Sample Copy. Not For Distribution.Prof. Prafull P. Shirpurkar, Dr. Subhash Namdeo Waghmare, Prof....

Sample Copy. Not For Distribution.

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Optimization of Turning Process

Optimization of Turning Process Parameters by Using Tool Inserts


ii

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iii

OPTIMIZATION

of Turning Process


Parameters by Using Tool Inserts

Prof. Prafull P. Shirpurkar,

Dr. Subhash Namdeo Waghmare,

Prof. Praful T. Date Assistant Professor in Department of Mechanical Engineering

Priyadarshini College of Engineering, Nagpur,India

EDUCREATION PUBLISHING (Since 2011)

www.educreation.in


iv


v

CONTENTS

Sr. Chapter Page

1 Title Page iii

6 Table of Contents iv

7 List of Tables viii

8 List of Figures xi

9 List of Abbreviations xiii

10 Abstract xiv

11 Chapter 1 1

Introduction 1

1.1 Introduction 1

1.1.1 Adjustable Cutting Factors in Turning 5

1.1.2 Cutting Tools and Tool Geometry 7

1.1.3 Cutting Tool Materials 11

1.1.4 Surface Finish in Machining 18

1.1.5 Factors Affecting the Surface Finish 21

1.1.6 Measurement of Surface Roughness 22

1.2 Factors Influencing Surface Roughness in

Turning

25

1.3 Present Needs in Industry 27

12 Chapter 2 28

Literature Review 28

2.1 Literature Review 28


vi

2.2 Literature Review Analysis 51

2.3 Company Profile 52

2.4 Problem Statement 52

2.5 Objective of present work 54

13 Chapter 3 55

Methodology 55

3.1 Taguchi Method 55

3.2 Taguchi Based Design of Experiments 56

3.3 Steps in Taguchi Method 57

3.4 Selection of Orthogonal Array 58

3.5 Orthogonal Array Selector 59

14 Chapter 4 61

Experimentation 61

4.1 Tools, Machine and Equipment’s Used 61

4.1.1 Tool Used 62

4.1.2 Specification of cutting tool inserts 63

4.1.3 Work piece 64

4.1.4 Vibrotometer 64

4.2 Design of Experiments Via Taguchi Method:

(OA)

66

4.3 Experimental setup 68

4.4 Working Procedure 69

15 Chapter 5 71

Experimental Data Analysis 71

5.1 Data Analysis: Application of Taguchi Method 71

5.2 Optimization of Parameter for Minimum 72


vii

Surface Roughness

5.3 Response Table Mean S/N Ratio for Surface

Roughness

73

5.4 Result Of (ANOVA) For Roughness 74

5.5 Noise Factors Calculation for Surface

Roughness

76

5.6 ANOVA Calculation: Vibration (Noise 1,

Noise 2)

76

5.7 Experimental Result for Surface Roughness 77

16 Chapter 6 79

Tool life and Material Removal Rate 79

6.1 Optimization of Parameter For Maximum Tool

life

79

6.2 Response Table Mean S/N Ratio for Tool Life 80

6.3 Result of Analysis of Variance (ANOVA) For

Tool Life

81

6.4 Noise Factors Calculation for Tool Life 82

6.5 ANOVA Calculation: Vibration (Noise 1,

Noise 2)

83

6.6 Experimental Result for Tool Life 84

6.7Optimization Of Parameter for Maximum

(MRR)

86

6.8 Response Table Mean S/N Ratio For MRR 87

6.9 Result of Analysis Of Variance (ANOVA) For

MRR

88

6.10 Noise Factors Calculation For MRR 89

6.11 ANOVA Calculation: Vibration (Noise 1, 90


viii

Noise 2) For MRR

6.12 Experimental Result For MRR 91

17 Chapter 7 93

Results and Discussion 93

7.1 Result of Experimentation in Optimizing

Parameter for Minimum Surface Roughness

93


Parameter for Maximum Tool Life

95


Parameter for Maximum MRR

97

18 Chapter 8 99

Conclusion 99

19 Chapter 9 99

Future Scope 101

References 102

Appendices 109

Resume 112


ix

LIST OF TABLES

SR.

NO. NAME OF THE TABLE/GRAPH

FIG/G

RAPH

NO.

PAGE

NO.

1 Adjustable Parameter in Turning

Operation 1.1 2

2 Turning process 1.2 5

3 Geometry of a single point turning tool 1.3 8

4 Idealized model of surface roughness 1.4 19

5 Surface roughness profiles. 1.5 20

6

Schematic diagram of surface

roughness measurement technique by

Stylus equipment

1.6 22

7 Surface roughness measurement

principle by non-contact method 1.7 23

8 Number of papers/ journals published

in year 2.1(a) 51

9 Number of papers/ journals published

in year 2.1(b) 51

10 Split Bush 2.2 52

11 Block Diagram Steps in Taguchi

Method 3.1 58

12 CNC SPINNER 15 4.1 61

13 ( CNMG120408 & CNMG120412)

Tool Inserts 4.2 63


x

14 VM 6360 Vibration Meter 4.3 65

15 Figure 4.4:-Experimental setup 4.4 66

16 Surface roughness Measurement and

Final Work pieces 4.5 70

17 Main Effect Plot for S/N Ratio

(Surface Roughness) 5.1 73

18 Main Effect Plot for S/N Ratio (Noise

factors) On Surface Roughness 5.2 74

19 Main Effect plot for S/N Ratio

(Tool Life) 6.1 80


factors) On Tool Life 6.2 81

21 Main Effect Plot for S/N Ratio

(MRR) 6.3 82


factors) On MRR 6.4 83


xi

LIST OF FIGURES/GRAPHS

SR. NO. NAME OF THE

FIGURE/GRAPH

FIG/GRA

PH NO.

PAGE

NO.

1 Adjustable Parameter in

Turning Operation 1.1 2

2 Turning process 1.2 5

3 Geometry of a single point

turning tool 1.3 8

4 Idealized model of surface

roughness 1.4 19

5 Surface roughness profiles. 1.5 20

6

Schematic diagram of surface

roughness measurement

technique by Stylus

equipment

1.6 22

7

Surface roughness

measurement principle by

non-contact method

1.7 23

8 Number of papers/ journals

published in year 2.1(a) 51

9 Number of papers/ journals

published in year 2.1(b) 51

10 Split Bush 2.2 52

11 Block Diagram Steps in

Taguchi Method 3.1 58


xii

12 CNC SPINNER 15 4.1 61

13 ( CNMG120408 &

CNMG120412) Tool Inserts 4.2 63

14 VM 6360 Vibration Meter 4.3 65

15 Figure 4.4:-Experimental

setup 4.4 69

16

Surface roughness

Measurement and Final Work

pieces

4.5 67

17 Main Effect Plot for S/N

Ratio (Surface Roughness) 5.1 73

18

Main Effect Plot for S/N

Ratio (Noise factors) On

Surface Roughness

5.2 74

19 Main Effect plot for S/N

Ratio(Tool Life) 6.1 80

20


Ratio (Noise factors) On Tool

Life

6.2 81

21 Main Effect Plot for S/N

Ratio (MRR) 6.3 81

22


Ratio (Noise factors) On

MRR

6.4 83


xiii

LIST OF ABBREVIATIONS

MQL1 Minimum Quantity Lubrication (Vegetable oil

mixed with water)

MQL2 Minimum Quantity Lubrication (Synthetic oil

mixed with water)

VM-6360 Vibration Meter used to measure vibration in

KHz

Rє Nose Radius in mm

Yi Surface Roughness in μm.

Ti Tool Life in Min

Mi Material Removal Rate in mm3/min

n1, n2, n3 Tool life constant & depends on tool and

work piece material

Carbide Tool

inserts

(ISO Catalog

Number)

D

(mm)

L10

(mm)

S

(mm)

Rє

(mm)

D1

(mm)

CNMG120408CT 12.7 12.9 4.76 0.8 5.16

CNMG120412CT 12.7 12.9 4.76 1.2 5.16

CNMG120415CT 12.7 12.9 4.76 0.4 5.16

CNMG120416CT 12.7 12.9 4.76 1 5.16


xiv

ABSTRACT

Optimization of Multi response is now-a-days mostly used

optimization technique which is better than single response

optimizing technique because all the output is affected at a time by

all the input factors .The objective of this project work is to

determine the optimal setting of cutting parameters (speed(N)m/min

, depth of cut(d) mm, feed(f)mm/rev, Nose Radius(r)mm) ,variation

amplitude(mm/sec2), vibration frequency(kHz) in Cutting tool

Inserts to minimize surface roughness(Ra) and also to increase the

Tool life . In this work the experiment has been carried out on CNC

(SPINNER 15) lathe in dry, Wet and MQL(Minimum Quantity

Lubrication) cutting Condition turning of a commercially used EN

24 grade steel as a work material and carbide insert tool

(CNMG120408 CNMG120412).

The ranges of process cutting parameters are cutting speed

(52,105,158, 210 m/min) feed rate (0.15,0.20,0.25,0.3mm/rev),

depth of cut (0.5,1,1.5,2mm) and the Nose Radius

(0.4,0.8,1,1.2mm). This study highlights use of Taguchi experiment

design to optimize the multi response parameters on turning

operation. For this experiment Taguchi design of experiment was

carried out to collect the data for surface roughness and tool

vibration. The results indicate the optimum values of the input

factors and the results are conformed by a confirmatory test.


xv

In order to produce any product with desired quality by machining,

proper selection of process parameters is essential. Taguchi

parameter design is an important tool for robust design, which

offers a simple and systematic approach to optimize a design for

performance, quality and cost. The Taguchi method of off-line

quality control encompasses all stages of product /process

development. However, the key element for achieving high quality

at low cost is Design of Experiments (DOE). Quality achieved by

means of process optimization is found by many manufacturers to

be cost effective in gaining and maintaining a competitive position

in the world market. This project work describes use and steps of

Taguchi design of experiments and orthogonal array to find a

specific range and combinations of turning parameters like cutting

speed, feed rate and depth of cut, Nose Radius and Cutting

condition to achieve optimal values of response variables like

surface roughness, tool life, material removal rate in turning of Split

Bush of EN24 Material.


xvi



1

1. INTRODUCTION

1.1 INTRODUCTION

Turning is the removal of metal from the outer diameter of a

rotating cylindrical work piece. Turning is used to reduce the

diameter of the work piece, usually to a specified dimension, and to

produce a smooth finish on the metal. Often the work piece will be

turned so that adjacent sections have different diameters. Turning is

the machining operation that produces cylindrical parts. In its basic

form, it can be defined as the machining of an external surface:

❖ With the work piece rotating.

❖ With a single-point cutting tool, and

❖ With the cutting tool feeding parallel to the axis of the work

piece and at a distance that will remove the outer surface of

the work.

Taper turning is practically the same, except that the cutter path is at

an angle to the work axis. Similarly, in contour turning, the

distance of the cutter from the work axis is varied to produce the

desired shape. Even though a single-point tool is specified, this does

not exclude multiple-tool setups, which are often employed in

turning. In such setups, each tool operates independently as a

single-point cutter.



2

Figure 1.1:- Adjustable Parameter in Turning Operation

The challenge of modern machining industries is mainly focused on

the achievements of high quality in terms of work piece

dimensional accuracy ,surface finish, high production rate, less

wear on the cutting tool, Maximum Tool Life ,economy of

machining in terms of cost saving and increase the performance of

the product with reduced environmental impact. Surface roughness

plays an important role in many areas and is factor of great

importance in evaluation of machining accuracy. Turning is the

process whereby a single point cutting tool removes unwanted

material from the cylindrical work piece and the tool is fed parallel

to the axis of rotation. It can be done manually, in a traditional form

of lathe, which frequently requires continuous supervision by the

operator, or by using a computer controlled and automated lathe. In

turning operation vibration is a frequent problem .Vibration in

machine tool is directly affecting the surface finish of the work



3

material in turning process. So, vibration of a machine tool is one of

the major factors limiting its performance. The vibration occurring

in the machine tool is due to the dynamic nature of force acting

during the turning operation on the cutting tool Vibration can be

measured in terms of Vibration peak Amplitude (mm/sec2) and

Vibration Frequency in Khz.

Achieving a desired level of surface quality for turned parts requires

practical knowledge and skill to properly set up this type of

operation with the given specifications and conditions. A

manufacturing engineer or machine setup technician is often

expected to utilize experience and published shop guidelines for

determining the proper machining parameters to achieve a specified

level of surface roughness. This must be done in a timely manner to

avoid production delays, effectively to avoid defects, and the

produced parts monitored for quality. Therefore, in this situation, it

is prudent for the engineer or technician to use past experience to

select parameters which will likely yield a surface roughness below

that of the specified level, and perhaps make some parameter

adjustments as time allows or quality control requires. Engineers

and technicians establishing such an operation would ideally

consider other implications of setup parameters such as production

schedules, processing time, and noise factors. A more methodical,

or experimental, approach to setting parameters should be used to

ensure that the operation meets the desired level of quality with

given noise conditions and without sacrificing production time.

Rather than just setting a very low feed rate to assure a low surface


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