Parametric optimization of surface roughness in turning inconel718 using tag
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Transcript of Parametric optimization of surface roughness in turning inconel718 using tag
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 4, July - August (2013) © IAEME
125
PARAMETRIC OPTIMIZATION OF SURFACE ROUGHNESS IN TURNING
INCONEL718 USING TAGUCHI METHODS AND ITS PREDICTION BY
REGRESSION ANALYSIS
Prasad.K.K1
, Dr. D. Chaudhary2
1Professor, Department of Mechanical Engineering, GNDEC, Bidar, Karnataka, India.
2Professor & Head, Department of Mechanical Engineering, GNDEC, Bidar, Karnataka, India.
ABSTRACT
Machining is a complex process wherein many variables are involved which are having a
bearing on the quality of the end products. Surface roughness is one of the most specified quality
characteristics which affect the functional behavior of parts. An excellent surface finish significantly
improves fatigue strength, corrosion resistance, creep life and also affects several other functional
attributes. There are controllable parameters like cutting speed, feed, depth of cut, nose radius and
uncontrollable parameters like machine tool vibration, tool wear, workmaterial flaws etc which are
having a telling influence on the quality of machined components.
There are objectives like surface roughness and tool life which imposes conflicting
requirement on parameters, so optimization of parameters assumes greater importance in machining.
In addition, there is a need for tools that will allow the prediction of quality characteristics in
advance to maximize the gain from machining operations.
The turning being the most widely used machining process, this work focused on parametric
optimization of surface roughness while turning components on CNC lathe. Since the effect of the
parameters on resulting surface roughness have not been quantified yet particularly when machining
difficult-to-machine materials like INCONEL718 super alloy using uncoated carbide turning inserts,
this work concentrated on those aspects. The experiment was performed based on L27 Taguchi
Orthogonal Arrays and optimal parameter setting was determined using Signal-to-Noise (S/N) ratio,
Lower-The-Better criterion. The significance of the parameters was determined by employing
Analysis of Variance (ANOVA) and the mathematical modeling and prediction of the surface
roughness is accomplished by Multiple Regression Analysis (MRA). The result obtained indicates
that Taguchi method is capable of optimizing process parameters in turning process and the
mathematical model obtained as a result of regression analysis can be reliably used for the prediction
of surface roughness.
INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING
AND TECHNOLOGY (IJMET)
ISSN 0976 – 6340 (Print)
ISSN 0976 – 6359 (Online)
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KEY WORDS: Surface roughness, optimization, Taguchi methods, Multiple Regression analysis
(MRA), Inconel718
1. INTRODUCTION
Machining is a term that covers a large collection of manufacturing processes designed to
remove unwanted maternal usually in the form of chips, from a workpiece to give the desired
geometry, size and finish specialized to fulfill design requirements. Almost every manufactured
product has components that require machining, often to great precision [1]
The overall value of a product and its acceptance by the customer is typically determined by
its performance with respect to multiple measures like how closely they adhere to set product
specifications for length, width, diameter, surface finish and reflective properties. Surface finish is
one of the most important quality measures that manufacturers must be able to control. Surface
roughness of machined components depends on many factors. Some of these factors can be
controlled and some cannot. Controllable process parameters include cutting speed, feed, depth of
cut, tool geometry (ie, nose radius, rake angle etc). Other factors such as vibrations of tool,
workpiece and machine tool, tool wear, variability of work material and tool material etc cannot be
controlled easily are called noise factors.[2]
Surface roughness refers to the relatively closely spaced or fine surface irregularities mainly
in the form of feed marks left by the cutting tool on the machined surface [3]. It plays a very
important role in the performance of turned workpiece as a good quality turned surface significantly
improves fatigue strength, corrosion resistance and creep life. Surface roughness also affects several
functional attributes of parts such as contact causing surface friction, wearing, light reflection, heat
transmission, ability of holding and distributing lubricant, load bearing capacity and resistance to
fatigue. [4]
Surface roughness is specified by the extent of deviation of the finished surface from the
ideal surface. There are different ways of representing this deviation. However arithmetic mean of
roughness or arithmetic mean deviation of roughness (Ra) is the most commonly used surface
roughness measure [5].Ra is the arithmetic mean of absolute values of the evaluation profile
deviations (yi) from the mean line and it is evaluated using the equation 1
Ra = (1/n) (∑ yi���� ----------------------- (01)
For the efficient use of machine tools optimum cutting conditions are required to be
determined because under-optimized machining conditions will result in loss of quality as well as
productivity. There are many optimization techniques employed for optimization of machining
parameters which include fussy logic, genetic algorithms, Taguchi techniques, response surface
methodology, Ant colony optimization, Artificial Neural Networks etc. A detailed review of
optimization techniques can be observed in the article mentioned in reference [6].
This work used Taguchi techniques for achieving optimization because of the simple reason
that it enables multiple complex properties to be optimized at minimal cost. Taguchi Design of
Experiments (DoE) methods incorporate Orthogonal Arrays (OA) to minimize the number of
experiments required to determine the effect of process factors upon the performance characteristics.
This approach allows a statically sound experiment to be completed while investigating a minimum
number of possible combinations of factors. Using this approach the goal can be accomplished in a
timely manner and at a reduced cost with results comparable to full factorial experiment [7].
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2. MATERIAL AND METHODS
2.1 MATERIAL: INCONEL718
Machining of Nickel based super alloys is a challenging task due to more reasons than one.
But it is worth to take up these challenges since these alloys are very popular in the industry due to
their superior properties. They have excellent high temperature strength, high corrosion and
oxidation resistance as well as resistance to thermal fatigue, thermal shock, creep and erosion
[8].Among Nickel based alloys, Inconel718 is the most widely employed construction material in the
aerospace industry, in particular in hot sections of gas turbine engines. Due to high shear strength,
low thermal conductivity, tendency to form Built Up Edge (BUE), chemical reaction tendency at
high temperatures, and high abrasive carbide particles in the micro structure and work hardening
tendency, this alloy is classified under the category of difficult to machine material. During
machining process, the interaction between the tool and workpiece causes severe plastic deformation
in the local areas of the workpiece, and intense friction at the tool work interface resulting in
excessive tool wear, low productivity and high power consumption [9].
2.2 Taguchi method
Taguchi philosophy provides two tenets (1) reduction in variation (improved quality) of a
product or process which represents a lower loss to society, and (2) the proper development of a
strategy that intentionally reduce variation.[10]
Taguchi method is an experimental technique which is useful in reducing the number of
experiments dramatically by using Orthogonal Arrays and also tries to minimize the effects of factors
out of control. The greatest advantage of Taguchi method is to decrease the experimental time, to
reduce the cost and to find out the significant factors in a shorter time period.
The most reliable of Taguchi techniques is the use of parameter design, which is an
engineering method for product or process design that focuses on determining the parameter (factor)
settings producing the best levels of a quality characteristics (performance measure) with minimum
variations[11].
Taguchi converts the objective function values to Signal-to-Noise ratio (S/N ratio) to measure
the performance characteristics of the levels of control factors. [9].The S/N ratio takes both the mean
and variability into account. In its simplest form, the S/N ration is the ratio of the mean (signal) to
the standard deviation (noise).[12]
The S/N ratio depends on the criteria of the quality characteristics to be optimized.
Depending upon the type of quality characteristics to be optimized, there are three important types of
S/N ratios defined. They are
(a) Smaller- the- Better Type (STB)
--------------------------- (02)
(b) Larger-the-Better Type (LTB)
------------------- (03)
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(c) Nominal-the-Best,
------------------------- (04)
Where yi is the measured response in ith
run, ‘n’ is the number of observations in a row. � is
the average of the observed data and s2 is the variance.
Since minimum surface roughness value is desirable, Smaller - the- Better Type of quality
characteristics is used (Eqn - 2). [11-13]
Analysis of Variance (ANOVA) is used to determine the statistical significance of the control
parameters. The optimum combination of cutting parameters is determined with the help of main
effect plots.
2.3 MULTIPLE REGRESSION ANALYSIS (MRA)
The objective of multiple regression analysis is to construct a model that explains as much as
possible, the variability in a dependent variable, using several independent variables. The model fit is
called regression model, is usually a linear model, though sometimes non linear models such as log-
linear models are also constructed. To include interaction terms, the following model is used in this
investigation.
Yi = ß0 + ß1 x1 + ß2 x2 +.....+ ßm xm + ß12 x1x2 + ß13x1x3 +....+ ß1m x1 xm + �.---(05)
Where Yi is the dependent variable and X1 …………… Xm are the independent variables � is the error
term. The coefficients, ß0,, ß1……………ß m , ß12, ß13……… ß1m are constants.[14]
The fitted model can be utilized to estimate the values of the responses.
3. EXPERIMENTAL
3.1 Work Material and Tool Turning experiment was performed on CNC lathe with Inconel 718 rod of 25mm diameter
and 100mm length (Fig.1) using uncoated carbide turning insert of Sandvik Coromant make (Fig.2)
with ISO specification numbers as given below.
1. CNMG12 04 04-QM H13A
2. CNMG12 04 08-QM H13A
3 CNMG12 04 12-QM H13A
FIG. 1 INCONEL718 FIG.2 UNCOATED CARBIDE WORKPIECE MATERIAL TURNING INSERT
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3.2 MEASUREMENT OF SURFACE ROUGHNESS
In this investigation, surface roughness (Ra) is measured by MITUTOYO SJ210 SURF
TEST, a stylus type profilometer (Fig3) and its specifications are given in table 1. Each surface is
characterized by the average surface roughness Ra value. The cut off length λc and the sampling
number (N) are selected as 0.8mm and5 respectively, and travel length selected is 4mm. In total four
different measurements in the scan direction are taken on the textured surface. The average of those
four measurements is used to find out the ultimate Ra values.
Table1. Specifications of SURFTEST SJ-210
FIG 3. SURFTEST SJ-210
Portable Surface Roughness Tester
Sl.No. Details Values
1 Measurement Range 360µm
2 Stylus Diamond
3 Tip radius 5 µm
4 Measuring Force 4mN
5 Ditector range 21mm
6 Transverse speed 0.25mm/s(measurement)
1mm/s(return)
7 Resolution 0.0016 µm
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4. SELECTION OF STANDARD ORTHOGONAL ARRAY AND ITS CONSTRUCTION
4.1 Basis for Selection of Orthogonal Array
Before constructing an orthogonal array, the following requirements must be defined:
• Number of factors to be studied
• Number of levels for each factor
• Specific 2-factor interactions to be estimated
4.2 COUNTING DEGREES OF FREEDOM The first step in constructing an Orthogonal Array to fit a specific case study is to count the
total degrees of freedom which tell the minimum number of experiments that must be performed to
study all the chosen control factors. To begin with, one degree of freedom is associated with the
overall mean regardless of the number of control factors to be studied. In general, the number of
degrees of freedom associated with a factor is equal to one less than the number of levels for that
factor. The degrees of freedom associated with interaction between two factors are given by the
product of the degrees of freedom for each of the two factors. A suitable OA is selected based on the
Degree of freedom [13]
4.3 EXPERIMENTAL DESIGN Number of parameters = 4
Number of levels for each parameters = 3
Total degree of freedom (DOF) for 4 parameters = 4× (3-1) = 8
Number of interactions considered (AXB), (AXD), and (BXD)
Degree of freedom for interactions= 3X2X2=12
Therefore Minimum number of experiment = Total DOF for parameters +1
= 20 + 1
Minimum number of experiment = 21
L27(3)13
orthogonal array of Taguchi is selected.
5. CONSTRUCTION OF ORTHOGONAL ARRAYS
5.1 Factors with codes and Levels
Table2. Parameter combinations for Experiment with four factors and three levels for INCONEL
718 using uncoated carbide tool [15-17]
Parameters/Factors
Levels
1 2 3
Speed(A) m/min 25 30 35
Feed(B) mm/rev 0.08 0.1 0.12
Depth of cut(C) mm 0.15 0.35 0.55
Nose radius(D)mm 0.4 0.8 1.2
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6. FACTOR ASSIGNMENTS ON L27 ORTHOGONAL ARRAY
TABLE3. Factor Assignments For INCONEL 718 and Experimental Results
TOOL: UNCOATED CARBIDE CNMG120404-QM H13A, CNMG120408-QMH13A,
CNMG120412-QM H13A
Expt.N0.
Column numbers and factor assignments Ra
Measured
S/N Ratio
Ratio
Ra predicted
1 2 3 5 6 8 9
A B
A*B D A*D B*D C µm �(dB) µm
1 25 0.08
1 0.4 1 1
0.15 0.559 5.0518 0.57984
2 25 0.08 1 0.8 2 2 0.35 0.448 6.9744 0.44355
3 25 0.08 1 1.2 3 3 0.55 0.317 9.9788 0.30297
4 25 0.1 2 0.4 1 2 0.35 0.767 2.3041 0.77100
5 25 0.1 2 0.8 2 3 0.55 0.636 3.9309 0.63245
6 25 0.1 2 1.2 3 1 0.15 0.463 6.6884 0.47157
7 25 0.12 3 0.4 1 3 0.55 0.955 0.3999 0.96199
8 25 0.12 3 0.8 2 1 0.15 0.782 2.1359 0.80314
9 25 0.12 3 1.2 3 2 0.35 0.641 3.8628 0.66482
10 30 0.08 2 0.4 2 1 0.35 0.591 4.5683 0.59534
11 30 .008 2 0.8 3 2 0.55 0.461 6.7260 0.45702
12 30 0.08 2 1.2 1 3 0.15 0.288 10.8122 0.29666
13 30 0.1 3 0.4 2 3 0.55 0.78 2.1581 0.77954
14 30 0.1 3 0.8 3 2 0.15 0.607 4.3362 0.61188
15 30 0.1 3 1.2 1 1 0.35 0.64 3.8764 0.51254
16 30 0.12 1 0.4 2 3 0.15 0.926 0.6678 0.89889
17 30 0.12 1 0.8 3 1 0.35 0.785 2.1026 0.78319
18 30 0.12 1 1.2 1 2 0.55 0.654 3.6884 0.67028
19 35 0.08 3 0.4 3 1 0.55 0.605 4.3649 0.61090
20 35 0.08 3 0.8 1 2 0.15 0.431 7.3105 0.45281
21 35 0.08 3 1.2 2 3 0.35 0.29 10.7520 0.31426
22 35 0.1 1 0.4 3 2 0.15 0.751 5.0518 0.72345
23 35 0.1 1 0.8 1 3 0.35 0.609 6.9744 0.60828
24 35 0.1 1 1.2 2 1 0.55 0.479 9.9788 0.49462
25 35 0.12 2 0.4 3 3 0.35 0.928 2.3041 0.91236
26 35 0.12 2 0.8 1 1 0.55 0.798 3.9309 0.82207
27 35 0.12 2 1.2 2 2 0.15 0.625 6.6884 0.64060
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7. RESULTS AND DISCUSSION
7.1. Analysis Using MINITAB Software After machining work pieces, response values were noted down (Table3) and with the help
of MINITAB14 software optimum levels of control factors determined based on S/N ratios. ANOVA
was performed to find out the influence of various factors on objective functions. Multiple
Regression Analysis (MRA) was performed to construct mathematical models and to estimate Ra.
Experiments were conducted on INCONEL718 based on L27 Orthogonal Array using uncoated
carbide turning insert.
7.2 Taguchi Analysis: Ra versus SPEED, FEED, DOC, NR
Table4. Response Table for Signal to Noise Ratios for Ra
Smaller is better
Level SPEED FEED DOC NR
1 4.592 7.393 4.841 2.517
2 4.326 4.054 4.377 4.420
3 4.701 2.172 4.400 6.682
Delta 0.375 5.221 0.464 4.165
Rank 4 1 3 2
Table5. Analysis of Variance for S/N Ratio of Ra
Source DF SS MS F P Contribution
(%)
SPEED 2 0.668 0.334 0.375 0.493 0.31
FEED 2 125.859 62.929 70.627 0.000 57.98
DOC 2 1.231 0.6155 0.691 0.144 0.567
NR 2 78.249 39.1245 43.91 0.000 36.05
SPEED*FEED 4 0.459 0.115 0.129 0.610 0.21
SPEED*NR 4 0.231 0.06 0.067 0.723 0.17
FEED*NR 4 5.031 1.258 1.412 0.019 2.32
Error 06 5.343 0.891 01 0.41
Total 26 217.072
S = 0.667293 R-Sq = 97.54% R-Sq (adj) = 94.67%
7.3 Regression Analysis: Ra versus SPEED, FEED, DOC AND NR
The regression equation is
Ra = 0.0194 - 0.00058 SPEED + 8.62 FEED + 0.0753 DOC - 0.344 NR
+ 0.0104 SPEED*FEED - 0.000508 SPEED*NR - 0.141 FEED*NR---- (6)
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Table6. Regression Table
Predictor Coef T P
Constant 0.01937 0.27 0.793
SPEED -0.000578 -0.36 0.725
FEED 8.6222 21.32 0.000
DOC 0.07530 1.85 0.080
NR -0.34362 -16.96 0.000
SPEED*FEED 0.010446 1.42 0.171
SPEED*NR -0.0005082 -1.01 0.327
FEED*NR -0.1414 -0.97 0.344
Analysis of Variance
Source SS F P
Regression 0.88107 106.91 0.000
Residual Error 0.02237
Total 0.90344
S = 0.0343120 R-Sq = 97.5% R-Sq (adj) = 96.6%
FIG.4. Main Effects Plot (data means) for SN ratios
Mean of SN ratios
353025
8
6
4
2
0.120.100.08
0.550.350.15
8
6
4
2
1.20.80.4
SPEED FEED
DOC NR
Main Effects Plot (data means) for SN ratios(Ra)
Signal-to-noise: Smaller is better
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FIG.5.Interaction Plot (data means) for SN ratios
FIG.6 Comparison of Measured and Predicted Values of Ra
Table7. Optimum parameter settings
Factor Level Value
Speed (A) A3 35m/min
Feed(B) B1 0.08mm/rev
DOC(C) C1 0.15mm
NR(D) D3 1.2mm
SP EED
10
5
0
FEED
NR
1.20.80.4
0.120.100.08
10
5
0
353025
10
5
0
SPEED
35
25
30
FEED
0.12
0.08
0.10
NR
1.2
0.4
0.8
Interaction Plot (data means) for SN ratios(Ra)
S ignal-to-noise: Smaller is better
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7.4. CONFIRMATION EXPERIMENT.
Confirmation experiment was conducted with the optimal parameter settings by taking four
trials and the results are shown in Table 8.The mean Ra value=0.286 µm
Table 8.Results of Confirmation Experiment
Factor Level Value Trial
No. Ra(µm)
Speed (A) A3 35m/min 1 0.286
Feed(B) B1 0.08mm/rev 2 0.285
DOC(C) C1 0.15mm 3 0.289
NR(D) D3 1.2mm 4 0.285
mean 0.286
When factor values are substituted in the mathematical model equation 6, it has given Ra
value =0.29 µm. The difference in surface roughness values observed is only 0.004 µm which is
negligibly small and hence the model is validated. Ra value of 0.286 µm is lower than the lowest
measured surface roughness value observed in table 3indicating optimum factor level A3B1C1D3 is
more or less satisfied
7.5. Optimization of Ra
The main effects of each parameter on Ra, are plotted on graphs shown in figure 4 for mean
values of S/N ratios for each level of control variables. These figures clearly indicates how speed,
feed, DOC and tool nose radius changes and affects the modified parameter S/N ratio. Figure shows
that, with the increase in speed, the S/N ratio initially decreases by a small value and subsequently it
increases leading to a resultant increase of S/N ratio and decrease in Ra value. With the increase in
feed, S/N ratio decreases implying an increase in roughness value. With the increase in depth of cut,
there is a resultant decrease in S/N ratio and increase in Ra value. With the increase in nose radius,
there is a resultant increase in S/N ratio and a reduction in surface roughness. Greater the value of
S/N ratio for each parameter minimizes Ra. So the optimum conditions for achieving minimum
surface roughness is cutting speed 35m/min(A3),feed 0.08mm/rev (B1), DOC 0.15mm(C1) and nose
radius 1.2mm(D3). This implies that maximum cutting speed of 35m/min, minimum feed rate of
0.08mm/rev, minimum depth of cut of 0.15mm and maximum nose radius of 1.2mm optimizes the
response.
The response table for the average value of S/N ratios for each level of parameters is
displayed in table 4 and this is utilized to find out their relative importance and to rank them based
on the differences in the average values. It is found that feed is the most important parameter that
influences Ra followed by NR and interaction between feed and nose radius. DOC plays the next
important role where as speed is having least effect on Ra value.
Analysis of Variance (ANOVA) has been performed to investigate the statistical significance
of parameters at 95% confidence level and to determine the percentage of contribution of parameters
to the process response The significance of each parameter was tested using probability values (p-
value).When the p-value in the ANOVA table for S/N ratios is less than 0.05for a confidence level of
95%, it is considered as statistically significant. In addition, the percentage of contribution expresses
the importance of the parameters for the response.
From the result of ANOVA shown in table 5, it is found that the most significant parameter is
feed and its contribution is (57.98%) followed by NR with a contribution of (36.05%). Interaction
between Feed and Nose radius is depicted in fig 5 and is having a small contribution of
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(2.32%).Other factors are not having significant effects since ‘p’ values are more than 0.05 for a
confidence level of 95%.
To establish a mathematical relationship between parameters and responses, the linear
regression analyses were performed and the following equations were developed.
Ra = 0.0194 - 0.00058 SPEED + 8.62 FEED + 0.0753 DOC - 0.344 NR+ 0.0104 SPEED*FEED -
0.000508 SPEED*NR - 0.141 FEED*NR
The significance of each coefficient in the equation and the regression model were analyzed
by ANOVA and tested by the probability (p value). Both the regression statistics and ANOVA
results for regression models are reported in table 6. Regression statistics indicate that coefficients
for feed and nose radius are statistically significant. ANOVA results of the regression shows that
regression model for Ra is statistically significant at 95% confidence level (p < 0.05) and at least one
of the regressor variables (control factors) is significantly related to response Ra. The predicted
values of the responses were compared with the measured values and the result is depicted as a graph
in fig.6.The graph shows that the predicted values are in reasonable agreement with measured values.
The value of R2
(97.54%) implies that 97.54% of variation in response values can be explained by
the variations in the control factors considered. A high value of determination coefficient ensures
model adequacy, goodness of fit and high significance of model. This indicates that the regression
model for the response can be used for determining and estimating Ra surface roughness value.
8. CONCLUSIONS
An investigation has been carried out to assess the effect of control parameters on the surface
roughness value in the turning process of INCONEL718 using uncoated carbide inserts. The
experiments were performed based on L27 Orthogonal array applying Taguchi’s technique. The input
parameters were cutting speed, feed, depth of cut and tool nose radius and the performance
characteristic investigated is Surface Roughness. The ANOVA was performed to evaluate the
statistical significance of each parameter on the performance characteristic. The relation between
input and output parameter is modeled using Multiple Regression Analysis for the estimation of Ra.
Mathematical model obtained by regression analysis and the equation (6) is reported in the main text.
Summary of the experimental results are tabulated and shown in table7 and results of confirmation
experiment are shown in table8.
Based on the results of theoretical analysis following conclusion are drawn
1) It is observed that the most significant parameter which affects surface roughness is feed and its
contribution is (57.98%) followed by NR with a contribution of (36.05%).Interaction between
feed and nose radius is having a contribution of 2.32% and other factors are not having
significant effects at a confidence level of 95%.
2) Linear regression model constructed using MINITAB software is used to predict the Surface
roughness Ra.
3) A comparison has been made between measured values and predicted values and it is shown in
fig.6.
4) Confirmation experiment using optimized parameter settings reported a surface roughness value
of 0.286 µm which is in accordance with the roughness value predicted by the mathematical
model of MRA within the acceptable range of errors; hence it validates the mathematical model.
5) Eventually it is concluded that Taguchi method is suitable for parametric optimization of turning
and MRA can predict responses reliably.
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