OPTIMIZATION OF MACHINING PARAMETERS WITH ...

Dissertation On

OPTIMIZATION OF MACHINING PARAMETERS WITH TOOL INSERT SELECTION FOR S355J2G3 MATERIAL USING TAGUCHI AND MADM METHODS Submitted in partial fulfilment of the requirement for the award of the degree Of Master of Technology In Mechanical Engineering with specialization in Manufacturing Engineering By

Avinash Juriani Admission No. 14MT000354

Under the guidance of

  Dr. Somnath Chhattopadhyay Associate Professor Department of Mechanical Engineering   Indian School of Mines, Dhanbad  

&

Mr. Shyam Sundar Mishra Assistant Manager Operations Department JSPL-Machinery Division Raipur

 

  Department of Mechanical Engineering Indian School of Mines Dhanbad-826004, Jharkhand, India May 2016

 

ACKNOWLEDGEMENT To accomplish a successful task, a man requires many hands to achieve it with perfect guidance. It is indeed a pleasure for me to express my sincere gratitude to those who have always helped me for this dissertation work .In particular, I express my gratitude and indebtedness to my thesis supervisors Dr.Somnath Chhattopadhyaya, Associate Professor Department of Mechanical Engineering, ISM Dhanbad and Mr. Shyam Sundar Mishra, Assistant Manager, Operations Department, JSPL Machinery Division, Raipur for kindly providing me to work under their supervision & guidance. I express sincere thanks to them for valuable guidance, encouragement, moral support, and affection & kind co-operation throughout the course of my work which has been a key in the success of thesis

I am heartly thankful and express deep sense of gratitude to Shri Dalbir Singh Rekhi General Manager – HR & IE JSPL Raigarh for extending all possible help in carrying out the Project directly or indirectly. I express my sincere gratitude to Dr. Somnath Chhattopadhyaya, Dr. Alok Kumar Das for their timely guidance throughtout the.course period. I am also thankful to Dr. R.K. Das HOD Department of Mechanical Engineering, ISM Dhanbad and the staff members of ISM Dhanabad & JSPL – Machinery Division Raipur for their indebted help in carrying out experimental work & valuable suggestions I express my sincere gratitude to Shri Suryodaya Dubey HR & ES & Shri Rajesh Nayak, Deputy Manager, JSPL Machinery Division, Raipur to grant the permission to carry out the entire thesis work at their esteemed organization. They have been great source of inspiration to me and I thank them from bottom of my heart. I am especially indebted to my parents for their love, sacrifices and support. They are my teachers after I came to this world and have set great example for me about how to live, study, work and to walk on an untraded path in the quest of knowledge.

AVINASH JURIANI 14MT000354

i

Abstract The current scenario of globalization focuses mainly on using modern computerized machines for high quality. This new era of manufacturing industries aims at high productivity, good surface finish, and better accuracy with high production rate CNC machines lead a major in a manufacturing industry, machining comprises of wide variety of operations with turning operation being the most important one. This operation is used for producing shafts, rollers and many other cylindrical components. In turning operation performance specifications of concern include surface finish, material removal rate & tolerance which are mostly affected by different machining parameters like machining condition, work piece, tool geometry and operating parameters. This project presents the optimization of various operating parameters as velocity, feed and depth of cut to obtain lower surface finish & high material removal rate for high productivity. The experiment was conducted on CNC lathe with S355J2G3 material. The key goal is to minimize response variations keeping the process to be monitored consistently irrespective of the environment used. In this analysis Taguchi methods along with Minitab 17 is used for optimization. Taguchi method involves use of orthogonal array design to assign the factors, chosen for experiment. Taguchi’s statistical analysis is employed for single optimization as it provides an effective method to select the control factor levels (velocity, feed and depth of cut) which effect the noise factors on the responses surface roughness & material removal rate. Multi objective optimization for getting good quality of surface roughness & material removal rate is carried out using Grey Relational Analysis. The analysis of variance (ANOVA) is carried out to determine the contribution of each control parameter on surface roughness & material removal rate. MADM (multiple attribute decision making) methods is employed to select tool insert to get better surface finish & material removal rate for given constant cutting parameters. The combination of tool insert selected is justified by experimentation work.

Keywords: Machining Parameters, S355J2G3 Steel, Design of Experiment (DOE),Surface Roughness, Material Removal Rate, Taguchi Method, Orthogonal Array, Grey Relation Analysis (GRA), Analysis of Variance (ANOVA), MADM Method.

ii

Table of Contents S.

Chapter title

Page

No.

No. Certificate from JSPL-Machinery Division Certificate from ISM Acknowledgements

i

Abstract

ii iii- iii

Table of Contents

1

List of figures

iii- iii

List of Tables

iii

Glossary of Terms & Nomenclature

iii

Introduction to Project & Objectives

1-21

1.1

Introduction

1

1.2

Steel

2

1.2.1 Steel Material S355J2G3

2

Lathes

3

1.3.1 CNC Machine

3

1.3.2 Operational features of CNC Machine Tools

4

1.3.3 Classification of CNC Machine Tools

5

Turning Process

5

1.4.1 Mechanism of Cutting

6

Types of Cutting Tool Materials

7

1.5.1 Properties of Ideal Cutting Tool Materials

7

1.5.2 Tool Materials

7

1.5.3 Cutting Tool Inserts

9

1.5.4 Basic Insert Selection Factors

12

1.5.5 Cutting Tool Geometry

13

Types of Turning Operations

15

1.6.1 Operation Types

15

1.3

1.4

1.5

1.6

iii

1.7

Cutting Parameters in Turning Operation

19

1.7.1 Speed

19

1.7.2 Feed

19

1.7.3 Depth of Cut

19

1.8

Advantages & Disadvantages of CNC Turning

20

1.9

Research objectives

20

1.10

Plan of Presentation

21

Literature review

22-31

2 2.1

Introduction

22

2.2

Investigation on Turning & Optimization

23

2.3

Investigation on Inserts

28

2.4

Gaps Identified In Literature

31

3

Design of Experiment & Methodology 3.1

3.2

3.3

32-50

DOE Overview

32

3.1.1 Stages of Design of Experiment

32

3.1.2 Procedures for Designing Experiments

33

3.1.3 Advantages of DOE

34

3.1.4 Applications of DOE

34

Taguchi Technique for Single Objective Optimization

34

3.2.1 Loss Function

35

3.2.2 Features of the Loss Function

36

3.2.3 Average Loss Function for Product Population

36

3.2.4 Signal to Noise (S/N) Ratio

37

3.2.5 Assortment of Orthogonal Arrays

39

3.2.6 Advantages of Taguchi Technique

40

3.2.7 Drawbacks of Taguchi Technique

40

Grey Relational Analysis

41

3.3.1 Data Pre Processing

41

3.3.2 Grey Relational Coefficient & Grey Relational Grade

42

iv

3.3.3 Advantages of Grey Relational Analysis

43

3.4

Analysis of Variance (ANOVA)

43

3.5

MADM Multi Attribute Decision Making)

46

3.5.1 Simple Additive Weighting (SAW) Method

47

3.5.2 Weighted Product Method (WPM)

50

Optimization

50

3.6

4

Experimental Details

51-63

4.0

Experimental Overview

51

4.1

Materials

52

4.1.1 Selection of Worpiece Material

52

4.1.2 Applications of the Selected Material

53

4.2

Selection of Cutting Tool

53

4.3

Machine Tool

54

4.3.1 CNC Turning Lathe

54

Surface Roughness

56

4.4.1 Factors Affecting the Surface Finish

56

4.4.2 Portable Surface Roughness Tester

56

4.5

CNC Turning Operations

58

4.6

Surface Finish Measurement

59

4.6.1 Measuring Procedure

59

Hardness Measurement

62

4.4

4.7

5

Results & Analysis

64-85

5.0

Selection of Process Variables

64

5.1

Selection of Orthogonal Array

65

5.2

Single Objective Optimization of Surface Roughness

66

5.2.1 Calculation of S/N ratio for Surface Roughness

66

5.2.2 Signal to Noise Ratio for Surface Roughness

66

5.2.3 Main effects plot of Surface Roughness

67

Single Objective Optimization of Material Removal Rate

68

5.3

v

5.3.1 Calculation of S/N ratio for Material Removal Rate

68

5.3.2 Signal to Noise Ratio for Material Removal Rate

68

5.3.3 Main Effects Plot of Material Removal Rate

69

Multi Objective Optimization of Process Parameter

70

5.4.1 Process Steps for Multi-Response Optimization

70

5.4.2 Grey Relational Generation

70

5.4.3 Calculation of Deviation Sequence

71

5.4.4 Calculation of GRC & GRG

72

5.4.5 Response Table for Grey Relational Grade

75

5.4.6 Main effects plots of Grey Relational Grade

76

5.5

ANOVA Analysis of Grey Relational Grade

77

5.6

Confirmation Test

80

5.7

Implementation of MADM methods

80

5.7.1 Simple Additive Weighting (SAW) Method

80

5.7.2 Weighted Product Method (WPM)

83

5.8

Comparison result

84

5.9

Discussion

84

5.4

6

7

Conclusions

86-87

6.1

Experimentation Summary

86

6.2

Future Scope

87

References

88-91

vi

List of Figures Fig.

Title

No.

Page No.

1.1

Classification of Lathes

3

1.2

CNC Turning Lathe

4

1.3

CNC Operational Feature

4

1.4

Classification of CNC Machines

5

1.5

Turning Process

5

1.6

Chip Deformation Zones

6

1.7

Shearing Action of Chip

6

1.8

Various Inserts

8

1.9

Different Insert Shapes

9

1.10

Grades of Material

12

1.11

Geometry of Single Point Cutting Tool

13

1.12

Turning Operation

15

1.13

Facing Operation

16

1.14

Grooving Operation

16

1.15

Threading Operation

17

1.16

Boring Operation

17

1.17

Reaming Operation

18

1.18

Tapping Operation

18

3.1

(a) Taguchi Loss Function (b) Traditional Approach

37

3.2

(a) Smaller the better (b) Larger the better

38

3.3

Classification of Optimization Types

50

4.1

Experimental Flowchart

51

4.2

(a) Optimization WorkPiece (b) MADM WorkPiece

52

4.3

(a) Tool Holders with Inserts (b) Inserts Exaggerated View

53

4.4

CNC Turning Machine

54

4.5

Pictorial View with WorkPiece Mounted

54

4.6

Control Panel of PUMA 400 MB

55

4.7

Portable Surface Roughness Tester

57 vii

4.8

Tool Movement

59

4.9

Turning Program

59

4.10

Calibration Specimen

60

4.11

Calibration

60

4.12

Photographs of Surface Roughness Measured by SurfTest SJ201P

62

4.13

a) Initially placing b) Hardness Measured

63

4.14

a) SR of Tool Combination Second b) SR of Tool Combination Third

63

5.1

Main Effects Plot of Surface Roughness for SN ratios

67

5.2

Main effects plot of Material Removal Rate for SN ratios

69

5.3

Graph for Grey Relational Grade

75

5.4

Main effects plot of Grey Relational Grade for SN ratios

76

5.5

Pie Chart for Percentage Contribution

79

5.6

Comparison of Performance Scores

84

viii

List of Tables Table

Title

No.

Page No.

3.1

Selection of Orthogonal Arrays

39

3.2

Quality Characteristics of the Machining Performance

41

3.3

ANOVA Table Generation

45

3.4

Saaty’s scale for Pair Wise Comparisons

48

3.5

Random Index (RI) Values

48

4.1

Chemical Composition

52

4.2

Mechanical Properties

52

4.3

Insert Dimensions

53

4.4

Specifications of CNC Turning Machine

55

4.5

Technical Specifications of SJ-201P

57

4.6

Control Factors and Setting Range

58

5.1

Process Parameters with Different Levels

64

5.2

Experiment Design by use of L16 Orthogonal array

65

5.3

Orthogonal Array L16 with S/N Ratio for Surface Roughness

66

5.4

Response Table for S/N Ratio (SR)

67

5.5

Orthogonal Array L16 with S/N Ratio for Material Removal Rate

68

5.6

Response Table for S/N Ratio (MRR)

69

5.7

Data Normalization of Experimental Result

71

5.8

Deviation Sequence

72

5.9

Calculated Grey Relational Co-efficient & Grey Relational grade

73

5.10

Grey Relational Grades with Order

74

5.11

Average Grey Relational Grade by Factor Level

75

5.12

Response Table for Factor Level

76

5.13

ANOVA Generation (By Minitab17)

77

5.14

Optimization Results

80

5.15

Attributes of CNC turning Inserts

82

5.16

Normalized Values of CNC Turning Inserts

83

ix

Glossary of Terms & Nomenclature ANOVA

Analysis of Variance

CNC

Computerized Numerical Control

DOE

Design of Experiments

DOF

Degree of Freedom

GRA

Grey Relational Analysis

GRC

Grey Relational Coefficient

GRD

Grey Relational Grade

MADM

Multi Attribute Decision Making

MINITAB

Statistical Software

MRR

Material Removal Rate

MSD

Mean Square Deviation

n

Total Number of Runs (For this work n=16)

S/N

Signal to Noise Ratio

SR

Surface Roughness

Symbols used

Description of the Symbols used

Nomenclature

V

Speed

[m/min]

F

Feed Rate

[mm/sec]

D

Depth of Cut

[mm]

Di

Initial Diameter

[mm]

Df

Final Diameter

[mm]

x

Chapter 1 Introduction to Project & Objectives  

Chapter 2 Literature Survey

 

Chapter 3                                                                    

Design of Experiments & Methodology  

 

Chapter 4 Experimentation Setup  

Chapter 5 Results & Analysis    

Chapter 6 Conclusions & Future Scope

Chapter 7 References