM) in order
to develop the empirical model for the prediction of surface
roughness, in deciding the optimal cutting condition in the
transformation. The authors showed that the feed rate
significantly influenced surface roughness. With increased
surface roughness speed power was found to be increased.
With the increase in the cutting speed decreased surface
roughness. Analysis of variance showed that the influence of
the feed rate and were in surface roughness of the cutting
depth was applied. Kassab and Khoshnaw (2007) [10]
examined the correlation between surface roughness and
vibration cutting tool for turning operations. The process
parameters were cutting speed, depth of cut, feed rate and
outstanding tool. The experiments were carried out on the
lathe turning using dry (without cutting fluid) operation of
medium carbon steel with different levels of process
parameters mentioned above. Turning was dry useful for a
good correlation between the surface roughness and the
cutting tool vibration due clean environment. The authors
developed a good correlation between the vibration cutting
tool surface roughness and to control the surface finish of
the workpieces during mass production. The study
concluded that it was observed that the surface roughness of
the workpiece to be affected more by the acceleration
cutting tool; acceleration increased overhang of the cutting
tool. The surface roughness was found to increase with
increasing feed rate. Al-Ahmari (2007) [11] developed
Evaluation the Effect of Machining Parameters on Mrr of Mild Steel
(IJSRD/Vol. 3/Issue 10/2015/238)
All rights reserved by www.ijsrd.com 1038
empirical models for the tool life, surface roughness and
cutting force for turning operation. The process parameters
were used in the study speed, feed, depth of cut and the nose
radius to develop the model machining. The methods used
to develop 48 models mentioned above were Response
Surface Methodology (RSM) and neural networks (NN).
Thamizhmanii et al. (2007) [12] applied the Taguchi method
to find the optimal value of the roughness of the surface
under optimal cutting conditions in turning SCM 440 alloy
steel. The experiment was designed using the Taguchi
method and experiments were carried out and the results
thereof were analyzed with the help of ANOVA (analysis of
variance). The causes of poor surface finish as vibrations
were detected machine tool, tool chatter whose effects were
ignored for analysis. The authors concluded that the results
obtained by this method would be useful for other research
study similar type of tool vibrations, forces, etc. The study
concluded that cutting deppth was the only major factor that
contributed to the roughness of the cutting surface.
Natarajan et al. (2007) [13] introduced the technique of
monitoring tool wear online processing operation. Spindle
speed, feed, cutting depth, cutting force, power spindle
motor and temperature were selected as the input parameters
for the monitoring technique. To determine the degree of
wear of the tool; Two methods of Hidden Markov Model
(HMM) method such as bar-graph and multiple modeling
methods were used. An algorithm of decision fusion center
(DFCA) was used to increase the reliability of this output
that combines the outputs of the individual methods to make
a comprehensive decision on the wear of the tool. Finally,
all the proposed methods were combined in a DFCA to
determine the wear of the tool during turning operations.
Ozel et al. (2007) [14] conducted the finish turning of steels
AISI D2 (60 HRC) using ceramic (multi-radio) design
inserts cleaner surface finish and tool flank wear research.
For prediction of surface roughness and tool flank wear
multiple linear regression models and neural network
models were developed. Predictions based neural network of
surface roughness and tool flank wear were carried out in
comparison with a non-formation experimental data and the
results thereof showed that neural network models proposed
were efficient for predicting patterns tool wear and surface
roughness for a range of cutting conditions. The study
concluded that the best tool life was obtained in lower feed
rate and the lowest shear rate combination. Wang Lan
(2008) [15] using orthogonal array of Taguchi method gray
relational analysis with considering four parameters viz.
speed, depth, rate of cutting tool nose to drain, etc., for the
optimization of three responses: surface roughness, the tool
wear and material removal rate on the accuracy of light a
ECOCA-3807 CNC lathe. It explored the MINITAB
software to analyze the average signal-to-noise (S / N) effect
to achieve the multi-purpose features. This study not only
suggests an optimization approaches using orthogonal
matrix and gray relational analysis, but also contributed a
satisfactory technique to improve performance of multiple
precision machining CNC turning, with profound insight.
Srikanth and Kamala (2008) [16] evaluated the optimal
values of cutting parameters using a genetic algorithm coded
Real (RCGA) and various issues RCGA and its advantages
over the current approach of Binary Coded Genetic
Algorithm explained ( BCGA). They concluded that RCGA
was reliable and accurate to solve the optimization
parameter and build cutting optimization problem with
multiple decision variables. These decision variables were
cutting speed, feed, depth of cut and nose radius. The
authors noted that the fastest solution RCGA can be
obtained with relatively high success rate, with selected
machining conditions providing for overall improvement
mode product quality by reducing the cost of production,
reduction in time production, flexibility in the selection of
machining parameters. Sahoo et al. (2008) [17] studied for
optimization of machining parameters combinations
emphasis on fractal characteristics of surface profile
generated in CNC turning operation. The authors used the
L27 orthogonal array design with machining Taguchi
parameters: speed, feed and depth of cut in three different
work piece materials viz. aluminum, mild steel and brass. It
was concluded that the feed rate was more significant
influence surface finish on the three materials. It was
observed that in the case of mild steel and aluminum feed
showed some influences, whereas in the case of brass
cutting depth was noticed impose some influence on the
surface finish. The interaction factor was responsible for
controlling the fractal dimensions of the surface profile
produced in CNC turning.
II. DESIGN OF EXPERIMENTS
Engineering methods, the quality of Dr. Taguchi hired to
design experiments (DOE) is a statistical tool, the most
important of TQM for the design of high quality at lower
prices Taguchi Method provides an effective method and a
system for optimizing the design for. Performance, quality
and cost. The basic design stage trials traditionally are too
complex and not easy to use. Many of the trials have to be
carried out on a number of process parameters added. To
solve this problem Taguchi method uses a special design of
the array to study the parameter space that only a small
number of trials. Taguchi is the developer of how Taguchi
Method was widely used in engineering analysis and
includes a plan of experiments with the aim of getting
information in a controlled and to obtain information about
the circumstances. of assignment process The greatest
advantage of the method is to save the efforts in conducting
trials. So you reduce the time trials as well as the costs by a
factor quickly.
A. Process Variables and their Limits
First pilot experiments were done on the work piece using
random values and then from those pilot experiments the
suitable values of these parameters were selected. On the
basis of observations from the pilot experiments these levels
were found suitable for the experimentation.
Factors Units
Level-
1
Level-
2
Level-
3
Spindle Speed
(N)
RPM 220 330 440
Feed (F) Mm/Rev. 0.2 0.3 0.4
Depth Of Cut
(DOC)
Mm 0.5 1 1.5
Table 1: Process variables and their limits.
B. L9 Orthogonal Array
Run C.S Feed D.O.C
Evaluation the Effect of Machining Parameters on Mrr of Mild Steel
(IJSRD/Vol. 3/Issue 10/2015/238)
All rights reserved by www.ijsrd.com 1039
1 440 0.4 0.5
2 440 0.3 1
3 440 0.2 1.5
4 330 0.4 1
5 330 0.3 1.5
6 330 0.2 0.5
7 220 0.4 1.5
8 220 0.3 0.5
9 220 0.2 1
Table 2: Taguchi’s L9 Orthogonal Array.
C. Material Removal Rate Measurement
Material removal rate (MRR) has been calculated from the
difference of weight of work piece before and after
experiment.
D. Measurement of MRR
C.S Feed D.O.C Time Initial Wt. Final Wt. Reduction in Wt. (Wi- Wf)/ρst in mm3
/ sec
440 0.4 0.5 11.16 282.5 276.86 5.64 65.63
440 0.3 1 16.13 288.51 278.26 10.25 82.53
440 0.2 1.5 23.8 288.8 264.44 24.36 132.93
330 0.4 1 16.59 285.88 277.6 8.28 64.82
330 0.3 1.5 23.87 279.96 265.2 14.76 80.31
330 0.2 0.5 32.4 285.52 272.66 12.86 51.55
220 0.4 1.5 24.67 284.96 268.65 16.31 85.86
220 0.3 0.5 31.19 284.74 279.49 5.25 21.86
220 0.2 1 44.65 281.59 280.3 1.29 3.75
Table 3: Measurement of MRR
E. Response Table for Means
Response Table for Means
Level C.S Feed D.O.C
1 37.16 62.74 46.35
2 65.56 61.56 50.37
3 93.70 72.10 99.70
Delta 56.54 10.54 53.35
Rank 1 3 2
Table 4: Response Table for Means
Fig. 1: Shows the effect of different parameters on MRR.
III. ANALYSIS
A. Analysis of Variance for Means
Source DF SeqSS Adj SS
Adj
MS
F P
C.S 2 4794.8 4794.8
2397
.4
4.39
0.1
85
Feed 2 200.1 200.1
100.
1
0.18
0.8
45
D.O.C 2 5296.0 5296.0
2648
.0
4.85
0.1
71
Residual
Error
2 1091.4 1091.4
545.
7
Total 8 11382.3
Table 5: Analysis of Variance for Means
IV. CONCLUSION
It is concluded that for MRR be maximum factor Cutting
speed has to be at high level 3, Feed has to be at high level 3
& D.O.C has to be at high level 3. As shown in table below.
Physical Optimal Combination
Requirements C.S F D.O.C
Maximum MRR
440 0.4 1.5
Level-3 Level-3 Level-3
Table 5.1: Optimal combination for MRR
REFERENCES
[1] Kohli A. & Dixit U. S., (2005),”A neural-network-
based methodology for the prediction of surface
roughness in a turning process”, International Journal of
Advanced Manufacturing Technology, Volume 25,
pp..118–129.
[2] Pal S. K. & Chakraborty D., (2005), “Surface roughness
prediction in turning using artificial neural network”,
Neural Computing & Application, Volume14, pp. 319–
324.
[3] Singh H. & Kumar P., (2006), “Optimizing Feed Force
for Turned Parts through the Taguchi Technique”,
Sadhana, Volume 31, Number 6, pp.. 671–681. 58
[4] Ahmed S. G., (2006), “Development of a Prediction
Model for Surface Roughness in Finish Turning of
Aluminium”, Sudan Engineering Society Journal,
Volume 52, Number 45, pp.. 1-5.
[5] Abburi N. R. & Dixit U. S., (2006), “A knowledge-
based system for the prediction of surface roughness in
turning process” Robotics & Computer-Integrated
Manufacturing, Volume 22, pp.. 363–372.
[6] Zhong Z. W., Khoo L. P. & Han S. T., (2006),
“Prediction of surface roughness of turned surfaces
using neural networks”, International Journal of
Advance Manufacturing Technology, Volume 28, pp..
688–693.
[7] Kumanan S., Saheb S. K. N. & Jesuthanam C. P.,
(2006), “Prediction of Machining Forces using Neural
Evaluation the Effect of Machining Parameters on Mrr of Mild Steel
(IJSRD/Vol. 3/Issue 10/2015/238)
All rights reserved by www.ijsrd.com 1040
Networks Trained by a Genetic Algorithm”, Institution
of Engineers (India) Journal, Volume 87, pp.. 11-15.
[8] Mahmoud E. A. E. & Abdelkarim H. A., (2006),
“Optimum Cutting Parameters in Turning Operations
using HSS Cutting Tool with 450 Approach Angle”,
Sudan Engineering Scoeiety Journal, Volume 53,
Number 48, pp.. 25-30.
[9] Doniavi A., Esk&erzade M. & Tahmsebian M., (2007),
“Empirical Modeling of Surface Roughness in Turning
Process of 1060 steel using Factorial Design
Methodology”, Journal of Applied Sciences, Volume 7,
Number17, pp.. 2509-2513.
[10]Kassab S. Y. & Khoshnaw Y. K., (2007), “The Effect
of Cutting Tool Vibration on Surface Roughness of
Work piece in Dry Turning Operation”, Engineering &
Technology, Volume 25, Number 7, pp.. 879-889.
[11]Al-Ahmari A. M. A., (2007),”Predictive machinability
models for a selected hard material in turning
operations”, Journal of Materials Processing
Technology, Volume 59 190, pp.. 305–311.
[12]Thamizhmanii S., Saparudin S. & Hasan S., (2007),
“Analysis of Surface Roughness by Using Taguchi
Method”, Achievements in Materials & Manufacturing
Engineering, Volume 20, Issue 1-2, pp.. 503-505.
[13]Natarajan U., Arun P., Periasamy V. M., (2007), “On-
line Tool Wear Monitoring in Turning by Hidden
Markov Model (HMM)” Institution of Engineers (India)
Journal (PR), Volume 87, pp.. 31-35.
[14]Özel T. & Karpat Y., (2005), “Predictive modeling of
surface roughness & tool wear in hard turning using
regression & neural networks”, International Journal of
Machine Tools & Manufacture, Volume 45, pp.. 467–
479.
[15]Wang M. Y. & Lan T. S., (2008), “Parametric
Optimization on Multi-Objective Precision Turning
Using Grey Relational Analysis”. Information
Technology Journal, Volume 7, pp..1072-1076.
[16]Srikanth T. & Kamala V., (2008), “A Real Coded
Genetic Algorithm for Optimization of Cutting
Parameters in Turning IJCSNS”, International Journal
of Computer Science & Network Security, Volume 8
Number 6, pp.. 189-193.
[17]Sahoo P., Barman T. K. & Routara B. C., (2008),
“Taguchi based practical dimension modeling &
optimization in CNC turning”, Advance in Production
pp..60.

More Related Content

What's hot

Effectiveness of multilayer coated tool in turning of aisi 430 f steel
Effectiveness of multilayer coated tool in turning of aisi 430 f steelEffectiveness of multilayer coated tool in turning of aisi 430 f steel
Effectiveness of multilayer coated tool in turning of aisi 430 f steeleSAT Publishing House
 
INVESTIGATING MECHINABILITY OF ALUMINIUM WITH VARIOUS PROCESS PARAMETER...
INVESTIGATING  MECHINABILITY  OF  ALUMINIUM WITH  VARIOUS  PROCESS  PARAMETER...INVESTIGATING  MECHINABILITY  OF  ALUMINIUM WITH  VARIOUS  PROCESS  PARAMETER...
INVESTIGATING MECHINABILITY OF ALUMINIUM WITH VARIOUS PROCESS PARAMETER...AMIT BANERJEE
 
Effectiveness of multilayer coated tool in turning of aisi 430 f steel
Effectiveness of multilayer coated tool in turning of aisi 430 f steelEffectiveness of multilayer coated tool in turning of aisi 430 f steel
Effectiveness of multilayer coated tool in turning of aisi 430 f steeleSAT Journals
 
Study on the effects of ceramic particulates (sic, al2 o3 and cenosphere) on ...
Study on the effects of ceramic particulates (sic, al2 o3 and cenosphere) on ...Study on the effects of ceramic particulates (sic, al2 o3 and cenosphere) on ...
Study on the effects of ceramic particulates (sic, al2 o3 and cenosphere) on ...eSAT Journals
 
Study on the effects of ceramic particulates (sic, al2 o3
Study on the effects of ceramic particulates (sic, al2 o3Study on the effects of ceramic particulates (sic, al2 o3
Study on the effects of ceramic particulates (sic, al2 o3eSAT Publishing House
 
International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)IJERD Editor
 
Experimental Approach of CNC Drilling Operation for Mild Steel Using Taguchi...
Experimental Approach of CNC Drilling Operation for Mild  Steel Using Taguchi...Experimental Approach of CNC Drilling Operation for Mild  Steel Using Taguchi...
Experimental Approach of CNC Drilling Operation for Mild Steel Using Taguchi...IJMER
 
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
 
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 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
 
IRJET- Review on Cutting Process Parameter for Surface Roughness During Turni...
IRJET- Review on Cutting Process Parameter for Surface Roughness During Turni...IRJET- Review on Cutting Process Parameter for Surface Roughness During Turni...
IRJET- Review on Cutting Process Parameter for Surface Roughness During Turni...IRJET Journal
 

What's hot (16)

Effectiveness of multilayer coated tool in turning of aisi 430 f steel
Effectiveness of multilayer coated tool in turning of aisi 430 f steelEffectiveness of multilayer coated tool in turning of aisi 430 f steel
Effectiveness of multilayer coated tool in turning of aisi 430 f steel
 
1 s2.0-s2212827117303487-main
1 s2.0-s2212827117303487-main1 s2.0-s2212827117303487-main
1 s2.0-s2212827117303487-main
 
30120140507013
3012014050701330120140507013
30120140507013
 
INVESTIGATING MECHINABILITY OF ALUMINIUM WITH VARIOUS PROCESS PARAMETER...
INVESTIGATING  MECHINABILITY  OF  ALUMINIUM WITH  VARIOUS  PROCESS  PARAMETER...INVESTIGATING  MECHINABILITY  OF  ALUMINIUM WITH  VARIOUS  PROCESS  PARAMETER...
INVESTIGATING MECHINABILITY OF ALUMINIUM WITH VARIOUS PROCESS PARAMETER...
 
Effectiveness of multilayer coated tool in turning of aisi 430 f steel
Effectiveness of multilayer coated tool in turning of aisi 430 f steelEffectiveness of multilayer coated tool in turning of aisi 430 f steel
Effectiveness of multilayer coated tool in turning of aisi 430 f steel
 
Study on the effects of ceramic particulates (sic, al2 o3 and cenosphere) on ...
Study on the effects of ceramic particulates (sic, al2 o3 and cenosphere) on ...Study on the effects of ceramic particulates (sic, al2 o3 and cenosphere) on ...
Study on the effects of ceramic particulates (sic, al2 o3 and cenosphere) on ...
 
Study on the effects of ceramic particulates (sic, al2 o3
Study on the effects of ceramic particulates (sic, al2 o3Study on the effects of ceramic particulates (sic, al2 o3
Study on the effects of ceramic particulates (sic, al2 o3
 
International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)
 
E012552428
E012552428E012552428
E012552428
 
Experimental Approach of CNC Drilling Operation for Mild Steel Using Taguchi...
Experimental Approach of CNC Drilling Operation for Mild  Steel Using Taguchi...Experimental Approach of CNC Drilling Operation for Mild  Steel Using Taguchi...
Experimental Approach of CNC Drilling Operation for Mild Steel Using Taguchi...
 
Aj4201235242
Aj4201235242Aj4201235242
Aj4201235242
 
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 ...
 
Q01314109117
Q01314109117Q01314109117
Q01314109117
 
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 ...
 
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
 
IRJET- Review on Cutting Process Parameter for Surface Roughness During Turni...
IRJET- Review on Cutting Process Parameter for Surface Roughness During Turni...IRJET- Review on Cutting Process Parameter for Surface Roughness During Turni...
IRJET- Review on Cutting Process Parameter for Surface Roughness During Turni...
 

Similar to Evaluation the Effect of Machining Parameters on MRR of Mild Steel

H0373058071
H0373058071H0373058071
H0373058071theijes
 
EFFECT OF NOSE RADIUS ON SURFACE ROUGHNESS DURING CNC TURNING USING RESPONSE ...
EFFECT OF NOSE RADIUS ON SURFACE ROUGHNESS DURING CNC TURNING USING RESPONSE ...EFFECT OF NOSE RADIUS ON SURFACE ROUGHNESS DURING CNC TURNING USING RESPONSE ...
EFFECT OF NOSE RADIUS ON SURFACE ROUGHNESS DURING CNC TURNING USING RESPONSE ...ijmech
 
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
 
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
 
Prediction of surface roughness in high speed machining a comparison
Prediction of surface roughness in high speed machining a comparisonPrediction of surface roughness in high speed machining a comparison
Prediction of surface roughness in high speed machining a comparisoneSAT Publishing House
 
Optimization of Surface Roughness for EN 1010 Low Alloy Steel on WEDM Using R...
Optimization of Surface Roughness for EN 1010 Low Alloy Steel on WEDM Using R...Optimization of Surface Roughness for EN 1010 Low Alloy Steel on WEDM Using R...
Optimization of Surface Roughness for EN 1010 Low Alloy Steel on WEDM Using R...IJAEMSJORNAL
 
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
 
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
 
Optimization of cutting parameters for surface roughness in turning
Optimization of cutting parameters for surface roughness in turningOptimization of cutting parameters for surface roughness in turning
Optimization of cutting parameters for surface roughness in turningiaemedu
 
Optimization of cutting parameters for surface roughness in turning no restri...
Optimization of cutting parameters for surface roughness in turning no restri...Optimization of cutting parameters for surface roughness in turning no restri...
Optimization of cutting parameters for surface roughness in turning no restri...iaemedu
 
Cutting fluid as a key parameter
Cutting fluid as a key parameterCutting fluid as a key parameter
Cutting fluid as a key parametervishwajeet potdar
 
Optimization of Metal Removal Rateon Cylindrical Grinding For Is 319 Brass Us...
Optimization of Metal Removal Rateon Cylindrical Grinding For Is 319 Brass Us...Optimization of Metal Removal Rateon Cylindrical Grinding For Is 319 Brass Us...
Optimization of Metal Removal Rateon Cylindrical Grinding For Is 319 Brass Us...IJERA Editor
 
IRJET- Optimization of Machining Parameters for Turning on CNC Machine of Sta...
IRJET- Optimization of Machining Parameters for Turning on CNC Machine of Sta...IRJET- Optimization of Machining Parameters for Turning on CNC Machine of Sta...
IRJET- Optimization of Machining Parameters for Turning on CNC Machine of Sta...IRJET Journal
 

Similar to Evaluation the Effect of Machining Parameters on MRR of Mild Steel (20)

IJSRDV2I1020
IJSRDV2I1020IJSRDV2I1020
IJSRDV2I1020
 
H0373058071
H0373058071H0373058071
H0373058071
 
EFFECT OF NOSE RADIUS ON SURFACE ROUGHNESS DURING CNC TURNING USING RESPONSE ...
EFFECT OF NOSE RADIUS ON SURFACE ROUGHNESS DURING CNC TURNING USING RESPONSE ...EFFECT OF NOSE RADIUS ON SURFACE ROUGHNESS DURING CNC TURNING USING RESPONSE ...
EFFECT OF NOSE RADIUS ON SURFACE ROUGHNESS DURING CNC TURNING USING RESPONSE ...
 
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...
 
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...
 
Be33331334
Be33331334Be33331334
Be33331334
 
Prediction of surface roughness in high speed machining a comparison
Prediction of surface roughness in high speed machining a comparisonPrediction of surface roughness in high speed machining a comparison
Prediction of surface roughness in high speed machining a comparison
 
Optimization of Surface Roughness for EN 1010 Low Alloy Steel on WEDM Using R...
Optimization of Surface Roughness for EN 1010 Low Alloy Steel on WEDM Using R...Optimization of Surface Roughness for EN 1010 Low Alloy Steel on WEDM Using R...
Optimization of Surface Roughness for EN 1010 Low Alloy Steel on WEDM Using R...
 
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 ...
 
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 ...
 
247
247247
247
 
Optimization of cutting parameters for surface roughness in turning
Optimization of cutting parameters for surface roughness in turningOptimization of cutting parameters for surface roughness in turning
Optimization of cutting parameters for surface roughness in turning
 
Optimization of cutting parameters for surface roughness in turning no restri...
Optimization of cutting parameters for surface roughness in turning no restri...Optimization of cutting parameters for surface roughness in turning no restri...
Optimization of cutting parameters for surface roughness in turning no restri...
 
Ijsrdv4 i70362 swati_kekade
Ijsrdv4 i70362 swati_kekadeIjsrdv4 i70362 swati_kekade
Ijsrdv4 i70362 swati_kekade
 
Cutting fluid as a key parameter
Cutting fluid as a key parameterCutting fluid as a key parameter
Cutting fluid as a key parameter
 
Master degree paper
Master degree paperMaster degree paper
Master degree paper
 
T4201135138
T4201135138T4201135138
T4201135138
 
Optimization of Metal Removal Rateon Cylindrical Grinding For Is 319 Brass Us...
Optimization of Metal Removal Rateon Cylindrical Grinding For Is 319 Brass Us...Optimization of Metal Removal Rateon Cylindrical Grinding For Is 319 Brass Us...
Optimization of Metal Removal Rateon Cylindrical Grinding For Is 319 Brass Us...
 
IRJET- Optimization of Machining Parameters for Turning on CNC Machine of Sta...
IRJET- Optimization of Machining Parameters for Turning on CNC Machine of Sta...IRJET- Optimization of Machining Parameters for Turning on CNC Machine of Sta...
IRJET- Optimization of Machining Parameters for Turning on CNC Machine of Sta...
 
G012663743
G012663743G012663743
G012663743
 

More from IJSRD

#IJSRD #Research Paper Publication
#IJSRD #Research Paper Publication#IJSRD #Research Paper Publication
#IJSRD #Research Paper PublicationIJSRD
 
Maintaining Data Confidentiality in Association Rule Mining in Distributed En...
Maintaining Data Confidentiality in Association Rule Mining in Distributed En...Maintaining Data Confidentiality in Association Rule Mining in Distributed En...
Maintaining Data Confidentiality in Association Rule Mining in Distributed En...IJSRD
 
Performance and Emission characteristics of a Single Cylinder Four Stroke Die...
Performance and Emission characteristics of a Single Cylinder Four Stroke Die...Performance and Emission characteristics of a Single Cylinder Four Stroke Die...
Performance and Emission characteristics of a Single Cylinder Four Stroke Die...IJSRD
 
Preclusion of High and Low Pressure In Boiler by Using LABVIEW
Preclusion of High and Low Pressure In Boiler by Using LABVIEWPreclusion of High and Low Pressure In Boiler by Using LABVIEW
Preclusion of High and Low Pressure In Boiler by Using LABVIEWIJSRD
 
Prevention and Detection of Man in the Middle Attack on AODV Protocol
Prevention and Detection of Man in the Middle Attack on AODV ProtocolPrevention and Detection of Man in the Middle Attack on AODV Protocol
Prevention and Detection of Man in the Middle Attack on AODV ProtocolIJSRD
 
Comparative Analysis of PAPR Reduction Techniques in OFDM Using Precoding Tec...
Comparative Analysis of PAPR Reduction Techniques in OFDM Using Precoding Tec...Comparative Analysis of PAPR Reduction Techniques in OFDM Using Precoding Tec...
Comparative Analysis of PAPR Reduction Techniques in OFDM Using Precoding Tec...IJSRD
 
Filter unwanted messages from walls and blocking nonlegitimate user in osn
Filter unwanted messages from walls and blocking nonlegitimate user in osnFilter unwanted messages from walls and blocking nonlegitimate user in osn
Filter unwanted messages from walls and blocking nonlegitimate user in osnIJSRD
 
Keystroke Dynamics Authentication with Project Management System
Keystroke Dynamics Authentication with Project Management SystemKeystroke Dynamics Authentication with Project Management System
Keystroke Dynamics Authentication with Project Management SystemIJSRD
 
Diagnosing lungs cancer Using Neural Networks
Diagnosing lungs cancer Using Neural NetworksDiagnosing lungs cancer Using Neural Networks
Diagnosing lungs cancer Using Neural NetworksIJSRD
 
A Survey on Sentiment Analysis and Opinion Mining
A Survey on Sentiment Analysis and Opinion MiningA Survey on Sentiment Analysis and Opinion Mining
A Survey on Sentiment Analysis and Opinion MiningIJSRD
 
A Defect Prediction Model for Software Product based on ANFIS
A Defect Prediction Model for Software Product based on ANFISA Defect Prediction Model for Software Product based on ANFIS
A Defect Prediction Model for Software Product based on ANFISIJSRD
 
Experimental Investigation of Granulated Blast Furnace Slag ond Quarry Dust a...
Experimental Investigation of Granulated Blast Furnace Slag ond Quarry Dust a...Experimental Investigation of Granulated Blast Furnace Slag ond Quarry Dust a...
Experimental Investigation of Granulated Blast Furnace Slag ond Quarry Dust a...IJSRD
 
Product Quality Analysis based on online Reviews
Product Quality Analysis based on online ReviewsProduct Quality Analysis based on online Reviews
Product Quality Analysis based on online ReviewsIJSRD
 
Solving Fuzzy Matrix Games Defuzzificated by Trapezoidal Parabolic Fuzzy Numbers
Solving Fuzzy Matrix Games Defuzzificated by Trapezoidal Parabolic Fuzzy NumbersSolving Fuzzy Matrix Games Defuzzificated by Trapezoidal Parabolic Fuzzy Numbers
Solving Fuzzy Matrix Games Defuzzificated by Trapezoidal Parabolic Fuzzy NumbersIJSRD
 
Study of Clustering of Data Base in Education Sector Using Data Mining
Study of Clustering of Data Base in Education Sector Using Data MiningStudy of Clustering of Data Base in Education Sector Using Data Mining
Study of Clustering of Data Base in Education Sector Using Data MiningIJSRD
 
Fault Tolerance in Big Data Processing Using Heartbeat Messages and Data Repl...
Fault Tolerance in Big Data Processing Using Heartbeat Messages and Data Repl...Fault Tolerance in Big Data Processing Using Heartbeat Messages and Data Repl...
Fault Tolerance in Big Data Processing Using Heartbeat Messages and Data Repl...IJSRD
 
Investigation of Effect of Process Parameters on Maximum Temperature during F...
Investigation of Effect of Process Parameters on Maximum Temperature during F...Investigation of Effect of Process Parameters on Maximum Temperature during F...
Investigation of Effect of Process Parameters on Maximum Temperature during F...IJSRD
 
Review Paper on Computer Aided Design & Analysis of Rotor Shaft of a Rotavator
Review Paper on Computer Aided Design & Analysis of Rotor Shaft of a RotavatorReview Paper on Computer Aided Design & Analysis of Rotor Shaft of a Rotavator
Review Paper on Computer Aided Design & Analysis of Rotor Shaft of a RotavatorIJSRD
 
A Survey on Data Mining Techniques for Crime Hotspots Prediction
A Survey on Data Mining Techniques for Crime Hotspots PredictionA Survey on Data Mining Techniques for Crime Hotspots Prediction
A Survey on Data Mining Techniques for Crime Hotspots PredictionIJSRD
 
Studies on Physico - Mechanical Properties of Chloroprene Rubber Vulcanizate ...
Studies on Physico - Mechanical Properties of Chloroprene Rubber Vulcanizate ...Studies on Physico - Mechanical Properties of Chloroprene Rubber Vulcanizate ...
Studies on Physico - Mechanical Properties of Chloroprene Rubber Vulcanizate ...IJSRD
 

More from IJSRD (20)

#IJSRD #Research Paper Publication
#IJSRD #Research Paper Publication#IJSRD #Research Paper Publication
#IJSRD #Research Paper Publication
 
Maintaining Data Confidentiality in Association Rule Mining in Distributed En...
Maintaining Data Confidentiality in Association Rule Mining in Distributed En...Maintaining Data Confidentiality in Association Rule Mining in Distributed En...
Maintaining Data Confidentiality in Association Rule Mining in Distributed En...
 
Performance and Emission characteristics of a Single Cylinder Four Stroke Die...
Performance and Emission characteristics of a Single Cylinder Four Stroke Die...Performance and Emission characteristics of a Single Cylinder Four Stroke Die...
Performance and Emission characteristics of a Single Cylinder Four Stroke Die...
 
Preclusion of High and Low Pressure In Boiler by Using LABVIEW
Preclusion of High and Low Pressure In Boiler by Using LABVIEWPreclusion of High and Low Pressure In Boiler by Using LABVIEW
Preclusion of High and Low Pressure In Boiler by Using LABVIEW
 
Prevention and Detection of Man in the Middle Attack on AODV Protocol
Prevention and Detection of Man in the Middle Attack on AODV ProtocolPrevention and Detection of Man in the Middle Attack on AODV Protocol
Prevention and Detection of Man in the Middle Attack on AODV Protocol
 
Comparative Analysis of PAPR Reduction Techniques in OFDM Using Precoding Tec...
Comparative Analysis of PAPR Reduction Techniques in OFDM Using Precoding Tec...Comparative Analysis of PAPR Reduction Techniques in OFDM Using Precoding Tec...
Comparative Analysis of PAPR Reduction Techniques in OFDM Using Precoding Tec...
 
Filter unwanted messages from walls and blocking nonlegitimate user in osn
Filter unwanted messages from walls and blocking nonlegitimate user in osnFilter unwanted messages from walls and blocking nonlegitimate user in osn
Filter unwanted messages from walls and blocking nonlegitimate user in osn
 
Keystroke Dynamics Authentication with Project Management System
Keystroke Dynamics Authentication with Project Management SystemKeystroke Dynamics Authentication with Project Management System
Keystroke Dynamics Authentication with Project Management System
 
Diagnosing lungs cancer Using Neural Networks
Diagnosing lungs cancer Using Neural NetworksDiagnosing lungs cancer Using Neural Networks
Diagnosing lungs cancer Using Neural Networks
 
A Survey on Sentiment Analysis and Opinion Mining
A Survey on Sentiment Analysis and Opinion MiningA Survey on Sentiment Analysis and Opinion Mining
A Survey on Sentiment Analysis and Opinion Mining
 
A Defect Prediction Model for Software Product based on ANFIS
A Defect Prediction Model for Software Product based on ANFISA Defect Prediction Model for Software Product based on ANFIS
A Defect Prediction Model for Software Product based on ANFIS
 
Experimental Investigation of Granulated Blast Furnace Slag ond Quarry Dust a...
Experimental Investigation of Granulated Blast Furnace Slag ond Quarry Dust a...Experimental Investigation of Granulated Blast Furnace Slag ond Quarry Dust a...
Experimental Investigation of Granulated Blast Furnace Slag ond Quarry Dust a...
 
Product Quality Analysis based on online Reviews
Product Quality Analysis based on online ReviewsProduct Quality Analysis based on online Reviews
Product Quality Analysis based on online Reviews
 
Solving Fuzzy Matrix Games Defuzzificated by Trapezoidal Parabolic Fuzzy Numbers
Solving Fuzzy Matrix Games Defuzzificated by Trapezoidal Parabolic Fuzzy NumbersSolving Fuzzy Matrix Games Defuzzificated by Trapezoidal Parabolic Fuzzy Numbers
Solving Fuzzy Matrix Games Defuzzificated by Trapezoidal Parabolic Fuzzy Numbers
 
Study of Clustering of Data Base in Education Sector Using Data Mining
Study of Clustering of Data Base in Education Sector Using Data MiningStudy of Clustering of Data Base in Education Sector Using Data Mining
Study of Clustering of Data Base in Education Sector Using Data Mining
 
Fault Tolerance in Big Data Processing Using Heartbeat Messages and Data Repl...
Fault Tolerance in Big Data Processing Using Heartbeat Messages and Data Repl...Fault Tolerance in Big Data Processing Using Heartbeat Messages and Data Repl...
Fault Tolerance in Big Data Processing Using Heartbeat Messages and Data Repl...
 
Investigation of Effect of Process Parameters on Maximum Temperature during F...
Investigation of Effect of Process Parameters on Maximum Temperature during F...Investigation of Effect of Process Parameters on Maximum Temperature during F...
Investigation of Effect of Process Parameters on Maximum Temperature during F...
 
Review Paper on Computer Aided Design & Analysis of Rotor Shaft of a Rotavator
Review Paper on Computer Aided Design & Analysis of Rotor Shaft of a RotavatorReview Paper on Computer Aided Design & Analysis of Rotor Shaft of a Rotavator
Review Paper on Computer Aided Design & Analysis of Rotor Shaft of a Rotavator
 
A Survey on Data Mining Techniques for Crime Hotspots Prediction
A Survey on Data Mining Techniques for Crime Hotspots PredictionA Survey on Data Mining Techniques for Crime Hotspots Prediction
A Survey on Data Mining Techniques for Crime Hotspots Prediction
 
Studies on Physico - Mechanical Properties of Chloroprene Rubber Vulcanizate ...
Studies on Physico - Mechanical Properties of Chloroprene Rubber Vulcanizate ...Studies on Physico - Mechanical Properties of Chloroprene Rubber Vulcanizate ...
Studies on Physico - Mechanical Properties of Chloroprene Rubber Vulcanizate ...
 

Recently uploaded

ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...
ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...
ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...Nguyen Thanh Tu Collection
 
UChicago CMSC 23320 - The Best Commit Messages of 2024
UChicago CMSC 23320 - The Best Commit Messages of 2024UChicago CMSC 23320 - The Best Commit Messages of 2024
UChicago CMSC 23320 - The Best Commit Messages of 2024Borja Sotomayor
 
demyelinated disorder: multiple sclerosis.pptx
demyelinated disorder: multiple sclerosis.pptxdemyelinated disorder: multiple sclerosis.pptx
demyelinated disorder: multiple sclerosis.pptxMohamed Rizk Khodair
 
24 ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH SỞ GIÁO DỤC HẢI DƯ...
24 ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH SỞ GIÁO DỤC HẢI DƯ...24 ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH SỞ GIÁO DỤC HẢI DƯ...
24 ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH SỞ GIÁO DỤC HẢI DƯ...Nguyen Thanh Tu Collection
 
When Quality Assurance Meets Innovation in Higher Education - Report launch w...
When Quality Assurance Meets Innovation in Higher Education - Report launch w...When Quality Assurance Meets Innovation in Higher Education - Report launch w...
When Quality Assurance Meets Innovation in Higher Education - Report launch w...Gary Wood
 
Envelope of Discrepancy in Orthodontics: Enhancing Precision in Treatment
 Envelope of Discrepancy in Orthodontics: Enhancing Precision in Treatment Envelope of Discrepancy in Orthodontics: Enhancing Precision in Treatment
Envelope of Discrepancy in Orthodontics: Enhancing Precision in Treatmentsaipooja36
 
Basic Civil Engineering notes on Transportation Engineering, Modes of Transpo...
Basic Civil Engineering notes on Transportation Engineering, Modes of Transpo...Basic Civil Engineering notes on Transportation Engineering, Modes of Transpo...
Basic Civil Engineering notes on Transportation Engineering, Modes of Transpo...Denish Jangid
 
SURVEY I created for uni project research
SURVEY I created for uni project researchSURVEY I created for uni project research
SURVEY I created for uni project researchCaitlinCummins3
 
Capitol Tech Univ Doctoral Presentation -May 2024
Capitol Tech Univ Doctoral Presentation -May 2024Capitol Tech Univ Doctoral Presentation -May 2024
Capitol Tech Univ Doctoral Presentation -May 2024CapitolTechU
 
philosophy and it's principles based on the life
philosophy and it's principles based on the lifephilosophy and it's principles based on the life
philosophy and it's principles based on the lifeNitinDeodare
 
Stl Algorithms in C++ jjjjjjjjjjjjjjjjjj
Stl Algorithms in C++ jjjjjjjjjjjjjjjjjjStl Algorithms in C++ jjjjjjjjjjjjjjjjjj
Stl Algorithms in C++ jjjjjjjjjjjjjjjjjjMohammed Sikander
 
Championnat de France de Tennis de table/
Championnat de France de Tennis de table/Championnat de France de Tennis de table/
Championnat de France de Tennis de table/siemaillard
 
MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...
MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...
MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...Krashi Coaching
 
MOOD STABLIZERS DRUGS.pptx
MOOD     STABLIZERS           DRUGS.pptxMOOD     STABLIZERS           DRUGS.pptx
MOOD STABLIZERS DRUGS.pptxPoojaSen20
 
Đề tieng anh thpt 2024 danh cho cac ban hoc sinh
Đề tieng anh thpt 2024 danh cho cac ban hoc sinhĐề tieng anh thpt 2024 danh cho cac ban hoc sinh
Đề tieng anh thpt 2024 danh cho cac ban hoc sinhleson0603
 
Graduate Outcomes Presentation Slides - English (v3).pptx
Graduate Outcomes Presentation Slides - English (v3).pptxGraduate Outcomes Presentation Slides - English (v3).pptx
Graduate Outcomes Presentation Slides - English (v3).pptxneillewis46
 
ANTI PARKISON DRUGS.pptx
ANTI         PARKISON          DRUGS.pptxANTI         PARKISON          DRUGS.pptx
ANTI PARKISON DRUGS.pptxPoojaSen20
 
How to Manage Closest Location in Odoo 17 Inventory
How to Manage Closest Location in Odoo 17 InventoryHow to Manage Closest Location in Odoo 17 Inventory
How to Manage Closest Location in Odoo 17 InventoryCeline George
 
Implanted Devices - VP Shunts: EMGuidewire's Radiology Reading Room
Implanted Devices - VP Shunts: EMGuidewire's Radiology Reading RoomImplanted Devices - VP Shunts: EMGuidewire's Radiology Reading Room
Implanted Devices - VP Shunts: EMGuidewire's Radiology Reading RoomSean M. Fox
 

Recently uploaded (20)

Mattingly "AI and Prompt Design: LLMs with Text Classification and Open Source"
Mattingly "AI and Prompt Design: LLMs with Text Classification and Open Source"Mattingly "AI and Prompt Design: LLMs with Text Classification and Open Source"
Mattingly "AI and Prompt Design: LLMs with Text Classification and Open Source"
 
ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...
ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...
ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...
 
UChicago CMSC 23320 - The Best Commit Messages of 2024
UChicago CMSC 23320 - The Best Commit Messages of 2024UChicago CMSC 23320 - The Best Commit Messages of 2024
UChicago CMSC 23320 - The Best Commit Messages of 2024
 
demyelinated disorder: multiple sclerosis.pptx
demyelinated disorder: multiple sclerosis.pptxdemyelinated disorder: multiple sclerosis.pptx
demyelinated disorder: multiple sclerosis.pptx
 
24 ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH SỞ GIÁO DỤC HẢI DƯ...
24 ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH SỞ GIÁO DỤC HẢI DƯ...24 ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH SỞ GIÁO DỤC HẢI DƯ...
24 ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH SỞ GIÁO DỤC HẢI DƯ...
 
When Quality Assurance Meets Innovation in Higher Education - Report launch w...
When Quality Assurance Meets Innovation in Higher Education - Report launch w...When Quality Assurance Meets Innovation in Higher Education - Report launch w...
When Quality Assurance Meets Innovation in Higher Education - Report launch w...
 
Envelope of Discrepancy in Orthodontics: Enhancing Precision in Treatment
 Envelope of Discrepancy in Orthodontics: Enhancing Precision in Treatment Envelope of Discrepancy in Orthodontics: Enhancing Precision in Treatment
Envelope of Discrepancy in Orthodontics: Enhancing Precision in Treatment
 
Basic Civil Engineering notes on Transportation Engineering, Modes of Transpo...
Basic Civil Engineering notes on Transportation Engineering, Modes of Transpo...Basic Civil Engineering notes on Transportation Engineering, Modes of Transpo...
Basic Civil Engineering notes on Transportation Engineering, Modes of Transpo...
 
SURVEY I created for uni project research
SURVEY I created for uni project researchSURVEY I created for uni project research
SURVEY I created for uni project research
 
Capitol Tech Univ Doctoral Presentation -May 2024
Capitol Tech Univ Doctoral Presentation -May 2024Capitol Tech Univ Doctoral Presentation -May 2024
Capitol Tech Univ Doctoral Presentation -May 2024
 
philosophy and it's principles based on the life
philosophy and it's principles based on the lifephilosophy and it's principles based on the life
philosophy and it's principles based on the life
 
Stl Algorithms in C++ jjjjjjjjjjjjjjjjjj
Stl Algorithms in C++ jjjjjjjjjjjjjjjjjjStl Algorithms in C++ jjjjjjjjjjjjjjjjjj
Stl Algorithms in C++ jjjjjjjjjjjjjjjjjj
 
Championnat de France de Tennis de table/
Championnat de France de Tennis de table/Championnat de France de Tennis de table/
Championnat de France de Tennis de table/
 
MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...
MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...
MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...
 
MOOD STABLIZERS DRUGS.pptx
MOOD     STABLIZERS           DRUGS.pptxMOOD     STABLIZERS           DRUGS.pptx
MOOD STABLIZERS DRUGS.pptx
 
Đề tieng anh thpt 2024 danh cho cac ban hoc sinh
Đề tieng anh thpt 2024 danh cho cac ban hoc sinhĐề tieng anh thpt 2024 danh cho cac ban hoc sinh
Đề tieng anh thpt 2024 danh cho cac ban hoc sinh
 
Graduate Outcomes Presentation Slides - English (v3).pptx
Graduate Outcomes Presentation Slides - English (v3).pptxGraduate Outcomes Presentation Slides - English (v3).pptx
Graduate Outcomes Presentation Slides - English (v3).pptx
 
ANTI PARKISON DRUGS.pptx
ANTI         PARKISON          DRUGS.pptxANTI         PARKISON          DRUGS.pptx
ANTI PARKISON DRUGS.pptx
 
How to Manage Closest Location in Odoo 17 Inventory
How to Manage Closest Location in Odoo 17 InventoryHow to Manage Closest Location in Odoo 17 Inventory
How to Manage Closest Location in Odoo 17 Inventory
 
Implanted Devices - VP Shunts: EMGuidewire's Radiology Reading Room
Implanted Devices - VP Shunts: EMGuidewire's Radiology Reading RoomImplanted Devices - VP Shunts: EMGuidewire's Radiology Reading Room
Implanted Devices - VP Shunts: EMGuidewire's Radiology Reading Room
 

Evaluation the Effect of Machining Parameters on MRR of Mild Steel

  • 1. IJSRD - International Journal for Scientific Research & Development| Vol. 3, Issue 10, 2015 | ISSN (online): 2321-0613 All rights reserved by www.ijsrd.com 1037 Evaluation the Effect of Machining Parameters on Mrr of Mild Steel Mr. Sandeep Boora1 Mr. Sandeep Kumar2 1 Student 2 H.O.D 1,2 Department of Mechanical Engineering 1,2 MIET, India Abstract— Turning metal removal is the removal of metal outside diameter of a rotating cylindrical workpiece. As it is used to reduce the diameter. of the work piece generally to a specified dimension and to produce a smooth finish on the metal. Often, the work piece is turned so that the sections have different diameters. As is the machining operation that produces cylindrical. In its basic form, it can be defined as an outer surface machining: Based on the findings of the Pilot study, actual experimentation work will be designed and input machining parameters and their values finalized. The results are expected to show that the response variables (output parameters) strongly influenced by the control factors (input parameters). So, the results which are obtained after experimentation analyzed and modeled for their application in manufacturing industry It is concluded that for MRR be maximum factor Cutting speed has to be at high level 3, Feed has to be at high level & D.O.C has to be at high level. Key words: Taguchi Design, Orthogonal Array, Turning, Cutting Speed, Feed I. INTRODUCTION Kohli and Dixit (2005) [1] proposed a method based on the neural network with the acceleration of the radial vibration of the tool holder as feedback. The backpropagation algorithm is used to train the network model for predicting the surface roughness in turning process. The methodology was validated for turning dry and wet steel using high speed steel and carbide tool and noted that the proposed methodology was able to make an accurate prediction of surface roughness using small formation of size and testing data sets. Pal and Chakraborty (2005) [2] studied to develop a model of neural network back-propagation for prediction of surface roughness in turning operation and is used mild steel work pieces with high speed steel as the cutting tool to carry out a number of experiments. The authors used speed, feed, depth of cut and cutting forces as inputs to the neural network model for prediction of surface roughness. The work resulted in the intended surface roughness was very close to the experimental value.Sing and Kumar (2006) [3] studied the optimization of the feed force through optimum adjustment of process parameters namely speed, feed and depth of cut in steel processing with carbide inserts EN24 TiC coated tungsten. The authors used the approach Taguchi design parameters and concluded that the effect of feed and cutting depth variation in feed force affected more as compared to the speed. Ahmed (2006) [4] developed the methodology for obtaining the optimum process parameters for prediction of surface roughness in turning Al. To develop the empirical model nonlinear regression analysis with logarithmic transformation of data it was applied. The model developed showed little mistakes and satisfactory results. The study concluded that low feed rate was good to produce low surface roughness and high speed couldproduce high surface quality within the experimental domain. Abburi and Dixit (2006) [5] they developed based on knowledge for predicting surface roughness in turning process system. Fuzzy set theory and neural networks were used for this purpose. The authors developed rule to predict the surface roughness for the process variables as well as for the prediction of process variables for a given surface roughness. Zhong et al. (2006) [6] predicted the surface roughness of machined surfaces using nets with seven innings namely degree insertion tool, workpiece materials, cutting edge radius, angle, depth of cut, spindle speed and feed rate . Kumanan et al. (2006) [7] proposed a methodology for predicting the machining forces using multilayer perceptron trained by genetic algorithm (GA). The data obtained from experimental results of a turning process is explored to train artificial neural networks proposed (RNAs) with three inputs for output machining forces. ANN optimal weights were obtained using GA search. This hybrid function replacing made of GA and ANN found computationally efficient and accurate to predict the forces of machining for the machining conditions of entry. Mahmoud and Abdelkarim (2006) [8] studied in turning operation using high speed steel (HSS) cutting tool 450 at an angle of approach. This tool proved he could carry out the cutting operation at higher speeds and longer service life of the traditional tool with a rake angle of 90 degrees. The study ultimately determines the optimum cutting speed for high production speed and minimum cost and tool, production time and operating costs. Doniavi et al. (2007) [9] using the response surface methodology (RSM) in order to develop the empirical model for the prediction of surface roughness, in deciding the optimal cutting condition in the transformation. The authors showed that the feed rate significantly influenced surface roughness. With increased surface roughness speed power was found to be increased. With the increase in the cutting speed decreased surface roughness. Analysis of variance showed that the influence of the feed rate and were in surface roughness of the cutting depth was applied. Kassab and Khoshnaw (2007) [10] examined the correlation between surface roughness and vibration cutting tool for turning operations. The process parameters were cutting speed, depth of cut, feed rate and outstanding tool. The experiments were carried out on the lathe turning using dry (without cutting fluid) operation of medium carbon steel with different levels of process parameters mentioned above. Turning was dry useful for a good correlation between the surface roughness and the cutting tool vibration due clean environment. The authors developed a good correlation between the vibration cutting tool surface roughness and to control the surface finish of the workpieces during mass production. The study concluded that it was observed that the surface roughness of the workpiece to be affected more by the acceleration cutting tool; acceleration increased overhang of the cutting tool. The surface roughness was found to increase with increasing feed rate. Al-Ahmari (2007) [11] developed
  • 2. Evaluation the Effect of Machining Parameters on Mrr of Mild Steel (IJSRD/Vol. 3/Issue 10/2015/238) All rights reserved by www.ijsrd.com 1038 empirical models for the tool life, surface roughness and cutting force for turning operation. The process parameters were used in the study speed, feed, depth of cut and the nose radius to develop the model machining. The methods used to develop 48 models mentioned above were Response Surface Methodology (RSM) and neural networks (NN). Thamizhmanii et al. (2007) [12] applied the Taguchi method to find the optimal value of the roughness of the surface under optimal cutting conditions in turning SCM 440 alloy steel. The experiment was designed using the Taguchi method and experiments were carried out and the results thereof were analyzed with the help of ANOVA (analysis of variance). The causes of poor surface finish as vibrations were detected machine tool, tool chatter whose effects were ignored for analysis. The authors concluded that the results obtained by this method would be useful for other research study similar type of tool vibrations, forces, etc. The study concluded that cutting deppth was the only major factor that contributed to the roughness of the cutting surface. Natarajan et al. (2007) [13] introduced the technique of monitoring tool wear online processing operation. Spindle speed, feed, cutting depth, cutting force, power spindle motor and temperature were selected as the input parameters for the monitoring technique. To determine the degree of wear of the tool; Two methods of Hidden Markov Model (HMM) method such as bar-graph and multiple modeling methods were used. An algorithm of decision fusion center (DFCA) was used to increase the reliability of this output that combines the outputs of the individual methods to make a comprehensive decision on the wear of the tool. Finally, all the proposed methods were combined in a DFCA to determine the wear of the tool during turning operations. Ozel et al. (2007) [14] conducted the finish turning of steels AISI D2 (60 HRC) using ceramic (multi-radio) design inserts cleaner surface finish and tool flank wear research. For prediction of surface roughness and tool flank wear multiple linear regression models and neural network models were developed. Predictions based neural network of surface roughness and tool flank wear were carried out in comparison with a non-formation experimental data and the results thereof showed that neural network models proposed were efficient for predicting patterns tool wear and surface roughness for a range of cutting conditions. The study concluded that the best tool life was obtained in lower feed rate and the lowest shear rate combination. Wang Lan (2008) [15] using orthogonal array of Taguchi method gray relational analysis with considering four parameters viz. speed, depth, rate of cutting tool nose to drain, etc., for the optimization of three responses: surface roughness, the tool wear and material removal rate on the accuracy of light a ECOCA-3807 CNC lathe. It explored the MINITAB software to analyze the average signal-to-noise (S / N) effect to achieve the multi-purpose features. This study not only suggests an optimization approaches using orthogonal matrix and gray relational analysis, but also contributed a satisfactory technique to improve performance of multiple precision machining CNC turning, with profound insight. Srikanth and Kamala (2008) [16] evaluated the optimal values of cutting parameters using a genetic algorithm coded Real (RCGA) and various issues RCGA and its advantages over the current approach of Binary Coded Genetic Algorithm explained ( BCGA). They concluded that RCGA was reliable and accurate to solve the optimization parameter and build cutting optimization problem with multiple decision variables. These decision variables were cutting speed, feed, depth of cut and nose radius. The authors noted that the fastest solution RCGA can be obtained with relatively high success rate, with selected machining conditions providing for overall improvement mode product quality by reducing the cost of production, reduction in time production, flexibility in the selection of machining parameters. Sahoo et al. (2008) [17] studied for optimization of machining parameters combinations emphasis on fractal characteristics of surface profile generated in CNC turning operation. The authors used the L27 orthogonal array design with machining Taguchi parameters: speed, feed and depth of cut in three different work piece materials viz. aluminum, mild steel and brass. It was concluded that the feed rate was more significant influence surface finish on the three materials. It was observed that in the case of mild steel and aluminum feed showed some influences, whereas in the case of brass cutting depth was noticed impose some influence on the surface finish. The interaction factor was responsible for controlling the fractal dimensions of the surface profile produced in CNC turning. II. DESIGN OF EXPERIMENTS Engineering methods, the quality of Dr. Taguchi hired to design experiments (DOE) is a statistical tool, the most important of TQM for the design of high quality at lower prices Taguchi Method provides an effective method and a system for optimizing the design for. Performance, quality and cost. The basic design stage trials traditionally are too complex and not easy to use. Many of the trials have to be carried out on a number of process parameters added. To solve this problem Taguchi method uses a special design of the array to study the parameter space that only a small number of trials. Taguchi is the developer of how Taguchi Method was widely used in engineering analysis and includes a plan of experiments with the aim of getting information in a controlled and to obtain information about the circumstances. of assignment process The greatest advantage of the method is to save the efforts in conducting trials. So you reduce the time trials as well as the costs by a factor quickly. A. Process Variables and their Limits First pilot experiments were done on the work piece using random values and then from those pilot experiments the suitable values of these parameters were selected. On the basis of observations from the pilot experiments these levels were found suitable for the experimentation. Factors Units Level- 1 Level- 2 Level- 3 Spindle Speed (N) RPM 220 330 440 Feed (F) Mm/Rev. 0.2 0.3 0.4 Depth Of Cut (DOC) Mm 0.5 1 1.5 Table 1: Process variables and their limits. B. L9 Orthogonal Array Run C.S Feed D.O.C
  • 3. Evaluation the Effect of Machining Parameters on Mrr of Mild Steel (IJSRD/Vol. 3/Issue 10/2015/238) All rights reserved by www.ijsrd.com 1039 1 440 0.4 0.5 2 440 0.3 1 3 440 0.2 1.5 4 330 0.4 1 5 330 0.3 1.5 6 330 0.2 0.5 7 220 0.4 1.5 8 220 0.3 0.5 9 220 0.2 1 Table 2: Taguchi’s L9 Orthogonal Array. C. Material Removal Rate Measurement Material removal rate (MRR) has been calculated from the difference of weight of work piece before and after experiment. D. Measurement of MRR C.S Feed D.O.C Time Initial Wt. Final Wt. Reduction in Wt. (Wi- Wf)/ρst in mm3 / sec 440 0.4 0.5 11.16 282.5 276.86 5.64 65.63 440 0.3 1 16.13 288.51 278.26 10.25 82.53 440 0.2 1.5 23.8 288.8 264.44 24.36 132.93 330 0.4 1 16.59 285.88 277.6 8.28 64.82 330 0.3 1.5 23.87 279.96 265.2 14.76 80.31 330 0.2 0.5 32.4 285.52 272.66 12.86 51.55 220 0.4 1.5 24.67 284.96 268.65 16.31 85.86 220 0.3 0.5 31.19 284.74 279.49 5.25 21.86 220 0.2 1 44.65 281.59 280.3 1.29 3.75 Table 3: Measurement of MRR E. Response Table for Means Response Table for Means Level C.S Feed D.O.C 1 37.16 62.74 46.35 2 65.56 61.56 50.37 3 93.70 72.10 99.70 Delta 56.54 10.54 53.35 Rank 1 3 2 Table 4: Response Table for Means Fig. 1: Shows the effect of different parameters on MRR. III. ANALYSIS A. Analysis of Variance for Means Source DF SeqSS Adj SS Adj MS F P C.S 2 4794.8 4794.8 2397 .4 4.39 0.1 85 Feed 2 200.1 200.1 100. 1 0.18 0.8 45 D.O.C 2 5296.0 5296.0 2648 .0 4.85 0.1 71 Residual Error 2 1091.4 1091.4 545. 7 Total 8 11382.3 Table 5: Analysis of Variance for Means IV. CONCLUSION It is concluded that for MRR be maximum factor Cutting speed has to be at high level 3, Feed has to be at high level 3 & D.O.C has to be at high level 3. As shown in table below. Physical Optimal Combination Requirements C.S F D.O.C Maximum MRR 440 0.4 1.5 Level-3 Level-3 Level-3 Table 5.1: Optimal combination for MRR REFERENCES [1] Kohli A. & Dixit U. S., (2005),”A neural-network- based methodology for the prediction of surface roughness in a turning process”, International Journal of Advanced Manufacturing Technology, Volume 25, pp..118–129. [2] Pal S. K. & Chakraborty D., (2005), “Surface roughness prediction in turning using artificial neural network”, Neural Computing & Application, Volume14, pp. 319– 324. [3] Singh H. & Kumar P., (2006), “Optimizing Feed Force for Turned Parts through the Taguchi Technique”, Sadhana, Volume 31, Number 6, pp.. 671–681. 58 [4] Ahmed S. G., (2006), “Development of a Prediction Model for Surface Roughness in Finish Turning of Aluminium”, Sudan Engineering Society Journal, Volume 52, Number 45, pp.. 1-5. [5] Abburi N. R. & Dixit U. S., (2006), “A knowledge- based system for the prediction of surface roughness in turning process” Robotics & Computer-Integrated Manufacturing, Volume 22, pp.. 363–372. [6] Zhong Z. W., Khoo L. P. & Han S. T., (2006), “Prediction of surface roughness of turned surfaces using neural networks”, International Journal of Advance Manufacturing Technology, Volume 28, pp.. 688–693. [7] Kumanan S., Saheb S. K. N. & Jesuthanam C. P., (2006), “Prediction of Machining Forces using Neural
  • 4. Evaluation the Effect of Machining Parameters on Mrr of Mild Steel (IJSRD/Vol. 3/Issue 10/2015/238) All rights reserved by www.ijsrd.com 1040 Networks Trained by a Genetic Algorithm”, Institution of Engineers (India) Journal, Volume 87, pp.. 11-15. [8] Mahmoud E. A. E. & Abdelkarim H. A., (2006), “Optimum Cutting Parameters in Turning Operations using HSS Cutting Tool with 450 Approach Angle”, Sudan Engineering Scoeiety Journal, Volume 53, Number 48, pp.. 25-30. [9] Doniavi A., Esk&erzade M. & Tahmsebian M., (2007), “Empirical Modeling of Surface Roughness in Turning Process of 1060 steel using Factorial Design Methodology”, Journal of Applied Sciences, Volume 7, Number17, pp.. 2509-2513. [10]Kassab S. Y. & Khoshnaw Y. K., (2007), “The Effect of Cutting Tool Vibration on Surface Roughness of Work piece in Dry Turning Operation”, Engineering & Technology, Volume 25, Number 7, pp.. 879-889. [11]Al-Ahmari A. M. A., (2007),”Predictive machinability models for a selected hard material in turning operations”, Journal of Materials Processing Technology, Volume 59 190, pp.. 305–311. [12]Thamizhmanii S., Saparudin S. & Hasan S., (2007), “Analysis of Surface Roughness by Using Taguchi Method”, Achievements in Materials & Manufacturing Engineering, Volume 20, Issue 1-2, pp.. 503-505. [13]Natarajan U., Arun P., Periasamy V. M., (2007), “On- line Tool Wear Monitoring in Turning by Hidden Markov Model (HMM)” Institution of Engineers (India) Journal (PR), Volume 87, pp.. 31-35. [14]Özel T. & Karpat Y., (2005), “Predictive modeling of surface roughness & tool wear in hard turning using regression & neural networks”, International Journal of Machine Tools & Manufacture, Volume 45, pp.. 467– 479. [15]Wang M. Y. & Lan T. S., (2008), “Parametric Optimization on Multi-Objective Precision Turning Using Grey Relational Analysis”. Information Technology Journal, Volume 7, pp..1072-1076. [16]Srikanth T. & Kamala V., (2008), “A Real Coded Genetic Algorithm for Optimization of Cutting Parameters in Turning IJCSNS”, International Journal of Computer Science & Network Security, Volume 8 Number 6, pp.. 189-193. [17]Sahoo P., Barman T. K. & Routara B. C., (2008), “Taguchi based practical dimension modeling & optimization in CNC turning”, Advance in Production pp..60.