 Open Access
 Total Downloads : 924
 Authors : Kishan Dhameliya, Jignesh Desai, Mohit Gandhi, Divyesh Dave
 Paper ID : IJERTV3IS051181
 Volume & Issue : Volume 03, Issue 05 (May 2014)
 Published (First Online): 22052014
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Experimental investigation of process parameters on MRR and Surface roughness in turning operation on CNC Lathe machine for Mild Steel E250: IS 2062
Kishan Dhameliya1, Jignesh Desai1, Mohit Gandhi1, Divyesh Dave1*
1. Dr. jivraj Mehta a institute of technology, Mogar 388340, Anand, GujaratIndia
Abstract – The performance of the manufactured products are often evaluated by several quality characteristics, responses and experimental techniques. In the present project a single characteristic response optimization model based on Taguchi Technique is developed to optimize process parameters such as spindle speed, feed and depth of cut. Taguchis L9 orthogonal array is selected for experimental planning. The Analysis of experimental result showed that the combination of optimum levels of cutting speed, feed and depth of cut is essential to achieve simultaneous maximization of material removal rate and minimization of surface roughness. This project also aims to determine Analysis of Variance.
Keywords – Optimization, MRR, Surface roughness, turning operation, CNC Lathe machine, E250: IS 2062

INTRODUCTION
In rivalry industry, each one assembling organization needs to fabricate ease and astounding item in a brief time to full fill client request. Robotized and adaptable assembling frameworks are utilized for that reason alongside computerized numerical control (CNC) machines that are fit for attaining high correctness and low preparing time. Cutting parameters are thought about surface Roughness, material removal rate (MRR), surface composition, and dimensional deviations of the item.( Behzad Jabbaripour et.al.)

Turning Operation
Turning is the removal of metal from the outer diameter of a rotating cylindrical work piece. Turning is used to decrease the diameter of the work piece, generally to a particular dimension, and to generate a smooth finish on the metal. Often the work piece will be turned so the adjacent sections have totally different diameters. In its basic type, it defined as the machining of an external surface:

With the work piece rotating.

With a singlepoint cutting tool, and

With the cutting implement feeding parallel to the axis of the work piece and at a distance which will take away the outer surface of the work. (Mohd Naiim B Yusoff et.al)


Adjustable Cutting Factors in Turning
The primary factors of turning operation are speed, feed, and depth of cut. Other factors like types of material and type of tool have a large influence, of course, but these three can be alter by adjusting the controls.( Figure ure 1)
Figure 1: Adjustable parameters in turning operation

Speed:
Speed continually refers to the spindle and the work piece. Once it's explicit in revolutions per minute (rpm) it tells their rotating speed. However the necessary feature for a selected turning operation is that the surface speed, or the speed at that the work piece material is moving past the cutting implement.
v = (*D*N) / 1000 m/min
Here, v is the cutting speed in turning,
D is the initial diameter of the work piece in mm, N is the spindle speed in RPM.
Feed:
Feed continuously refers to the cutter, and it's the speed at that the tool advances on its cutting path. On most powerfed lathes, the feed rate is directly associated with the spindle speed and is expressed in metric linear unit (of tool advance) per revolution (of the spindle), or mm/rev.
Fm = f * N mm / min.
Here, Fm is the feed in mm per minute, f is the feed in mm/rev,
N is the spindle speed in RPM.

Depth of Cut:
Depth of cut is much self instructive . it's the thickness of the layer being removed (in one pass) from the work piece or the gap from the uncut surface of the work to the cut surface, expressed in , mm.
dcut = ( D d ) / 2 mm
Here, D is the initial diameter (in mm) of the job, d is the final diameter (in mm) of the job.


Material removal rate (MRR)
The material removal rate (MRR) in turning operations is the volume of material/metal that is removed per unit time in mm3/min. For each revolution of the work piece, a ring shaped layer of material is removed.
MRR = { * ( D2 d2 ) * l } / { t * 4 } mm3 / min Here, D is the initial diameter in mm,
d is the final diameter in mm, l is the length in mm,
t is the time taken for machining in min.

surface roughness
Surface roughness, frequently reduced to roughness ad it can be measure by the texture of a surface. It is quantified by the vertical deviations of a real surface from its ideal form. If hese deviations are large, the surface is rough; if they are small the surface is smooth


METHODOLGY

Taguchi's Approach to Parameter Design

Determine the Quality Characteristic to be Optimized.
Taguchi's approach to parameter design provides the design engineer with a systematic and efficient method for determining near optimum design parameters for performance and cost (Kackar, 1985; Phadke, 1989; Taguchi 1986). The objective is to select the best combination of control parameters so that the product or process is most robust with respect to noise factors. A brief overview of Taguchi's approach for parameter design. (Figure 2) (Hemant Kumar Agarwal et. al.)

Identify the Noise Factors and Test Condition.
1
2

Identify the Control Factors and their alternative levels.
3

Design the Matrix Experiment and Define the Data Analysis Procedure.
4

Conduct the Matrix Experiment.
5

Analyze the data and determine optimum level of Control Factors.

Predect the performance at these level.
6
7
Figure: 2 Overview of Taguchi approach for parameter design

Determine the Quality Characteristic and to be optimized
The first step in the Taguchi method is to determine the quality characteristic to be optimized. The quality characteristic is a parameter whose variation has a critical effect on product quality. It is the output or the response variable to be observed. Examples are weight, cost, corrosion, target thickness, strength of a structure, and electromagnetic radiation.

Identify the Noise Factors and Test Conditions
To identify the noise factors to produce a negative impact on system performance and quality. Noise factors is a parameter which may either uncontrollable or are too expensive to control. Noise factors consist of variations in deterioration of components with usage, environmental operating conditions and variation in response between products of similar design with the similar input.

Identify the Control Parameters and Their Alternative Levels
To identify the control parameters thought to have significant effects on the quality characteristic. Control (test) parameters are those design factors that can be set and maintained. The levels (test values) for each test parameter must be chosen at this point. The numbers of levels, with related test values, for each test parameter was defining the experimental region.

Design the Matrix Experiment and Define the Data Analysis Procedure
A specific study is required to get proper orthogonal arrays for the noise and control. Taguchi gives many standard orthogonal arrays and corresponding linear graphs for this function (Taguch and Konishi, 1987).Subsequent to selecting the appropriate orthogonal arrays, a process to simulate the variation in the quality characteristic due to the noise factors wants to be defined. A general approach is the use of Monte Carlo simulation (Phadke, 1989). However, for an precise estimation of the mean and variance, Monte Carlo simulation requires a huge number of testing conditions which can be expensive and time consuming. As an substitute, Taguchi proposes orthogonal array based simulation to assess the mean and the variance of a product's response ensuing from variations in noise factors (Bryne and Taguchi, 1986; Phadke, 1989; Taguchi, 1986). Using this approach, orthogonal arrays are used to sample the domain of noise factors. The experimental results for each combination of control and noise array experiment are denoted by Y i,j

Conduct the Matrix Experiment
The Taguchi method may be used in any condition wherever there is a controllable process (Meisl, 1990; Phadke, 1989; Wille, 1990). The controllable process can be an real hardware experiment, computer models or systems of mathematical equations that can be effectively model for the response of many products and processes.

Analyze the Data and Determine the Optimum Levels
The optimal test parameter pattern within the experiment design must be determined, after the experiments have been conducted. For analysis of the results, the Taguchi method uses a statistical measure of performance called signal to noise (S/N) ratio borrowed from electrical control theory (Phadke, 1989). The S/N ratio produced by Dr. Taguchi is a performance measure to select control levels that most excellent cope with noise (Bryne and Taguchi, 1986; Phadke, 1989). The S/N ratio takes both the mean and the variability into relation. In easiest form, the S/N ratio is the ratio of the mean (signal) to the standard deviation (noise). The S/N equation depends on the principle for the quality characteristic to be optimized. While there are many diverse probable S/N ratios, three of them are considered standard and are usually applicable in the situations below (Bryne and Taguchi, 1986; Phadke, 1989); a) Biggestisbest quality characteristic (strength, yield), b) Smallestisbest quality characteristic (contamination), c) Nominalisbest quality characteristic (dimension). Whatever the type of quality or cost feature, the transformations are such that the S/N ratio is for all time interpreted in the same way: the larger the S/N ratio the better.

Predict the Performance at These Levels
With the help of Taguchi method for parameter designing, the predicted optimum setting are not required correspond to one of the rows of the matrix experiment. This is frequently the case when vastly fractioned designs are used (Bryne and Taguchi, 1986; Phadke, 1989). So, the final of step of an experimental confirmation is run using the predicted optimum levels for the control parameters being studied.



ANOVA (ANALYSIS OF VARIANCE)
Analysis of Variance (ANOVA) is a hypothesistesting method used to analysis the equality of two or more population (or treatment) mean by examining the variances of samples which are taken. ANOVA permits to determine whether the differences between the samples are only due to random error or if there are systematic treatment effects which make the mean in one group to differ from the mean in another. Mainly ANOVA is used to compare the parity of three or more means, but when the means from two samples are compared using ANOVA it is similar to using a ttest to compare the means of independent samples. ANOVA is comparing the variance (or variation) between the data samples to variation within each particular sample. Whenever the between variation is much larger than the within variation, the means of different samples will not be equal. If samples will not be equivalent. If the between and within variations are approximately the equal size, then there will be no significant difference among sample means.


EXPERIMENTTION
Accordingly the present study has been done through the following plan of experiment. a) Checking and preparing the CNC Lathe Machine ready for performing the machining operation. b) Cutting MS bars by power saw and performing initial turning operation in Lathe to get desired dimension of the work pieces. c) Calculating weight of each specimen by the high precision digital weighing machine before
machining. d) Performing straight turning operation on specimens in various cutting environments involving various combinations of process control parameters like: spindle speed, feed and depth of cut. e) Calculating weight of each machined component again by the digital weighing machine.
f) Measuring surface roughness and with the help of a Roughness Tester Mitutoyo make.

Process variables and their limits
The process variables with their units for roughing operation and finishing operation are listed in Table 1 and 2 respectively.
Table 1: Level of process parameters for roughing operation
Parameter
Level 1
Level 2
Level 3
Spindle Speed (RPM)
800
1000
1200
Feed Rate (mm/ revolution)
0.15
0.18
0.2
Depth of cut (mm)
0.5
0.6
0.7
Table 2: Level of process parameters for finishing operation
Parameter
Level 1
Level 2
Level 3
Spindle Speed (RPM)
800
1000
1200
Feed Rate (mm/ revolution)
0.10
0.12
0.14
Depth of cut (mm)
0.3
0.4
0.5

Design of experiment
Experiments have been carried out using Taguchis L9 Orthogonal Array (OA) experimental design which consists of 9 combinations of spindle speed, longitudinal feed rate and depth of cut. On the basis of design catalogue prepared by Taguchi, L9 Orthogonal Array design of experiment has been found suitable in the present work. It considers three process parameters (without interaction) to be varied in three discrete levels. The experimental design has been shown in Table 3 (all factors are in coded form).
Table 3 TAGUCHIS L9 orthogonal array
No.
Spindle Speed (rpm)
Feed
Depth of Cut
(mm/rev.)
(mm)
1
1
1
1
2
1
2
2
3
1
3
3
4
2
1
2
5
2
2
3
6
2
3
1
7
3
1
3
8
3
2
1
9
3
3
2
Sr. Factorial Combination

Test Condition
Turning operation has been carried out on CNC turning machine (Batliboi Sprint20TC) using cutting insert Sandvik CNMG 12 04 08PR 4225 for roughing operation and
Sandvik TNMG 16 04 08MM 2015 for finishing operation on material E250 : IS 2062 pipe at different levels of process parameters Spindle Speed, Feed and Depth of cut give by L9 orthogonal array. Weight before and after operation has been measred by digital weighing scale and Surface roughness value (Ra) measured by Portable surface roughness tester (Mitutoyo) SJ201P. (Figure ure 3)
Figure: 3 Cnc Lathe Machine

Roughness measurement
Surface Roughness of the finished component were measured by Portable surface roughness tester (Mitutoyo) SJ201P at Elecon Engineering Co. Ltd.

Material removal rate measurement
Material removal rate (MRR) has been calculated from the difference of weight of work piece before and after experiment.
MRR = ( Wi – Wf ) / ( s * t ) mm3 / min
Here W is the initial weight of work piece in g, Wf is the final weight of work piece in g, t is the machining time in minutes,
s is the density of mild steel (7.8 x 103g/mm3).

Data collection
MS pipe (84 * 62 * 150) required for conducting the experiment have been prepared first. Nine numbers of samples of same material and same dimensions have been made. After that, the weight of each samples have been measured accurately with the help of a high precision digital balance meter. Then, using different levels of the process parameters nine specimens have been turned in lathe accordingly. Machining time for each sample has been
calculated accordingly. After machining, weight of each machined parts have been again measured precisely with the help of the digital weighing machine. Then surface roughness and surface profile have been measured precisely with the help of a portable surface roughness tester.


RESULTS AND DISCUSSION

Experiment results and Taguchi analysis
A series of turning tests was conducted to assess the influence of turning parameters on material removal rate and surface roughness in turning E250. Experimental results of the material removal rate and surface roughness for turning with various turning parameters are shown in Table 4 and Table 5 respectively. A table also gives S/N ratio for material removal rate and surface roughness. The S/N ratio for each experiment of L9 was calculated. The objective of using the S/N ratio as a performance measurement is to develop products and process insensitive to noise factor.

Determination of optimal value

Material removal rate (MRR)
In response factors, the material removal rate (MRR), the largerthebetter characteristic was used and the largest resultant cutting force value would be the ideal situation. The graphs in Figure 4 & 5 are used to determine the optimal set of parameters from this experimental design. From the graphs, the control factor of spindle speed (A) at level 3 (1200 rpm) showed the best result. Besides that, the feed control factor (B) provided the best result at the level 3 (0.2 mm/rev). On the other hand, the depth of cut control factor
(C) showed the best results at the level 3 (0.7 mm). There were also no conflicts happening in determining the optimal spindle speed, feed rate and depth of cut while the criteria of the largest response and highest S/N ration were followed. (Table 6) Thus,
Cutting Speed (A) at level 3 (1200 rpm) Feed (B) at level 3 (0.2 mm/rev)
Depth of cut (C) at level 3 (0.7 mm)
Table 4 Experimental result for MRR
Exp No.
Spindle Speed
(rpm)
FEED
(mm/re
v) (B)
DOC
(mm)
(C)
Weight Before
Operatio
Weight After
Operatio
Wei ght
Diff.
Time(sec)
volume(mm3)
MRR(mm3/min)
(A)
n (gm)
n(gm)
(gm)
1
800
0.15
0.5
2839
2784
55
33.05
6997.455471
12703.39874
2
800
0.18
0.6
2782
2716
66
28
8396.946565
17993.45692
3
800
0.2
0.7
2776
2699
77
25.55
9796.437659
23005.33305
4
1000
0.15
0.6
2772
2706
66
27.15
8396.946565
18556.78799
5
1000
0.18
0.7
2820
2743
77
23.45
9796.437659
25065.51213
6
1000
0.2
0.5
2810
2755
55
21
6997.455471
19992.72992
7
1200
0.15
0.7
2901
2824
77
22.95
9796.437659
25611.60172
8
1200
0.18
0.5
2920
2865
55
19.42
6997.455471
21619.32689
9
1200
0.2
0.6
2935
2869
66
17.45
8396.946565
28872.02257
Table 5 Experimental result for Ra
Exp.
Spindle Speed (rpm)
Feed (mm/rev.)
Depth of Cut (mm)
Ra (m)
No.
1
800
0.1
0.3
0.53
2
800
0.12
0.4
0.67
3
800
0.14
0.5
0.29
4
1000
0.1
0.4
0.80
5
1000
0.12
0.5
0.77
6
1000
0.14
0.3
0.25
7
1200
0.1
0.5
1.50
8
1200
0.12
0.3
1.20
9
1200
0.14
0.4
1.25
Exp.
Spindle Speed (rpm)
Feed (mm/rev)
Depth of cut (mm)
MRR(mm3/min)
SNMRR
No.
1
800
0.15
0.5
12703.39874
82.0784
2
800
0.18
0.6
17993.45692
85.10229
3
800
0.2
0.7
23005.33305
87.23657
4
1000
0.15
0.6
18556.78799
85.37006
5
1000
0.18
0.7
25065.51213
87.98153
6
1000
0.2
0.5
19992.72992
86.01744
7
1200
0.15
0.7
25611.60172
88.16873
8
1200
0.18
0.5
21619.32689
86.69684
9
1200
0.2
0.6
28872.02257
89.20954
0.20
0.18
0.15
1200
800 1000
26000
24000
22000
20000
18000
Feed (mm/rev)
Spindle Speed (rpm)
0.20
0.18
0.15
1200
1000
Dept h of cut (mm)
800
Mean of SN ratios
Mean of Means
Table 6 S/N ratio for material removal rate (MRR)
88
87
86
85
Main Effects Plot for SN ratios (MRR)
Data Means
Spindle Speed (rpm) Feed (mm/rev)
Main Effects Plot for Means (MRR)
Data Means
Signaltonoise: Larger is better
0.5
0.6
0.7
26000
24000
22000
20000
18000
Depth of cut (mm)
0.7
0.6
0.5
88
87
86
85
Figure 4 Main effects plot for SN ratios (MRR) Figure 5 Main effects plot for means (MRR)

Surface roughness (Ra):
Thesmallerthebetter characteristic was used to determine the smallest surface roughness (Ra ) that would be the ideal situation for this study. Meanwhile, the larger S/N ration would be projected as the best response given in the machine setup system which would be the ideal situation. The graphs in Figure 6 & 7 are used to determine the optimal set of parameters form this experimental design. Form the graphs, the control factor of Spindle Speed (A) at level 1 (800 rpm) show the best result. On the other hand, the feed control factor (B) provides the best result at the
level 3 (0.14 mm/rev). Meanwhile, the depth of cut control factor (C) gives the best results at the level 1 (0.3 mm). There are no conflicts in determining the optimal depth of cut, spindle speed and feed rate and the criteria of the lowest response and highest S/N ration were followed (Table 7). Thus, the optimized combination of levels for all the three control factors from the analysis which provides the best surface finish was found to be
Cutting Speed (A) at level 1 (800 rpm) Feed (B) at level 3 (0.14 mm/rev) Depth of cut (C) at level 1 (0.3 mm)
Table 7 S/N ratio for surface roughness (RA)
Exp. No. Spindle Speed (rpm) Feed (mm/rev.) Depth of Cut (mm) Ra SNRa
1 800 0.10 0.3 0.53 5.5145
2 800 0.12 0.4 0.67 3.4785
3 800 0.14 0.5 0.29 10.7520
4 1000 0.10 0.4 0.80 1.9382
5 1000 0.12 0.5 0.77 2.2702
6 1000 0.14 0.3 0.25 12.0412
7 1200 0.10 0.5 1.50 3.5218
8 1200 0.12 0.3 1.20 1.5836
0.14
0.12
0.10
1000 1200
Dept h of cut (mm)
800
0.14
0.12
0.10
1000 1200
Depth of cut (mm)
800
Mean of SN ratios
Mean of Means
9 1200 0.14 0.4 1.25 1.9382
Main Effects Plot for SN ratios (Ra)
Data Means
Main Effects Plot for Means (Ra)
Data Means
6
4
2
0
2
Spindle Speed (rpm)
Feed Rate (mm/rev)
1.4
1.2
1.0
0.8
0.6
Spindle Speed (rpm)
Feed Rat e (mm/rev)
0.3
0.4
0.5
Signaltonoise: Smaller is better
0.3
0.4
0.5
1.4
1.2
1.0
0.8
0.6
6
4
2
0
2
Figure 6 Main effects plot for SN ratio (surface roughness) Figure 7 Main effects plot for means (surface roughness)
B. ANALYSIS OF VARIANCE (ANOVA)
1) Material removal rate
Table 8 and Figure 8 shows the percent effect of each of each parameter on the Material Removal Rate. It is illustrated that Spindle Speed has the most significant effect on the output response (MRR). Other significant parameters
are, in turn, depth of cut and feed. Graphical representation of the contribution of each parameter can be easily understood by Pie Chart. Table 9 and Figure 9 shows the percent effect of each of each parameter on the Surface Roughness. It is illustrated that Spindle Speed has the most significant effect on the output response (Ra). Other significant parameters are, in turn, feed, depth of cut.
Graphical representation of the contribution of each parameter can be easily understood by Pie Chart.
D. Validation
Validation of result is an important part of project work. We have predicted Performance characteristics by two different methods as Prediction by Taguchi Method and Prediction by Regression Analysis. Three confirmations experiments also done to verify the predicted values of Performance characteristics and compared it with predicted value.

For Material Removal Rate (MRR)

Prediction by Taguchi Method
For the optimal condition of process parameters Cutting Speed (A) at level 3 (1200 rpm) Feed (B) at level 3 (0.2 mm/rev) and Depth of cut (C) at level 3 (0.7 mm) value of MRR can be predicted by following equation as
MRR333 = 3 + B 3 + C 3 – (2 * T)
Here 3 is average of mean at level 3 of MRR
B 3 is average of mean at level 3 of MRR
C3 is average of mean at level 3 of MRR
T is average of mean of MRR for all 9 experiments. so, MRR333 = 25368 + 23957 + 24561 – (2 * 21491.13)
= 30903 mm3/min

Prediction by Regression Analysis
For the optimal condition of process parameters Cutting Speed (A) at level 3 (1200 rpm) Feed (B) at level 3 (0.2 mm/rev) and Depth of cut (C) at level 3 (0.7 mm) value of MRR can be predicted by following regression equation as :
MRR333 = – 34023 + 18.7 Spindle Speed (rpm) + 98943 Feed (mm/rev) + 32278 Depth of cut (mm)
= – 34023 + 18.7 * 1200 + 98943 * 0.20 + 32278
* 0.7
= 30800.2 mm3/min

Comparison of results
Table 11 shows value of Material Removal Rate (MRR) of optimal combination of process parameters by different methods. Value of MRR from the confirmation test is differing by less than 2 % of the prediction by Taguchi Method. Also Figure 10 shows a graphical representation of comparison by all three methods
Table 8: ANOVA for material removal rate (MRR)
Source
DF
SS
MS
F
P
% Contribution
Spindle Speed (rpm)
2
84000751
42000375
35.32
0.028
44.9550894
Feed (mm/rev)
2
37512479
18756240
15.77
0.06
20.0757354
Depth of cut (mm)
2
62963577
31481788
26.48
0.036
33.696523
Error
2
2378012
1189006
Total
8
186854819
Table 9: ANOVA for Surface Roughness (Ra)
Source
DF
SS
MS
F
P
% Contribution
Spindle Speed (rpm)
2
1.18860
0.59430
105.50
0.009
78.95575927
Feed (mm/rev
2
0.20447
0.10223
18.15
0.052
13.58243656
Depth of cut (mm)
2
0.10107
0.05053
8.97
0.100
6.713830211
Error
2
0.01127
0.00563
Total
8
1.50540
20%
Depth of cut (mm)
Error
Error
Feed (mm/rev)
79% Depth of cut (mm)
13%
Significance of Process Parameters for Ra
Spindle Speed (rpm)
7% 1%
Feed (mm/rev)
45%
34%
Significance of Process Parameters for MRR
Spindle Speed (rpm)
1%
Figure 8: Significance of process parameters for MRR Figure 9: Significance of process parameters for Ra

Confirmation Experiment Result
Table 10: Result of confirmation experiment (MRR)
Exp. No.
Spindle Speed
(rpm) (A)
FEED
(mm/rev)( B)
DOC
(mm)
(C)
Weight Before
Operation
Weight After
Operation
Weight Diff.(gm)
Time (sec)
Volume (mm3)
MRR
(mm3/min)
(gm)
(gm)
1
1200
0.2
0.7
2930
2806
124
31.17
15770
30350.2
2
1200
0.2
0.7
2772
2653
119
29.8
15139
30480.6
3
1200
0.2
0.7
2831
2704
127
31.87
16457
30410.3
31000
30903
30800.2
30413.7
30900
30800
30700
30600
30500
30400
30300
30200
30100
Comparison of results for MRR
Speed (A) at level 1 (800 rpm) Feed (B) at level 3 (0.14 mm/rev) Depth of cut (C) at level 1 (0.3 mm) value of Ra can be predicted by following regression equation as :
Ra131 = 0.59 + 0.00205 Spindle Speed (rpm) – 8.66667 Feed Rate (mm/rev) +0.966667 Depth of cut (mm)
= 0.59 + (0.00205 * 800) – (8.66667 * 0.14) +
(0.966667 * 0.3)
= 0.1726 m

Confirmation Experiment Result
Table 12: result of confirmation experiment (Ra)
Exp. No. Ra (m)
1 0.15
Prediction by Taguchi Method
Prediction by Regression Analysis
Series1
Confirmation Experiment Result
2 0.16
3 0.15
Avg. 0.1533

Comparison of results



Table 13 shows value of Surface Roughness (Ra) of
Figure 10: Comparison of results for MRR
Table 11: Comparison of results (MRR)
Method MRR(mm3/min)
Prediction by Taguchi Method 30903
optimal combination of process parameters by different methods. Value of Ra from the confirmation test is differing by less than 10 % of the prediction by Taguchi Method. Also Figure.11 shows a graphical representation of comparison by all three methods.
Table 13: comparison of results (Ra)
Prediction by Regression Analysis 30800.2
Method Ra(m)
Confirmation Experiment Result 30413.7



For Surface Roughness (Ra)
Prediction by Taguchi Method 0.1402
Prediction by Regression Analysis 0.1726
Confirmation Experiment Result 0.1533

Prediction by Taguchi Method For the optimal condition of process parameters Cutting
Speed (A) at level 1 (800 rpm) Feed (B) at level 3 (0.14
mm/rev) Depth of cut (C) at level 1 (0.3 mm) value of Ra can be predicted by following equation as :
Ra131 = 1 + 3 + 1 – ( 2 * T )
Here 1 is average of mean at level 1 of Ra
0.1726
0.1402
0.1533
3 is average of mean at level 3 of Ra
0.2
0.15
0.1
0.05
0
Comparison of results for Ra
1 is average of mean at level 1 of Ra _
is average of mean of Ra for all 9 experiments.
Ra131= 0.4967+0.5967 + 0.6600 – ( 2 * 0.8066 )
= 0.1402

Prediction by Regression Analysis

Prediction by Taguchi Method
Prediction by Regression Analysis Series1
Confirmation Experiment Result
For the optimal condition of process parameters cutting
Figure 11: Comparison of results for Ra


CONCLUSION:
The present project work was carried out to study the effect of process parameter such as spindle speed, feed and depth of cut on the performance parameter such as material removal rate and surface roughness. The following conclusion has been drawn from the study. Material removal rate is mainly affected by spindle speed (45%)and depth of cut (34%) while surface roughness is mainly affected by spindle speed (79%).The least significant parameter for material removal rate is feed (20%) and for surface roughness is depth of cut(7%). Linear regression model and Taguchi mean estimation method is used to predict material removal rate and surface roughness. The process parameters considered in the experiments are optimized to attain maximum material removal rate. The best combination of process parameters for turning within the selected range is as follow: Cutting Speed 1200 rpm, Feed 0.2 mm/rev, Depth of cut 0.7 mm. The process parameters considered in the experiments are optimized to attain minimum Surface Roughness (Ra). The best combination of process parameters for turning within the selected range is Cutting Speed 800 rpm, Feed 0.14 mm/rev Depth of cut 0.3 mm.

FUTURE SCOPE
It can be also done for L27 orthogonal array. We can also do for more process parameters such as nose radius and rake angle of cutting tool which we have taken constant. On economical aspect of view it can be also done for it.

ACKNOWLEDGEMENT
All authors are thankful to our trust CENT & institute DJMIT for providing excellent research environment. Our special thanks to MR. Mihir Patel for his guidance and support and also to our external guide MR. N. J. Dhameliya (Shiv Engineering Works, Anand) who helped us for production on CNC machine. We are gratified towards ELECON Engineering Co. Ltd. for valuable support.

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