Study and Analysis of Effect of Cutting Parameters on ...urpjournals.com/tocjnls/2_15v5i3_6.pdf ·...

63 Advanced Engineering and Applied Sciences: An International Journal 2015; 5(3): 63-73

ISSN 2320–3927

Original Article

Study and Analysis of Effect of Cutting Parameters on Cutting Forces

and Surface Roughness

Masood Atahar Khan1, Jayant K. Kittur2, Vishal. Dutt Kohir3

1) M.Tech student, Production Management, Department of Industrial Production, KLS Gogte Institute of Technology,

Belgaum, Karnataka, India

2) Prof, PhD, Department of Industrial Production, KLS Gogte Institute of Technology, Belgaum, Karnataka, India

3) Prof, Department of Mechanical Engineering, Angadi Institute of Management & Technology, Belgaum, Karnataka,

India

1. Contact: 9901638687, e-mail: [email protected]; 2. Contact: 0831-2498508, e-mail: [email protected]; 3. Contact:

9449140953, e-mail: [email protected]

Received 26 June 2015; accepted 07 July 2015

Abstract

In the present competitive and dynamic market environment, large and small manufacturing industries are more allotted to

Optimization and economic machining. Cutting parameters play a crucial role in every machining process in affecting the

production rate, quality of the output and economical aspects of a machining process. Finite element simulation of the

cutting process is advantageous in many ways over experimental analysis, as it provides information on deformations along

each point of cutting line, stresses induced at each node/point and temperature distribution in the tool and work material

during the machining process. In this paper, the significant effect of cutting speed, feed rate and rake angle on cutting

forces, tool temperature and work specimen temperature in machining AISI 1045 mild carbon steel has been studied. For

the study of which, a series of 20 experiments and simulation trials were conducted as per design of experiments. All the

other parameters besides the selected were kept constant throughout the simulation process. Finite element simulations

were conducted using computer aided engineering software Deform 2D. The results from simulations indicate that forces

decrease with increment in cutting speed. Machining with tools having bigger rake angles tend to increase the temperature

and but decrease in cutting forces. Whereas, higher feed values result in higher cutting forces and high temperature.

© 2015 Universal Research Publications. All rights reserved

Key words: Cutting process, Deform 2D, cutting forces, Surface roughness, Design of experiment (DOE), Response

surface method (RSM).

1. INTRODUCTION

1.1 PREFACE

Turning process is more widely metal cutting operation

used among all the machining operations. The rising

interest in turning process is reaching new levels in the

current industrial age. High level of competition is leading

for making efforts in maximizing the economical factors in

manufacturing machined parts quality measurement of

machined mechanical products. With further rise in

modernization, customers have increasing demands on

quality compatibility, retentivity, reliability and

functionability. As a result during machining operations,

selection of input machining parameters and understanding

their effects has become a critical job. Thus, a need for a

systematic methodological approach by using experimental

methods and statistical/mathematical models to achieve

optimum productivity and quality of products has become

compulsory in every manufacturing organization [14]. The

objective of this project is to study and analyze the effect of

cutting parameters i.e. speed, feed and back rake angle in

turning of En 8 medium carbon steel using a carbide tip

tool. Develop a plot of experiments using design of

experiment (DOE) technique by Response surface method

(RSM) and generate a Regression model for Cutting forces

and Surface Roughness. Lastly Performing a software

simulations using Deform 2D for the same set of input

parameters and data and in the end compare the

experimental results with simulation results and conclude

the work of study.

1.2 WORK PROBLEM STATEMENT

In a turning process, cutting parameters such as Spindle

Speed, Feed and Rake angle have a significant influence on

Tool forces and Surface Roughness of the material. Aim of

this project work is to study the effect of varying cutting

parameters like Speed, Feed and back rake angle on

responses, Surface Roughness and cutting forces generated

Available online at http://www.urpjournals.com

Advanced Engineering and Applied Sciences: An International Journal

Universal Research Publications. All rights reserved

mailto:[email protected]

mailto:[email protected]

http://www.urpjournals.com/


Fig 1.1: Deform 2D interface

in the carbide tool during the machining of En 8 work

material.

1.3 DESIGN OF EXPERIMENTS

It is a scientific approach of analyzing and developing

statistics of an experimental data in a systematic and

orderly manner is called Design of experiments. Here,

Response surface method type of design has been made use

of for the experimental study and analysis.

1.4 DEFORM 2D

Deform 2D is one of the process simulation computer aided

engineering (CAE) software, that is designed to analyze the

two-dimensional flow in metal forming processes using the

Axi-symmetric assumption based on the finite element

method (FEM). Deform 2D is an accurate and robust CAE

software that has been used in industrial applications for

more than 20 years. Deform 2D finds application in; Closed

die forging, Open die Forging, Machining, Rolling,

Extrusion, Drawing, Cogging, Compaction, Upsetting etc.

Deform 2D provides; contour plots, load-stroke, point

tracking, flow net, prediction information material flow and

heat flow during the forming process. Degree of

deformation and temperature distribution is easily

determined in an integrated simulation interface. Automatic

meshing is performed during simulations using four-node

quadrilateral elements for the meshing. Here, the friction

between tool and chip is shear type (constant shear friction

modelling). Material types available include elastic, plastic

and thermo-plastic and the tool used is rigid. Boundary

conditions are based on Lagrangian boundary conditions

(i.e. Left, right and bottom edges fixed in 2 directions). Fig

1.1 shows the Deform 2D simulation interface.

2 MATERIALS AND METHODS

2.1LITERATURE REVIEW

Jadhav J.S, Jadhav B.R et.al [1] here, the effect of cutting

parameters on cutting force & feed force in turning Process

have been reviewed. Experiments were conducted on centre

lathe in order to study the influence of cutting parameters

on response using variance analysis (ANOVA) technique.

D.V.V. Krishan Prasad et.al [2] researches on optimum

parameters in order to get the minimum surface roughness

and to analyze the effect of machining parameters and rake

angles on the surface finish. In a turning process surface

roughness depends on machining parameters and tool

geometry.

Al-Zkeri, J. Rech, T. Altan, H. Hamdi, and F. Valiorgue

et.al [3] studied the effects of edge radius of a round-edge

coated carbide tool on chip formation, cutting forces, and

tool stresses in orthogonal cutting of an alloy steel

42CrMo4 (AISI 4142H).

N.E.Edwin Paul et.al [4] used and is powerful tool for

optimization, which provides a systematic and effective

methodology for design and optimization of cutting

parameters.

Neeraj Saraswat, Ashok Yadav, Anil Kumar and Bhanu

Prakesh Srivastava et.al [5] In the present work the cutting

parameters (depth of cut, feed rate, spindle speed) have

been optimized in turning of mild steel of in turning

operations on mild steel and as a result of that the

combination of the optimal levels of the factors was

obtained to get the lowest surface roughness.

F. Klocke, H.-W. Raedt, and S. Hoppe et.al [6] this papers

provides a small review on description of cutting processes

that are FEM (Finite element method) simulations based. In

this article the simulation of orthogonal turning of AISI

1045 steel in DEFORM 2D software has been elaborated

Nicoleta Lungu , Marian Borzan et.al [7] This paper studies

the influence of cutting speed and feed rate on tool

geometry, temperature and cutting forces in machining

AISI 1045 carbon steel. For this research were run six trials

of simulation. Finite element simulation was performed

with the software Deform 2D Machining.


2.2 METHODOLOGY FLOW-CHART

Fig 2.1: EN 8 work specimen

2.3 METHODOLOGY

a) Checking and preparing the Centre Lathe ready for

performing the machining operation.

b) Mounting the tool and work apparatus on the lathe along

with dynamometer and accessories, to measure the force

components.

c) Performing straight orthogonal turning operation on each

of the 20 specimens as per the standard order of the

experimental design matrix developed, using various

combinations of cutting input parameters like spindle

speed, feed and back rake angle (keeping depth of cut

constant as 1mm) to control the process.

d) Now recording the force data obtained on the display

unit through dynamometer at interval of each second.

Taking the mean force for each work-piece data obtained.

e) Similarly performing a repetition of the experiment to

obtain replica set of readings and choosing the best results

of the two.

f) Now measuring the surface roughness for each work-

piece with the help of a portable stylus-type Taylor

Hobson's Talysurf and recording individual data.

g) After experimental analysis is done, determination of

constant coefficients of the developed model is done using

Response surface methodology. Finally testing the

significance of the coefficients using the analysis of

variance (ANOVA).

h) In the end, all experimental input data is fed into Deform

2D software for simulation analysis. The results from

simulation analysis and experimental analysis are compared

to check their relation and extent of similarity.


Table 2.1: Chemical composition of EN 8 material by weight %

Elements C Mn Si P S Ni Cr Cu

Composition (%) 0.39998 0.75462 0.20041 0.03121 0.02356 0.00520 0.01004 0.00445

Elements Mo V Ti Al Nb Sn Pb

Composition (%) 0.01426 0.00071 0.00196 0.04507 0.00001 0.00158 0.00459

Elements Mg Zn Ce N Te Fe_ CEQ

Composition (%) 0.00029 0.00215 0.00492 0.02346 0.00435 98.45946 0.47719

Table 2.2: Machining parameters & their levels

Table 3.1: Experimental data of cutting forces and surface roughness for En8 work specimen with Carbide tool

Std Order Run Order Speed

(rpm)

Feed

(mm) Back rake (0)

Force Fc

(Kgf)

Ra

(µ)

1 12 420 0.08 5.0 28.6608 5.92

2 15 1280 0.08 5.0 33.5267 5.35

3 10 420 0.20 5.0 28.4314 5.11

4 11 1280 0.20 5.0 21.3421 5.93

5 2 420 0.08 10.0 27.9206 5.05

6 8 1280 0.08 10.0 32.2076 2.23

7 17 420 0.20 10.0 29.1812 4.95

8 1 1280 0.20 10.0 33.9131 6.54

9 13 420 0.14 7.5 33.2264 4.80

10 14 1280 0.14 7.5 33.6474 3.65

11 9 850 0.08 7.5 33.6932 5.21

12 19 850 0.20 7.5 33.7428 4.45

13 6 850 0.14 5.0 34.0772 9.01

14 4 850 0.14 10.0 34.4265 4.70

15 20 850 0.14 7.5 31.3683 4.55

16 7 850 0.14 7.5 32.2529 3.40

17 3 850 0.14 7.5 31.9498 3.45

Table 4.2: Tool specifications

Tool Dimensions in mm Inclinations Tip

height Breadth length clearance nose clearance

angles

top Rake

angle

Fig 2.2: ISO6 R2020 carbide tool

Cutting parameters

Code Levels Speed

(rpm)

Feed

(mm/rev)

Back Rake angle

(degrees)

1 Low 420 0.08 5.00

2 Medium 850 0.14 7.50

3 High 1280 0.20 10.00


Fig 2.3: Roughness and waviness profile for the given work specimen

2.4 WORK-PIECE AND TOOL MATERIAL

The current turning process experiment is carried

on medium carbon steel specimen by the name EN 8 (AISI

1045) of dimension Ø25 X 80 mm. Fig 2.1 shows EN 8

work material used for experimentation.

The chemical composition for EN 8 work material is as

shown in Table 2.1. Single point carbide tip tools with three

different geometries varying only in top rake angle have

been used for the orthogonal turning of EN 8 work

specimen. Carbides have a wide range of grades that are

available for several applications and processes. A carbide

tool ISO6 R2020 P40 a standard carbide tool for high speed

orthogonal cutting has been used. Fig 2.2 shows ISO6

R2020 carbide tool.

2.5 SELECTION OF FACTORS, THEIR RANGE AND

LEVELS

For the present work study and analysis, the selected

variable input parameters are;

Cutting speed of the tool

Feed rate

Back Rake angle

2.6 EXPERIMENTATION

Experimentation is carried out on medium steel

cylindrical bars of EN 8 (AISI 1045) material using a

standard Carbide tool (ISO6 R2020) in order to find out the

effect of cutting parameters such as cutting speed, feed rate

and top rake angle on the work material to study and

analyze surface roughness (SR) of the specimen and cutting

Fig 4.1: Main effect plot for experimental cutting force Fc


Fig 4.2: Main effect plot for analytical cutting force Fx

Fig 4.3: Main effect plot for arithmetic mean roughness value Ra

forces on the tool during straight turning process.

Throughout the experimentation Depth of cut (DOC) is

kept constant. The measurement of surface roughness is

done by using Taylor Hobson’s surftronic talysurf. Fig 2.3

shows a typical roughness and waviness profile for the

given work specimen obtained on talysurf. The project is

further extended to software analysis of the experiments

carried. A machining simulation software named Deform

2D has been used for the analysis. Later the results from

software analysis are compared with experimental analysis

to conclude the accuracy of the project. Table 2.2 shows

Machining parameters & their levels

3 RESULTS

By using the Analysis of variance (ANOVA) for

the data collected through practical experiment and

software analysis, a linear regression prediction model for

the response parameters using the input parameters is

developed. The regression analysis comprising regression

co-efficient, surface plots, contour plots, interaction plots

and main effect plots for the output parameters viz. cutting

force (Fc), surface roughness (Ra), x-load (Fx) and y-load

(Fy) have been generated. Table 3.1 shows Experimental

data of cutting forces and surface roughness for En8 work

specimen with Carbide tool.


Fig 4.4: Bar comparison between Fc and Fx

Fig 4.5: Line comparison between Fc and Fx

3.1 ANOVA TABLE, GRAPHS, PLOTS AND PREDICTION MODEL FOR CUTTING FORCE (Fc)

Response Surface Regression: Fc versus Cutting speed, Feed, Back rake angle

Analysis of Variance

Source DF Adj SS Adj MS F-Value P-Value

Model 9 126.458 14.0509 0.97 0.051

Linear 3 53.048 17.6826 1.22 0.035

Cutting speed 1 1.956 1.9556 0.14 0.721

Feed 1 49.815 49.8149 3.45 0.093

Back rake angle 1 1.277 1.2774 0.09 0.077

Square 3 10.899 3.6330 0.25 0.859

Cutting speed*Cutting speed 1 9.475 9.4749 0.66 0.043

Feed*Feed 1 0.111 0.1108 0.01 0.093

Back rake angle*Back rake angle 1 0.561 0.5612 0.04 0.084

2-Way Interaction 3 62.511 20.8370 1.44 0.288

Cutting speed*Feed 1 4.654 4.6537 0.32 0.058

Cutting speed*Back rake angle 1 39.819 39.8194 2.76 0.128

Feed*Back rake angle 1 18.038 18.0378 1.25 0.029

Error 10 144.489 14.4489

Lack-of-Fit 5 140.172 28.0345 32.47 0.001

Pure Error 5 4.317 0.8634

Total 19 270.947


Model Summary

S R-sq R-sq(adj) R-sq(pred)

3.80118 93.67% 0.00% 0.00%

Coded Coefficients

Term Effect Coef SE Coef T-Value P-Value VIF

Constant 29.69 1.55 19.15 0.000

Cutting speed 0.76 0.38 1.03 0.37 0.072 1.00

Feed -3.82 -1.91 1.03 -1.86 0.093 1.00

Back rake angle 0.61 0.31 1.03 0.30 0.772 1.00

Cutting speed*Cutting speed 1.62 0.81 1.00 0.81 0.043 1.02

Feed*Feed -0.18 -0.09 1.00 -0.09 0.093 1.02

Back rake angle*Back rake angle -0.39 -0.20 1.00 -0.20 0.848 1.02

Cutting speed*Feed 1.53 0.76 1.34 0.57 0.058 1.00

Cutting speed*Back rake angle 4.46 2.23 1.34 1.66 0.012 1.00

Feed*Back rake angle 3.00 1.50 1.34 1.12 0.290 1.00

The P value for the above model Cutting speed*Cutting speed and Cutting speed*Back rake angle is lower than 0.05 (at

93% confidence level) indicates that the model considered is statistically significant.

Regression Equation in Uncoded Units

Fc = 70.5 - 0.0286 Cutting speed - 243 Feed - 3.37 Back rake angle

+ 0.000004 Cutting speed*Cutting speed - 97 Feed*Feed

- 0.032 Back rake angle*Back rake angle + 0.059 Cutting speed*Feed

+ 0.00208 Cutting speed*Back rake angle + 20.0 Feed*Back rake angle

3.2 ANOVA TABLE, GRAPHS, PLOTS AND PREDICTION MODEL FOR ANALYTICAL CUTTING FORCE (Fx)

Response Surface Regression: Fx versus Cutting speed, Feed, Back rake angle

Analysis of Variance Source DF Adj SS Adj MS F-Value P-Value

Model 9 112.659 12.5177 0.91 0.550

Linear 3 72.460 24.1532 1.76 0.000

Cutting speed 1 2.218 2.2179 0.16 0.696

Feed 1 63.139 63.1387 4.60 0.057

Back rake angle 1 7.103 7.1030 0.52 0.048

Square 3 18.056 6.0188 0.44 0.054


Feed*Feed 1 1.269 1.2692 0.09 0.076


2-Way Interaction 3 22.143 7.3811 0.54 0.066




Error 10 137.156 13.7156

Lack-of-Fit 5 135.511 27.1021 82.35 0.000

Pure Error 5 1.646 0.3291

Total 19 249.81

Model Summary S R-sq R-sq(adj) R-sq(pred)

3.70346 95.10% 83.00% 80.00%

Coded Coefficients


Constant 31.98 1.51 21.17 0.000

Cutting speed 0.81 0.40 1.00 0.40 0.696 1.00

Feed -4.30 -2.15 1.00 -2.15 0.057 1.00

Back rake angle 1.44 0.72 1.00 0.72 0.048 1.00

Cutting speed*Cutting speed -2.207 -1.103 0.976 -1.13 0.033 1.02

Feed*Feed -0.594 -0.297 0.976 -0.30 0.767 1.02

Back rake angle*Back rake angle -0.216 -0.108 0.976 -0.11 0.091 1.02



Feed*Back rake angle 2.85 1.42 1.31 1.09 0.033 1.00


The P value for the above model back rake angle, Cutting speed*Cutting speed and Feed*Back rake angle is lower than

0.05 (at 95% confidence level) indicates that the model considered is statistically significant.


Fx = 51.2 + 0.0017 Cutting speed - 178 Feed - 2.06 Back rake angle

- 0.000006 Cutting speed*Cutting speed - 330 Feed*Feed

- 0.017 Back rake angle*Back rake angle + 0.044 Cutting speed*Feed

+ 0.00061 Cutting speed*Back rake angle + 19.0 Feed*Back rake angle

3.5 ANOVA TABLE, GRAPHS, PLOTS AND PREDICTION MODEL FOR ARITHMETIC MEAN ROUGHNESS VALUE

(Ra)

Response Surface Regression: Ra versus Cutting speed, Feed, Back rake angle

Analysis of Variance Source DF Adj SS Adj MS F-Value P-Value

Model 9 31.2314 3.4702 3.00 0.051

Linear 3 10.0360 3.3453 2.90 0.088

Cutting speed 1 0.6838 0.6838 0.59 0.460

Feed 1 0.7821 0.7821 0.68 0.430

Back rake angle 1 8.5701 8.5701 7.42 0.021

Square 3 14.1342 4.7114 4.08 0.039


Feed*Feed 1 0.9639 0.9639 0.83 0.383


2-Way Interaction 3 7.0612 2.3537 2.04 0.017




Error 10 11.5545 1.1555

Lack-of-Fit 5 6.7722 1.3544 1.42 0.035

Pure Error 5 4.7823 0.9565

Total 19 42.7860

Model Summary

S R-sq R-sq(adj) R-sq(pred)

1.07492 92.99% 88.69% 78.00%

Coded Coefficients


Constant 3.955 0.438 9.02 0.000

Cutting speed 0.448 0.224 0.291 0.77 0.461 1.00

Feed -0.479 -0.239 0.291 -0.82 0.043 1.00

Back rake angle -1.584 -0.792 0.291 -2.72 0.021 1.00

Cutting speed*Cutting speed 0.089 0.045 0.283 0.16 0.877 1.02

Feed*Feed 0.517 0.259 0.283 0.91 0.038 1.02

Back rake angle*Back rake angle 1.949 0.975 0.283 3.44 0.066 1.02



Feed*Back rake angle -0.383 -0.191 0.380 -0.50 0.062 1.00

The P value for the above model back rake angle, Feed*Feed and Feed is lower than 0.05 (at 93% confidence level)

indicates that the model considered is statistically significant.


Ra = 25.70 - 0.01002 Cutting speed - 100.1 Feed - 2.819 Back rake angle

+ 0.000000 Cutting speed*Cutting speed + 287 Feed*Feed

+ 0.1559 Back rake angle*Back rake angle + 0.0565 Cutting speed*Feed

+ 0.000522 Cutting speed*Back rake angle - 2.55 Feed*Back rake angle

4. DISCUSSION

From the Fig 4.1, it is clear that, while machining En 8

Specimen on a conventional lathe machine, Cutting force is

affected significantly by all three process parameters

namely cutting speed/spindle speed, feed rate and back rake

angle. Cutting force (Fc) is minimal at start and further

decreases on increasing the cutting speed. Whereas, Fc

increases drastically as the feed rate increases. Fc decreases

gradually with increase in back rake angle. From

interaction plot it is clear that there is a significant effect

of from cutting speed, feed and back rake angle on cutting

force Fc. The first plot shows that, there exists a certain

interaction between cutting speed and feed rate in speed

range of 400-800 rpm at all three levels of feed. Likewise

in the second plot there is interaction between cutting speed

and back rake angle in speed range of 800-1200 rpm at all

three levels of back rake angle. Also in the third plot,

Interaction between feed and back rake angle exists

between all three levels of feed in the range of Fc between

30-32.5 Kgf.

Therefore by using a combination of level three speed,

level one feed and level three back rake angle would give


the optimal cutting forces influenced on the tool.

According to the above main effect plot, the optimal

conditions for minimum cutting forces (Fc) are:

Cutting speed at level 3 (1280 rpm)

Feed rate at level 1 (0.08 mm/ rev)

Back rake angle at level 3 (100)

From the Fig 4.2 it is clear that in machining En 8 work

specimen, analytical Cutting force Fx is affected by all the

three process parameters. Fx rises initially with increase in

speed and later decreases on further increment. Whereas Fx

decreases drastically with increment in the feed rate. Rake

angle has a significant on Fx, i.e Fx is increases over the

given range of back rake angle. Interpretation of the above

interaction plot shows that, there is a significant effect of

from cutting speed, feed and back rake angle on cutting

force Fx. The first plot shows that, there exists no

interaction between cutting speed and feed rate throughout

the entire cutting speed range. In the second plot there is

interaction between cutting speed and back rake angle in

speed range of 800-1200 rpm between each level of back

rake angle. Likewise in the third plot, Interaction between

feed and back rake angle exists at all three levels of feed in

the values of Fx ranging between 27.5-32.5 Kgf.

Hence by using a combination of level three speed, level

three rake angle and level one feed would give the best

achievable optimal cutting forces experienced by the tool.


conditions for minimum cutting forces (Fc) are:



Back rake angle at level 3 (100)

Fig 4.3 indicates that the mean roughness Ra is affected

mainly by feed rate and back rake angle. Both of which

follow a similar pattern or fall and rise. Cutting speed has a

slight significant on Ra. Speed is almost uniform initially,

but further increment in speed leads to increment in

roughness value Ra. From interpretation of the interaction

plot it can be said that significant effect on mean roughness

value Ra. The first plot shows that, there exists a certain

interaction between cutting speed and feed rate in speed

range of 800-1200 rpm at each level of feed. Likewise in

the second plot there is interaction between cutting speed

and back rake angle in speed range of 800-1600 rpm at

each level of back rake angle. In the third plot, Interaction

between feed and back rake angle exists only at two levels

in the feed range of 0.125 and 0.15 mm/rev, giving Ra

value ranging between 3-6µ.

Thus by using a combination of level three cutting speed,

level two back rake angle and level one feed would give the

optimal surface finish on the work specimen.


conditions for minimum surface roughness are:



Back rake angle at level 2 (7.50)

4.1 COMPARISON BETWEEN THE PRIMARY FORCES

OBTAINED FROM SOFTWARE ANALYSIS AND

EXPERIMENTAL ANALYSIS

It can be stated from the Fig 4.4 and Fig 4.5 that the values

of both Fc and Fx follow a close relation with each other

for the given set of readings, and thus show minimal

variation throughout the distribution of the graph profile.

The experimentally obtained set of readings for cutting

force Fc and analytical set of readings obtained through

simulation analysis show a similar pattern throughout the

graph, hence it can be stated that simulation analysis can be

a feasible alternate option to experimental analysis.

Since the two sets of readings obtained from experimental

analysis and simulation analysis show very little variation,

therefore the readings of cutting force obtained are correct

and genuine.

5. CONCLUSION

The current work was carried out in order to study and

analyze the effects of input parameters in an orthogonal

turning process, also to determine their effect on the output

response parameters; tool cutting forces and surface

roughness. Thus it was found that, the tool geometry and

cutting parameters have a significant effect on cutting

forces and surface roughness. After completing the study

and analysis of the subject, following conclusions have

been proposed.

I) Tool cutting forces

From the experimental study we understand that

magnitude of cutting forces increases with rise in feed

rate, when other factors like depth of cut are kept

constant. Also the cutting forces decreases with

increasing spindle/cutting speed while depth of cut is

kept constant.

The cutting force is significantly influenced (at 93%

confidence level) by feed rate and back rake angle,

while cutting speed does not have much influence in

comparison. The cutting force increases with the

increase of feed rate and decreases with increase in

rake angle.

Response surface methodology (RSM) gives a

systematic, simple approach and efficient statistical

method for the optimum operating conditions.

The cutting force decreases as the tool rake angle

increases. Higher the top rake angle lesser are forces

experienced by tool. Despite this benefit, greater rake

angle reduces tool tip toughness and reduces tool life.

From the study and analysis, we obtained lowest

cutting forces as 21.08 Kgf for EN8 steel specimen at

cutting speed of 420 rpm, feed rate of 0.2 and rake

angle of 100. Using ISO6 R2020 carbide tool.

II) Surface Roughness

From the experimental study it analysis of variance

(with confidence level of 92.99%) it is clear that tool

with high rake angle provides good surface finish.

Thus it can be stated that the surface roughness of the

work specimen increases as the top/back rake angle of

the tool increases.

surface roughness is continuously improved with the

increase in cutting speed, but increase in feed rate and

rake angle leads to deterioration of the surface.

The regression model developed for surface roughness

is reasonably accurate and can be used of prediction

within limits.

A better surface finish of 2.23 microns of all the

experimental readings was obtained by using


combination of speed 1280 rpm, 0.08mm feed and top

rake angle of 100.

The surface roughness is significantly influenced by

the feed rate. The surface roughness increases with the

increase of the feed rate. The cutting speed has a less

significant effect, i.e. the surface roughness slightly

decreases with increase in cutting speed.

REFERENCES

Reference to journal publication

1. Jadhav J.S, Jadhav B.R “Experimental study on effect

of parameters on cutting force in turning process”,

IJIRAE publishers. 2. D.V.V. Krishna Prasad, “Optimization of Turning

Parameters in order to get the minimum surface

roughness”, ISSN: 2320-2491

3. Nicoleta Lungu , Marian Borzan, “Effect of cutting

speed and feed rate on tool geometry, temperature and

cutting forces in machining AISI 1045 carbon steel

using FEM simulation”, Proceedings in Manufacturing

Systems. ISSN 2067-9238

4. S.V.Alagarsamy, N.Rajakumar, “Analysis of cutting

parameters in CNC machine of Alluminum Alloy

7075”, Khanna Publishers.

5. “Al-Zkeri, J. Rech, T. Altan, H. Hamdi, and F.

Valiorgue., “Optimization of the cutting edge

geometry of coated carbide tools in dry turning of

steels using a finite element analysis”, International of

Machining Science and Technology, Vol. 13, No. 1,

pp. 36-51, 2009.

6. Ilhan Asilturk and Mehmet Cunkas, “Modeling and

prediction of surface roughness in turning operations

using artificial neural network and multiple regression

method”, Expert Systems with Applications, Vol. 38,

No. 5, pp. 5826-5832, 2011

7. Tugrul Ozel and Yigit Karpat, “Predictive modeling of

surface roughness and tool wear in hard turning using

regression and neural networks”, International Journal

of Machine Tools & Manufacture, Vol. 45, pp. 467-

479, 2005.

8. Daniel Kirby(jubuk), E., Zhe Zang, Joseph Chen, C.

and Jacob Chen, “Optimizing surface finish in a

turning operation using the Taguchi parameter design

method”, International Journal of Advanced

Manufacturing Technology, Vol. 30, pp. 1021-1029,

2006.

9. Dr. Philip Selvaraj, P. Chandramohan, “Optimization

of Surface Roughness of AISI 304 Austenitic Stainless

Steel in Dry Turning Operation Using Taguchi Design

Method”- “Journal of Engineering Science and

Technology.

10. Jitendra Verma, Pankaj Agarawal, Lokesh Bajpai,

“Turning Parameter Optimization for Surface

Roughness of ASTM A242 Type-1 Alloys steel by

Taguchi Method”- “International Journal of Advances

in Engineering and Technology March 2012”.

11. S Tamizhmanii, S.Saparudin, S.Hasan, “ Analysis of

Surface Roughness By Turning Process Using Taguchi

Method”- “Journal of Achievements in Materials and

Manufacturing Engineering, Vol. 20, pp. 503-506,

2007”.

12. Hardeep Singh, Rajesh Khanna, M.P.Garg, “Effect of

cutting parameters on Material Removal Rate and

Surface Roughness in Turning En-8”- “Current Trends

in Engineering Research Vol.1, No.1 (Dec.2011)”.

13. Upinder Kumar Yadav, Deepak Narang, Pankaj

Sharma Attri, “ Experimental Investigation and

Optimization Of Machining Parameters for Surface

Roughness in CNC Turning By Taguchi Method”-

“International Journal of Engineering Research and

Applications Vol.2, July-August 2012,pp.2060-2065”.

14. M Kaladhar,K.V.Subbaiah,Ch.Srinivas Rao and

K.Narayana Rao, “ Application of Taguchi Approach

and Utility concept in Solving the Multi-objective

Problem when turning AISI 202 Austenitic Stainless

Steel” - “ Journal of Engineering Science and

Technology Review”.

15. Ashish Kabra, Amit Agarwal, Vikas Agarwal, Sanjay

Goyal, Ajay Bangar, “Parametric Optimization &

Modeling for Surface Roughness, Feed and Radial

Force of EN-19/ANSI-4140 Steel in CNC Turning

Using Taguchi and Regression Analysis Method”,

International Journal of Engineering Research and

Applications (IJERA) ISSN: 2248-9622

16. E. Daniel Kirby, “A parameter design study in a

turning operation using the Taguchi method” , the

Technology Interface/Fall 2006

17. Kapil Sharma, Dalgobind Mahto and S.S. Sen, “In

metal turning, effect of various parameters on cutting

tool: A Review” International Journal of application or

Innovation in Engineering & Management (IJAIEM)

18. Ravinder Tonk and Jasbir Singh Ratol, “Investigation

of the Effects of the Parametric Variations in Turning

Process of En31 Alloy”-“International Journal on

Emerging Technologies 3(1): 160-164(2012)”

19. Krishankant, Jatin Taneja, Mohit Bector, Rajesh

Kumar “Application of Taguchi Method for

Optimizing Turning Process by the effects of

Machining Parameters” International Journal of

Engineering and Advanced Technology (IJEAT)

Volume-2, Issue-1, October 2012

Reference to books:

20. Juneja, B.L. and Sekhon, G.S. “Fundamentals of metal

cutting and machine tools”, New Age International

Pvt. Ltd. Publishers

21. Douglas C. Montgomery, “Design and Analysis of

Experiments” - 5th Edition.

Source of support: Nil; Conflict of interest: None declared

http://www.publishresearch.com/download/323

Study and Analysis of Effect of Cutting Parameters on ...urpjournals.com/tocjnls/2_15v5i3_6.pdf ·...

Documents

Transcript of Study and Analysis of Effect of Cutting Parameters on ...urpjournals.com/tocjnls/2_15v5i3_6.pdf ·...