**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):**30-10-2019 -
**ISSN (Online) :**2278-0181 -
**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 non-starting 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 Be-cu 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 copper-zinc 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%

2.5-3.5

56.5-58.5

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 (20-200Â°C)

20.9 x 10-6

Electrical conductivity

28% IACS

Electrical Resistivity

0.062-ohm 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 stylus-type 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

P-value

Mod el

49.90

6

8.32

11.42

0.0002

Signifi cant

A-s

1.65

1

1.65

2.26

0.0156

4

B-f

46.16

1

46.16

63.41

< 0.0001

C-d

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

P-value

Mod el

49.90

6

8.32

11.42

0.0002

Signifi cant

A-s

1.65

1

1.65

2.26

0.0156

4

B-f

46.16

1

46.16

63.41

< 0.0001

C-d

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 F-value 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 non-parallel 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

A-s

3.52E+08

1

3.52E+08

15.2

4

0.0018

B-f

4.30E+08

1

4.30E+08

18.6

1

0.0008

C-d

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 F-value 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 F-value 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

F-value

p-value

Model

64.97

9

7.22

548.99

< 0.0001

signific

ant

A-s

0.5245

1

0.524

39.89

< 0.0001

B-f

0.5268

1

0.526

40.07

< 0.0001

C-d

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 three-dimensional 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): 2249-6890; ISSN(E): 2249-8001 Vol. 6, Issue 1, Feb 2016, 11-22.

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. 55-68.

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: 2278-0181 IJERTV4IS100190 www.ijert.org Vol. 4 Issue 10, October-2015