 Open Access
 Authors : C N Vishnu Vandana , Dr. B Chandra Mohan Reddy , Dr. M Devaki Devi
 Paper ID : IJERTV8IS100247
 Volume & Issue : Volume 08, Issue 10 (October 2019)
 Published (First Online): 30102019
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Statistical Approach for Behaviour of Cutting Parameters of Turning Operation
C N Vishnu Vandana1 , Dr. B Chandra Mohan Reddy2, Dr. M Devaki Devi3
HOD
HOD
1PG Student in ME, 2 and Professor in ME, 3Assistant Professor in ME
1,2 JNTUA, Anantapur, India, 3 GPREC, India
Abstract To produce any desired product, machining is one of the most important tasks. Through manipulating its system parameters, the difficulty of obtaining desired product can be accomplished. It can provide an effective method for determining optimal measuring parameters. The present examination applied Response Surface Methodology through a test study in straight turning of free cutting metal bar. The examination planned for assessing the best procedure condition which could at the same time fulfill prerequisites of both quality and just as profitability. At long last, the Optimization should be possible by many upgrading methods which pursues ANOVA to think about variety of advanced parameters. The information parameters considered in this work are cutting rate, feed, depth of cut and work material.
Keywords ANOVA, Cutting Parameters, Free Cutting Brass, Machining, optimisation, surface roughness, Turning.

INTRODUCTION
Machining has been a tested by the new age of materials which are regularly hard to machine. Careful comprehension of slicing mechanics can grow new techniques, devices and machining forms. Copper composite with beryllium has wide range applications because of its adaptable properties keeping pace with steels, with the extra properties of nonstarting conductivity, under electric and attractive field impact and stylish look [1]. Turning is the expulsion of metal from the external distance across of a pivoting round and hollow work piece. Turning is utilized to diminish the distance across of the work piece, for the most part to a predefined measurement, and to deliver a smooth completion on the metal. Regularly the work piece will be turned with the goal that contiguous segments have various distances across [2].
Surface hardness has gotten genuine consideration for a long time. It has planned a significant structure highlight by and large, for example, parts subject to weariness loads, accuracy fits, latch gaps and necessities. Notwithstanding resiliences, surface unpleasantness forces one of the principle basic requirements for the determination of machines and cutting parameters in procedure arranging. Surface completion is the strategy for estimating the nature of an item and significant parameter in machining process. It is one of the prime prerequisites of clients for machine parts [3].
Response Surface Methodology (RSM) is an accumulation of measurable and scientific strategies valuable for creating, improving, and enhancing forms. It likewise has significant applications in the plan, advancement, and definition of new items, just as in the improvement of existing item structures.

LITERATURE REVIEW
K Devaki Devi.et.al [1] The present study investigates the effect of cutting parameters cutting speed, feed rate, depth of cut, and heat treatment of work material (Be cu alloy) in turning process using uncoated CBN cutting tool. Four outputs of the machining, along which heat treatment is categoric, studied. The outputs are cutting force and cutting tool temperature. Neural Network based Genetic Algorithm approach is used to study the performance characteristics and to find out the optimal cutting parameters of the turning process for heat treated Becu alloy. Experimental results prove the effectiveness of this approach. In this study, the main cutting parameters that affect the cutting performance in turning operations and the best combination are determined.
K Devaki Devi et.al [2] The present paper invites optimization problem which seeks identification of the best process condition or parametric combination for the said manufacturing process. The study aimed at evaluating the best process environment which could simultaneously satisfy requirements of both quality and as well as productivity. Finally, the effect of four input variables namely cutting speed, feed, depth of cut and type of coolant on different output parameters is studied in the study.

Naga Lakshmi et.al [3] This research paper is focused on the analysis of optimum cutting conditions to get lowest surface roughness in turning by regression analysis. An experimental study was carried out to investigate the effect of cutting parameters like spindle speed, feed and depth of cut on surface finish in turning on Aluminum 7075 alloy. A multiple regression analysis (Ra) using analysis of variance is conducted to determine the performance of experimental measurements and to it show the effect of cutting parameters on the surface roughness. Multiple regression modeling was performed to predict the surface roughness by using machining parameters. Machining was done by using tungsten carbide tool. The objective was to establish correlation between cutting speed, feed rate and depth of cut and optimum the turning conditions based on surface roughness. These correlations are obtained by multiple regression analysis (RA).


MATERIAL AND METHODOLOGY
The free cutting metal with the assignment CuZn39Pb3 has built up Itself as an astounding material for assembling a wide range of structure turned parts. The amazing machining properties of these copperzinc amalgams is so outstanding that they are regularly utilized as benchmarks for portraying the machining properties of copper and copper composites. Composition of free cutting brass as shown in table 1 below.
TABLE I. CHEMICAL COMPOSTION OF FREE CUTTING
BRASS
Element
Pb
Cu
Fe
Zn
Weight%
2.53.5
56.558.5
0.3
Rem
Typical Physical Properties:
TABLE II. PHYSICAL PROPERTIES OF FREE CUTTING BRASS
Melting Point
890Â°C
Density
8.4 g/cmÂ³
Specific Heat
380 J/KgÂ°K
Thermal conductivity (RT)
121 W/mÂ°K
Thermal expansion coefficient (20200Â°C)
20.9 x 106
Electrical conductivity
28% IACS
Electrical Resistivity
0.062ohm mm2/m
Optimal Design in CCD:
The design plan with high and low limits as indicated is utilized looking into practical considerations of the Turning operation as in the Table:
Input Factors and Levels values in coded form
Spindle Speed
(S) (RPM)
Feed (f) (mm/rev)
Depth of cut (d) (mm)
1
200
0.105
0.1
+1
1200
0.232
0.3
Input Factors and Levels values in coded form
Spindle Speed
(S) (RPM)
Feed (f) (mm/rev)
Depth of cut (d) (mm)
1
200
0.105
0.1
+1
1200
0.232
0.3
TABLE III. CONSIDERATIONS OF DESIGN PLAN
Experimental Design:

The experimental etails with the output values in the predefined central composite design matrix is given in the table. The procedural steps followed for experimentation are:

Cutting free cutting brass bars by power saw and performing initial turning operation in Lathe to get desired dimension of the work pieces.

Calculating weight of each specimen by the high precision digital balance meter before machining.

Performing straight turning operation on specimen in various cutting environments involving various combinations of process control parameters: spindle speed, feed, depth of cut.

Calculating weight of each machined plate again by the digital balance meter. And measuring surface roughness and surface profile with the help of a portable stylustype profile meter.
TABLE IV. EXPERIMENTAL DETAILS WITH OUTPUT FACTORS.
F1
F2
F 3
R1
R2
R3
Run
Speed (RPM)
Feed (Mm/R EV)
DOC (Mm)
S. Rough (Âµm)
MRR
(Mm3/ sec)
Forc e (N)
1
910
0.184
0.2
3.4
14007
6.2
2
700
0.105
0.3
2.5
33903
8
3
550
0.105
0.1
1.7
17770
6.45
4
550
0.323
0.1
3.7
3988
8.5
5
550
0.323
0.2
6.8
24546
9.27
1
6
1200
0.105
0.2
2.3
17173
8.5
7
910
0.184
0.2
4.4
13753
6.5
8
1200
0.323
0.1
8
2770
7
9
1200
0.323
0.3
7.6
10471
9.20
5
10
550
0.184
0.2
3.9
16796
9
11
1200
0.105
0.1
2.6
5734
5.2
12
910
0.323
0.2
4.6
5995
6.37
13
910
0.184
0.2
4.4
17366
6.24
14
550
0.184
0.2
4.04
26930
9
15
910
0.184
0.1
5.2
9880
3.83
7
16
910
0.323
0.1
6.1
1339
4.88
17
700
0.105
0.3
3.25
36391
8
18
1200
0.184
0.3
3.95
14608
10
19
550
0.323
0.3
6.55
14313
9
20
910
0.184
0.1
3.85
2485
4
Analysis of Surface roughness(R):
Table depicts the significance of the model and its terms through ANOVA.
Sour ce
Sum of Squares
df
Mean Square
F
value
Pvalue
Mod el
49.90
6
8.32
11.42
0.0002
Signifi cant
As
1.65
1
1.65
2.26
0.0156
4
Bf
46.16
1
46.16
63.41
< 0.0001
Cd
0.7292
1
0.729
1.00
0.0335
2
AB
2.81
1
2.81
3.86
0.0712
AC
3.61
1
3.61
4.96
0.0442
BC
0.6947
1
0.694
0.9543
0.3464
Resi dual
9.46
13
0.728
Lack of Fit
7.59
8
0.949
2.54
0.1597
not Signifi cant
Pure Error
1.87
5
0.373
Cor Tota l
59.36
19
Sour ce
Sum of Squares
df
Mean Square
F
value
Pvalue
Mod el
49.90
6
8.32
11.42
0.0002
Signifi cant
As
1.65
1
1.65
2.26
0.0156
4
Bf
46.16
1
46.16
63.41
< 0.0001
Cd
0.7292
1
0.729
1.00
0.0335
2
AB
2.81
1
2.81
3.86
0.0712
AC
3.61
1
3.61
4.96
0.0442
BC
0.6947
1
0.694
0.9543
0.3464
Resi dual
9.46
13
0.728
Lack of Fit
7.59
8
0.949
2.54
0.1597
not Signifi cant
Pure Error
1.87
5
0.373
Cor Tota l
59.36
19
TABLE V. ANALYSIS OF VARIANCE TABLE FOR SURFACE ROUGHNESS
From the Table, the Model F, the Model Fvalue of 11.42 implies the model is significant. Values of "Prob > F" less than 0.0500 indicate model terms are significant. In this case A, B, C are significant model terms. Values greater than 0.1000 indicate the model terms are not significant.
The Predicted RÂ² of 0.6455 is in reasonable agreement with the Adjusted RÂ² of 0.7670; i.e. the difference is less than
0.2. Adeq Precision measures the signal to noise ratio. A ratio greater than 4 is desirable. The ratio of 10.905 indicates an
adequate signal. This model can be used to navigate the design space.
An interaction occurs when the response is different depending on the settings of two factors. Plots make it easy to interpret two factor interactions. They will appear with two nonparallel lines, indicating that the effect of one factor depends on the level of the other. The Interaction graph in figure shows that speed is effects Roughness (R) much more than feed and depth of cut. In the figure, the actual values are marked as dots i.e., experimental values, then the straight line of predicted values are drawn using prediction equations.
Fig.1. Interaction plot of SR
Fig.2. Residual plot of SR
Analysis of metal removal rate (MRR):
Table depicts the significance of the model and its terms through ANOVA.
TABLE VI. ANALYSIS OF VARIANCE TABLE FOR MATERIAL REMOVE RATE
Source
Sum of Squares
df
Mean Square
F
valu e
p value
Model
1.59E+09
6
2.65E+08
11.5
0
0.0001
Signific ant
As
3.52E+08
1
3.52E+08
15.2
4
0.0018
Bf
4.30E+08
1
4.30E+08
18.6
1
0.0008
Cd
6.35E+08
1
6.35E+08
27.4
7
0.0002
AB
1.04E+07
1
1.04E+07
0.45
1
0.5132
AC
9.22E+06
1
9.22E+06
0.39
9
0.5386
BC
4.65E+07
1
4.65E+07
2.01
0.1797
Residual
3.00E+08
13
2.31E+07
Lack of Fit
2.10E+08
8
2.63E+07
1.46
0.3506
Not signific ant
Pure Error
8.99E+07
5
1.79E+07
CorTotal
1.89E+09
19
From the Table, it is clear that the Model Fvalue of 11.50 implies the model is significant. Values of "Prob > F" less than 0.0500 indicate model terms are significant. In this case A, B, C are significant model terms.
The Predicted RÂ² of 0.5210 is not as close to the Adjusted RÂ² of 0.7683 as one might normally expect; i.e. the difference is more than 0.2. This may indicate a large block effect or a possible problem with your model and/or data. Things to consider are model reduction, response transformation, outliers, etc.
All empirical models should be tested by doing confirmation runs. Adeq Precision measures the signal to noise ratio. A ratio greater than 4 is desirable. Your ratio of
12.012 indicates an adequate signal. This model can be used to navigate the design space.
The Interaction graph in figure shows that speed is effects MRR much more than feed and depth of cut. In the figure, the actual values are marked as dots i.e., experimental values, then the straight line of predicted values are drawn using prediction equations.
Fig.3. Interaction plot of MRR
Fig.4. Residual plot of MRR
Analysis of Cutting Force (F):
Table depicts the significance of the model and its terms through ANOVA.
From the Table, it is clear that the Model Fvalue of
548.99 implies the model is significant. Values of "Prob > F" less than 0.0500 indicate model terms are significant. In this case A, B, C are significant model terms.
The Predicted RÂ² of 0.9884 is in reasonable agreement with the Adjusted RÂ² of 0.9962; i.e. the difference is less than

Adeq Precision measures the signal to noise ratio. A ratio greater than 4 is desirable. Your ratio of 75.312 indicates an adequate signal. This model can be used to navigate the design space.
The Interaction graph in figure shows that feed effects F much more than speed and depth of cut. In the figure, the actual values are marked as dots i.e., experimental values, then the straight line of predicted values are drawn using prediction equations.
TABLE VII. ANALYSIS OF VARIANCE TABLE FOR CUTTING FORCE
Source
Sum of Square
df
Mean
Squar e
Fvalue
pvalue
Model
64.97
9
7.22
548.99
< 0.0001
signific
ant
As
0.5245
1
0.524
39.89
< 0.0001
Bf
0.5268
1
0.526
40.07
< 0.0001
Cd
22.17
1
22.17
1686.06
< 0.0001
AB
0.0547
1
0.054
4.16
0.0687
AC
1.31
1
1.31
99.79
< 0.0001
BC
3.96
1
3.96
301.33
< 0.0001
AÂ²
21.86
1
21.86
1662.61
< 0.0001
BÂ²
0.0335
1
0.033
2.55
0.1417
CÂ²
1.66
1
1.66
126.35
< 0.0001
Residua
l
0.1315
10
0.013
Lack of Fit
0.0651
5
0.013
0.9817
0.5078
not
signific ant
Pure
Error
0.0664
5
0.013
Cor
Total
65.10
19
Fig.5. Interaction plot of Cutting Force
Fig.6. Residual plot of Cutting Force


RESULTS
TABLE VIII. OPTIMAL SOLUTION
S 
F 
D 
Ra 
MRR 
Cutting force 
790.48 
0.105 
0.166 
2.579 
19055 
5.4401 
Surface plots for optimal outputs
The threedimensional surface plots given below depict the behaviour of response surfaces with respect to the most influencing input parameters at optimal setting.
Fig.7. Surface plot for SR
Fig.9. Surface plot for Cutting Force
CONCLUSION
From the observation the following results are summarized.

Feed has more significance while speed and depth of cut have less significant effect on surface roughness (Ra).

Speed has more significant effect while feed and depth of cut have less significant effect on MRR.

Feed has more significant effect while speed and depth of cut ave less significant effect on Cutting force(F).
Fig.8 Surface plot for MRR
REFERENCES

K Devaki Devi, K Satish Babu and K Hema Chandra Reddy (2016), Optimization of Cutting Force And Tool Temperature Using Ann Based Multi Objective Genetic Algorithms In Turning Heat Treated Beryllium Copper Alloy, International Journal of Mechanical and Production Engineering Research and Development (IJMPERD) ISSN(P): 22496890; ISSN(E): 22498001 Vol. 6, Issue 1, Feb 2016, 1122.

K Devaki Devi, K Satish Babu and K Hema Chandra Reddy (2015), Mathematical Modelling and Optimization of Turning Process Parameters using Response Surface Methodology, International Journal of Applied Science and Engineering (IJASE),13(1), pp. 5568.

K. Naga Lakshmi, K. Rambabu, K. V. P. P. Chandu, B.V. Subrahmanyam, Optimization of Cutting Parameters in Turning Operation of Aluminium 7075 Alloy, International Journal of Engineering Research & Technology (IJERT) ISSN: 22780181 IJERTV4IS100190 www.ijert.org Vol. 4 Issue 10, October2015