CONTENTS

ABSTRACT i

LIST OF FIGURES viii

LIST OF TABLES xiii

NOMENCLATURE xiv

CHAPTER-I INTRODUCTION 1

1.1 Tool Wear and its Forms 1

1.2 Significance of Cutting Fluids 3

1.3 Historical Development of Cutting Fluids 4

1.4 Types of Cutting Fluids 4

1.4.1 Straight Oils 5

1.4.2 Water Soluble Oils 6

1.4.3 Synthetic Fluids 6

1.4.4 Semi-Synthetic Fluids 7

1.5 Environmental Aspects of Cutting Fluids 8

1.6 Present Work 8

1.7 Thesis Layout 9

CHAPTER-II LITERATURE REVIEW 11

2.1 Introduction 11

2.2 Tool Wear 11

2.3 Cutting Forces 14

2.4 Cutting Temperatures 17

2.5 Influence of Cutting Fluids on Machining Processes 18

2.5.1 Influence on Cutting Temperatures 18

2.5.2 Influence on Cutting Forces 19

2.5.3 Influence on Product Quality and Tool Wear 20

2.6 Cutting Fluids as Quenchants 25

2.7 Tool Wear Prediction Models 25

iii

2.7.1 Mathematical Models 25

2.7.2 Neural Network Models 26

2.8 Environmental Aspects of Cutting Fluids 27

2.8.1 Microbial Contamination 29

2.8.2 Identification of Bacterial Species 31

2.8.3 Use of Biocides 32

2.8.4 Mist Generation 33

2.8.5 Disposal of Used Cutting Fluid 34

2.9 Summary 36

CHAPTER-III EXPERIMENTAL SET-UP AND EXPERIMENTATION 37

3.1 Introduction 37

3.2 Evaluation of Cutting Fluid Properties 37

3.2.1 Water Separability 37

3.2.2 Measurement of Kinematic Viscosity 38

3.2.3 Measurement of Thermal Conductivity 38

3.2.4 Measurement of Flash and Fire Points 39

3.2.5 Measurement of pH Value 39

3.3 Evaluation of Cutting Fluids’ Performance 39

3.3.1 Workpiece Material 40

3.3.2 PSG-124 Lathe 40

3.3.3 Supply of Cutting Fluid 41

3.3.4 Cutting Tool 41

3.3.4.1 Cemented Carbide Insert and Tool Holder 41

3.3.4.2 High Speed Steel Tool 42

3.3.5 Measurement of Cutting Forces 42

3.3.6 Measurement of Cutting Temperatures 42

3.3.7 Measurement of Tool Wear 42

3.3.8 Measurement of Surface Roughness 43

3.4 Assessment of Quenching Effects of Cutting Fluids 44

3.4.1 Measurement of Hardness of Samples 44

iv

3.4.2 Metallographic Studies of Samples 44

3.4.3 Jominy End Quench Test 44

3.5 Determination of Microbial Contamination 44

3.5.1 Arrangement for Simulation of Working Conditions 45

3.6 Summary 47

CHAPTER-IV RESULTS AND DISCUSSIONS 48

4.1 Introduction 48

4.2 Basic properties of Cutting Fluids 48

4.2.1 Water Separability 48

4.2.2 Kinematic Viscosity 48

4.2.3 Thermal Conductivity 50

4.2.4 Flash and Fire Points 51

4.2.5 pH value 52

4.3 Performance of Cutting Fluids in Machining 52

4.3.1 Tool Wear 52

4.3.2 Cutting Temperatures 54

4.3.3 Cutting Forces 58

4.3.4 Surface Roughness 59

4.3.5 Hardness 60

4.4 Microbial Contamination 63

CHAPTER-V TOOL WEAR PREDICTION MODELS AND VALIDATION 67

5.1 Introduction 67

5.2 Mathematical Regression Model 67

5.3 Analysis of Variance (ANOVA) 69

5.4 Artificial Neural Networks 71

5.4.1 Network Architecture Optimisation 71

5.5 Validation of Proposed Models 73

5.6 Summary 77

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CHAPTER-VI CONCLUSIONS AND FUTURE SCOPE OF THE WORK 78

6.1 Conclusions 78

6.1.1 Basic Properties of Cutting Fluids 78

6.1.2 Performance of Cutting Fluids in Machining 78

6.1.3 Microbial Contamination of Cutting Fluids 79

6.2 Future Scope of the Work 80

REFERENCES 81

APPENDICES

APPENDIX-A DETAILS OF EQUIPMENT AND APPARATUS A1

A.1 Estimation of Cutting Fluids’ Basic Properties A1

A.1.1 Measurement of Kinematic Viscosity A1

A.1.1.1 Redwood Viscometer-I A1

A.1.1.2 Redwood Viscometer-II A3

A.1.2 Thermal Conductivity Measurement A6 Apparatus for Liquids

A.1.3 Measurement of Flash and Fire Points A8

A.1.4 Measurement of pH Value A9

A.2 Evaluation of Cutting Fluids’ Performance A10

A.2.1 Measurement of Cutting Forces A10

A.2.2 Measurement of Cutting Temperatures A11

A.2.3 Measurement of Tool Wear A13

A.2.4 Measurement of Surface Roughness A14

A.2.5 Assessment of Quenching Effects of Cutting Fluids A15

A.2.5.1 Polishing Equipment A15

A.2.5.2 Micro Hardness Tester A16

A.2.5.3 Metallurgical Microscope A16

A.2.5.4 Muffle Furnace A17

A.2.5.5 Jominy End Quench Test Apparatus A18

A.3 Determination of Microbial Contamination A19

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A.3.1 Petri Plates A19

A.3.2 Laminar Air Flow Cabinet A20

A.3.3 B.O.D Incubator A21

APPENDIX-B ARTIFICIAL NEURAL NETWORKS B1

B.1 Introduction B1

B.2 Back Propagation Network B1

B.2.1 Weight Structure B2

B.2.2 Normalization B2

B.2.3 Training B3

B.2.4 Testing B6

B.2.5 Denormalisation B7

PUBLICATIONS FROM THE PRESENT WORK

CURRICULUM VITAE

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LIST OF FIGURES

Fig.No. Description of Figure Page No.

1.1 Various forms of tool wear 2

1.2 Growth of tool flank wear with machining time 2

1.3 Schematic representation of emulsifier molecule 6

2.1 Variation of shear and normal stresses on tool face 12

2.2 Variation of specific energy (u) with undeformed chip thickness (t) when machining mild steel

13

2.3 Variation of specific wear parameter (Bw/PAL) with undeformed chip thickness (t)

13

2.4 Variation of specific wear parameter with undeformed chip thickness 14

2.5 Force system in metal cutting 15

2.6 Colwell’s observations for a sharp tool 15

2.7 Estimated tool flank temperatures 17

2.8 Cooling curves of different fluids 19

2.9 Comparison of vegetable emulsion (V), synthetic oil (S) and mineral oil emulsions (M)

21

2.10 Phases of growth in a bacterial culture 30

2.11 Comparison of dissolved oxygen (٠) and plate count (+) for a semi-synthetic fluid

31

3.1 Water separability in the samples (after 30 min) 37

3.2 Experimental set up on PSG-124 lathe 40

3.3 Tool holder with provision for thermocouple 41

3.4 Projected tool profile 43

3.5 Individual components of heating element 45

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Fig.No. Description of Figure Page No.

3.6 Assembled heating element 45

3.7 Arrangement of heating elements 46

3.8 Pumping of fluids on the heating elements 46

4.1 Progress of tool flank wear with machining time for HSS tool 53

4.2 Progress of tool flank wear with machining time for cemented carbide tool

53

4.3 Variation of nodal temperatures with machining time while using HSS tool

54

4.4 Variation of nodal temperatures with machining time while using cemented carbide tool

55

4.5 Finite element model of HSS tool 55

4.6 Finite element model of cemented carbide tool 56

4.7 Estimation of tool tip temperatures for HSS tool 56

4.8 Estimation of tool tip temperatures for cemented carbide tool 57

4.9 Extrapolated tool tip temperatures under dry cutting while using HSS and cemented carbide tools

57

4.10 Variation of cutting force with machining time while using HSS tool 58

4.11 Variation of cutting force with machining time while using cemented carbide tool

59

4.12 Variation of surface roughness with machining time while using HSS tool

60

4.13 Variation of surface roughness with machining time while using cemented carbide tool

60

4.14 Hardness of samples machined with HSS tool 61

4.15 Hardness of samples machined with cemented carbide tool 61

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Fig.No. Description of Figure Page No.

4.16 Structure of Sample Machined with H.S.S. Tool 62

4.17 Structure of Sample Machined with Carbide Tool 62

4.18 Results from Jominy end quench test while employing different quenchants

63

4.19 Colony growth in stored samples 63

4.20 Colony growth in working samples 64

4.21 Microbial growth in stored samples 64

5.1 Variation of hardness at quenched end with thermal conductivity 68

5.2 Variation of thermal conductivity with emulsifier content 68

5.3 Comparison of predicted and measured hardness values while using HSS and cemented carbide tools

69

5.4 Optimisation of number of hidden layers in neural networks 72

5.5 Optimisation of number of iterations in neural networks 72

5.6 Comparison of predicted and experimental values while using HSS tool and cutting fluid with 10% emulsifier content

73

5.7 Comparison of predicted and experimental values while using HSS tool and cutting fluid with 20% emulsifier content

73

5.8 Percentage errors in regression model predictions for HSS tool 74

5.9 Percentage errors in neural network predictions for HSS tool 74

5.10 Comparison of predicted and experimental values while using cemented carbide tool and cutting fluid with 10% emulsifier content

75

5.11 Comparison of predicted and experimental values while using cemented carbide tool and cutting fluid with 20% emulsifier content

75

5.12 Percentage errors in regression model predictions for cemented carbide tool

76

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Fig.No. Description of Figure Page No.

5.13 Percentage errors in neural network predictions for cemented carbide tool

76

A.1 Redwood viscometer-I A1

A.2 Comparison of actual and measured kinematic viscosity of water A2

A.3 Error in measurement of kinematic viscosity of water A2

A.4 Comparison of actual and measured kinematic viscosity of SAE 20 oil

A3

A.5 Error in measurement of kinematic viscosity of SAE 20 oil A3

A.6 Redwood viscometer-II A4

A.7 Comparison of actual and measured kinematic viscosity of glycerin A4

A.8 Error in measurement of kinematic viscosity of glycerin A5

A.9 Comparison of actual and measured kinematic viscosity of engine oil A5

A.10 Error in measurement of kinematic viscosity of engine oil A6

A.11 Thermal conductivity measurement apparatus A6

A.12 Comparison of actual and measured thermal conductivity of water A7

A.13 Error in measurement of thermal conductivity of water A7

A.14 Comparison of actual and measured thermal conductivity of glycerin A8

A.15 Error in measurement of thermal conductivity of glycerin A8

A.16 Cleveland open cup tester A9

A.17 pH meter A9

A.18 Lathe tool dynamometer A10

A.19 Comparison of applied and measured load on lathe tool dynamometer

A11

A.20 Error in measurement on lathe tool dynamometer A11

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Fig.No. Description of Figure Page No.

A.21 Digital temperature indicator A12

A.22 Comparison of actual and measured temperatures in thermocouple A12

A.23 Error in measurement of temperatures A13

A.24 Optical projector A13

A.25 Surface roughness tester A14

A.26 Standard specimen for calibrating surface roughness tester A14

A.27 Single disc polishing machine A15

A.28 Microhardness tester A16

A.29 Metallurgical microscope A17

A.30 Muffle furnace A17

A.31 Jominy end quench apparatus A18

A.32 Schematic representation of sample in Jominy end quench test A18

A.33 Petri plate A19

A.34 Laminar air flow cabinet A20

A.35 B.O.D. Incubator A21

B.1 Back propagation neural network training phase B2

B.2 Training in back propagation neural network B4

B.3 Global and local minima B5

B.4 Testing in back propagation neural network B6

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LIST OF TABLES

Table.No. Description of Table Page No.

4.1 Water separability of fluids 49

4.2 Kinematic viscosity of concentrated mixtures, × 10-4 m2/s 50

4.3 Kinematic viscosity of cutting fluids (95% water), ×10-4 m2/s 50

4.4 Thermal conductivity of the fluids with varying content of SPS, W/m-K

51

4.5 Variation of flash and fire points with percentage of SPS 51

4.6 Variation of pH value with percentage of SPS 52

4.7 Identification of bacterial species 65

5.1 Prediction model from regression analysis 70

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NOMENCLATURE

ENGLISH ALPHABETS

Symbol Description

a Activation of neuron

A, B Constants for Redwood viscometers

Bw Volume of material worn, mm3

C CBN content in tool, percentage volume

E Edge radius of CBN tool, mm

e Emulsifier content, %

E(Italics) Error in neural network

f Feed, mm/min

FC Resultant cutting force, N

F F-statistic (variance ratio)

Fx Feed force, N

Fy Radial force, N

Fz Main cutting force, N

H Hardness, MPa

h Heat transfer coefficient, W/m2-K

I Input to neuron layer

k Thermal conductivity, W/m-K

K Mathematical constant

k (Italics) Number of the present iteration of neural network

L Sliding distance, cm

LC Cutting length in axial direction, mm

LHT Average length of the diagonals of indentation, mm

Nw Wear number

O Output of neuron

P p-value in ANOVA

PA Applied load, N

R Coefficient of regression

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Ra Surface Roughness, µm

t Undeformed chip thickness, µm

T t-value in ANOVA

TC Cutting temperature, 0C

tm Machining time, min

Tr Time required for collection of 60 ml, s

u Specific energy, µN/cm2

V Cutting speed, m/min

VB Flank wear, mm

W (Italics) Weight structure

GREEK ALPHABETS

Symbol Description

HT Angle between the opposite faces of the diamond, degrees

(Italics) Bias of neuron

(Italics) Learning rate of neural network

(Italics) Momentum of neural network

Δ Difference

μ Coefficient of friction

σc Normal Stress, N/mm2

τc Shear Stress, N/mm2

υ Kinematic viscosity, m2/s

ABBREVIATIONS

AISI American Iron and Steel Institute

ANN Artificial Neural Network

ANOVA Analysis of Variance

ASME American Society of Mechanical Engineers

ASTM American Society for Testing and Materials

CBN Cubical Boron Nitride

DF Degrees of Freedom

xv

EP Extreme Pressure

EPA Environmental Protection Agency

HP Hypersensitivity Pneumonitis

HPLC High Pressure Liquid Chromatography

HRC Rockwell C Hardness

HSS High Speed Steel

ISO International Organization for Standardization

PCB Polychlorinated Biphenyls

SPS Sodium Petroleum Sulphonate

SS Sum of Squares

v/v volume/volume (ratio)

SUBSCRIPTS

Symbol Description

HT Related to Micro Hardness Tester

i, j Successive Neuron layers

w Related to tool wear

X,Y,Z Axes

1, 2, 3 … Number of the parameter under consideration

xvi