EXPERIMENTAL STUDIES ON MICROTURNING

A THESIS Submitted by

J. HARI PRASAD In partial fulfillment for the award of the Degree of

MASTER OF TECHNOLOGY IN MECHANICAL ENGINEERING (MANUFACTURING TECHNOLOGY) Under the guidance of

Dr. JOSE MATHEW

DEPARTMENT OF MECHANICAL ENGINEERING NATIONAL INSTITUTE OF TECHNOLOGY CALICUT NIT CAMPUS PO, CALICUT KERALA, INDIA 673601 MAY 2007

ACKNOWLEDGEMENT First of all, I thank almighty for empowering me to complete the project work successfully. I wish to express my sincere gratitude to Dr. Jose Mathew, Professor, Department of Mechanical Engineering, NIT Calicut whose timely guidance, providing required amenities and inspiration helped me in completion of this project. I am thankful for his valuable suggestions in completing this script. I extend my thanks to Dr. K. Allesu, Professor, Course Coordinator, Manufacturing Technology, Department of Mechanical Engineering, NIT Calicut for his constant encouragement. I thank Dr.P.V. Ramachandran, Professor and Head, Department of Mechanical Engineering, NIT Calicut, for his support to complete this project. I obliged to Dr. Lim (MikroTools, Singapore), for his suggestions and kind information during the work. I am grateful to all the staff members of Production Engineering Lab, Mechanical Workshops for their kind help, assistance and cooperation during the experimental work. I also thank all the faculty members and staff members of the Department of Mechanical Engineering for their cooperation and support. Also sincere thanks to all my friends for their support and help during the course of my project work.

J. Hari Prasad

DECLARATION

"I hereby declare that this submission is my own work and that, to the best of my knowledge and belief, it contains no material previously published or written by another person nor material which has been accepted for the award of any other degree or diploma of the university or other institute of higher learning, except where due acknowledgment has been made in the text.”

Place: Calicut

Signature

Date Name: J. Hari Prasad Reg. No: M050110MT

CERTIFICATE This is to certify that the thesis entitled “Experimental Studies on Microturning” submitted by Mr. J. Hari Prasad to the National Institute of Technology Calicut towards partial fulfillment of the requirements for the award of the Degree of Master of Technology in Mechanical Engineering (Manufacturing Technology) is a bonafide record of the work carried out by him under my supervision and guidance.

Dr. Jose Mathew (Thesis Supervisor) Professor, Department of Mechanical Engineering, National Institute of Technology Calicut.

Professor and Head, Department of Mechanical Engineering, National Institute of Technology Calicut.

Place: Calicut Date:

CONTENTS TITLE

CHAPTER – 1

CHAPTER – 2

Page No. ABSTRACT

i

NOMENCLATURE

iii

LIST OF FIGURES

iv

LIST OF TABLES

viii

INTRODUCTION

1-3

1.1 Background

1

1.2 Motivation and problem statement

2

1.3 Dissertation outline

2

LITERATURE REVIEW

4-17

2.1 Introduction

4

2.1.1 Micromachining-Unit removal

4

2.1.2 Micromachining-Equipment precision

5

2.1.3 Micromachining-Sufficient condition

5

2.2 Types of micromachining processes 2.2.1 Typical micromachining technologies

5 6

2.3 Advantages/challenges

6

2.4 Concept of minimum chip thickness

7

2.5 Estimation of minimum chip thickness

8

2.6 Evaluation of critical parameters in micro-

9

cutting 2.6.1 Size effect

10

2.6.2 Built-up edge elastic-plastic

11

deformation 2.6.3 Elastic-plastic deformation

11

2.6.4 Microstructure effects

11

2.7 Surface finish in microturning

12

2.8 Micro shaft fabrication

13

CHAPTER – 3

2.9 Application of microturning

14

2.10 Summary and conclusions

16

2.11 Research objectives

17

MICROTURNING: CONCEPTS AND

18-21

PROCEDURES

CHAPTER – 4

3.1 Introduction

18

3.2 Process development

18

3.3 Microturning cutting path schemes

19

3.4 Forces in microturning

20

EXPERIMENTATION 4.1 Machine tool

22

4.2 Workpiece and cutting tool

23

4.3 Dynamometer and workpiece setup

23

4.4 Minidyn 9256C2

23

4.4.1 Description

23

4.4.2 Principle of operation

25

4.5 DynoWare

CHAPTER – 5

22-34

25

4.5.1 Description

27

4.5.2 Analysis

27

4.5.2.1 Mean value analysis

27

4.5.2.2 Cursor tool analysis

27

4.6 Machining process

28

4.7. Design of experiments

32

RESULTS AND DISCUSSION

35-60

5.1 ANOVA analysis for cutting force

35

5.2 Diagnostic results for cutting force

36

5.3 ANOVA analysis for thrust force

43

5.4 Diagnostic results for thrust force

44

5.5 Model graphs

50

5.6 SEM analysis

58

CHAPTER – 6

CONCLUSIONS AND SCOPE FOR FUTURE

61-62

WORK 6.1 Conclusions

61

6.2 Scope for future work

62

REFERENCES

63

ABSTRACT The last decade has shown an ever-increasing interest in higher precision and miniaturization in a wide range of manufacturing activities. These growing trends have led to new requirements in machining, especially in micromachining. It is the key technology of micro engineering to produce miniature components and micro products. Micromachining technology using photolithography on silicon substrate is one of the key processes used to fabricate microstructures. But the microproducts produced by photolithography have the limitations of low aspect ratio and quasi-3D structure. However, high aspect ratio products with 3D submicron structure can be possible to fabricate by deep X- ray lithography process and focus ion beam machining process. But, these are slow processes, and require special facilities. The advancement in machine tool technology especially with the development of highly precise CNC machines also helps to achieve very fine shapes with high accuracy. In this regard, mechanical fabrication processes using solid tools are useful in terms of realizing complex 3D features on microscale. One group of tool based micromachining technology is microturning. It is a conventional material removal process that has been miniaturized. Microturning has the capability to produce 3D structures on microscale. As solid cutting tool is used in microturning, it can produce definite 3D shapes. The major limitation of this microturning process is that the machining forces influences machining accuracy and the final size that can be achieved. With the available Micromachining Centre (Mikrotools, Singapore Make), no cutting data regarding selection of cutting parameters is available. Hence it has been decided to carry out detailed experimental studies in order to arrive a proper machining condition for standard workpieces. Statistically designed experiments were carried out and two level full factorial experiments were conducted. In order to accurately and precisely control of cutting tool motions during machining, cutting path generation by CNC programming is employed. Turning exercises were done on 6.5 mm brass rods and the experiments were carried out at a cutting speed of 20-60 m/min with feed rate 10-50 mm/min, step cut length of 100-500 µm and depth of cut of 50-200 µm. PCD inserts with 0.1 mm nose

i

radius were used. The effects of depth of cut, feed rate, spindle speed and step cut length on cutting force and thrust force were analyzed. The resulting forces were measured using ‘KISTLER’ three component cutting tool mini dynamometer with DynoWare software and results were analyzed Based on the experiments, a statistical model to predict the cutting and thrust forces for turning brass was developed. It was found that the depth of cut and step cut lengths are the major influential factors in prediction of forces during microturning. Spindle speed and depth of cut contributing 22%, 33% respectively on cutting force. The thrust force was high at higher values of step cut length. The contribution of step cut length, depth of cut on thrust force was 75% and 11% respectively. Key words: micromachining, microturning, microshaft, step cut length.

ii

NOMENCLATURE h

Uncut chip thickness, mm

hm

Minimum chip thickness, mm

Rth

Kinematic surface roughness, µm

Redge

Roughness associated with plastic side flow, µm

Rp

Roughness of the cutting edge, µm Deflection, mm Bending stress, N/mm2

d

Diameter of the workpiece, mm

N

Spindle speed, rpm

f

Feed, mm/min

D

Depth of cut, µm

l

Step cut length, µm

rn

Tool nose radius, µm

Fx

Thrust force, N

Fy

Cutting force, N

Fz

Feed force, N

iii

LIST OF FIGURES Fig. No.

Title

Page No.

Fig. 2.1

Schematic of the effect of the minimum chip thickness

8

Fig. 2.2

Flow chart to determine normalized chip thickness

10

Fig. 2.3

Workpiece deflection in microturning

14

Fig. 2.4

Turning-EDM hybrid machining

15

Fig. 2.5

Effect of step size on step error and deflection

15

Fig. 2.6

Micro-turned parts

16

Fig. 3.1

Turning by parallel cut to workpiece axis

19

Fig. 3.2

Turning by step cutting process

19

Fig. 3.3

Microturning path schemes. (a) Whole element, (b) Step cut, (c) Reverse cutting

20

Fig. 3.4

Force directions in microturning

21

Fig. 4.1

Machine tool

22

Fig. 4.2

Schematic sketch of Experimental setup

24

Fig. 4.3

Experimental setup

24

Fig. 4.4

9256C2 Dynamometer

25

Fig. 4.5

Typical Hardware setup details of charge amplifier.

26

Fig. 4.6

Selection of Dynamometer details

26

Fig. 4.7

Mean Value analysis in DynoWare

27

Fig. 4.8

Cursor tool analysis in DynoWare

28

iv

Fig. No.

Title

Page No.

Fig. 4.9

Microturning operation

29

Fig. 4.10

A typical turned component

29

Fig. 4.11

Selection of cutting conditions in SLICER

30

Fig. 4.12

Loading the workpiece dimensions into SLICER

30

Fig. 4.13

Cutting path scheme generated by SLICER

31

Fig. 4.14

Generated NC program by SLICER

31

Fig. 4.15

Ishikawa diagram

32

Fig. 5.1

Normal Plot of effects for cutting force

37

Fig. 5.2

Half Normal Plot of effects for cutting force

37

Fig. 5.3

Normal plot of residuals for cutting force

38

Fig. 5.4

Diagnostic plot of Predicted Vs Actual values for cutting force

38

Fig. 5.5

Normal Plot of effects for cutting force (after neglecting higher order interaction terms)

40

Fig. 5.6

Half Normal Plot of effects for cutting force (after neglecting higher order interaction terms)

41

Fig. 5.7

Normal plot of residuals for cutting force (after neglecting higher order interaction terms)

41

Fig. 5.8

Diagnostic plot of Predicted Vs Actual values for cutting force (after neglecting higher order interaction terms)

42

Fig. 5.9

Normal Plot of effects for thrust force

44

Fig. 5.10

Half Normal Plot of effects for thrust force

45

Fig. 5.11

Normal plot of residuals for thrust force

45

Fig. 5.12

Diagnostic plot of Predicted Vs Actual values of thrust force

46

Fig. 5.13

Normal Plot of effects for thrust force (after neglecting higher order interaction terms)

48

v

Fig. No.

Title

Page No.

Fig. 5.14

Half Normal Plot of effects for thrust force (after neglecting higher order interaction terms)

48

Fig. 5.15

Normal plot of residuals for thrust force (after neglecting higher order interaction terms)

49

Fig. 5.16

Diagnostic plot of Predicted vs. Actual values of thrust force (after neglecting higher order interaction terms)

49

Fig. 5.17

Variation of Spindle Speed on cutting force

50

Fig. 5.18

Variation of Spindle Speed on thrust force

50

Fig. 5.19

Variation of Depth of cut on cutting force

51

Fig. 5.20

Variation of Depth of cut on thrust force

51

Fig. 5.21

Variation of Feed on cutting force

51

Fig. 5.22

Variation of feed on thrust force

51

Fig. 5.23

Variation of Step cut length on cutting force

52

Fig. 5.24

Variation of step cut length on thrust force

52

Fig. 5.25

Spindle Speed- Depth of cut interaction for cutting force

52

Fig. 5.26

Spindle Speed- Feed interaction for cutting force

53

Fig. 5.27

Spindle Speed- Step cut length interaction for cutting force

53

Fig. 5.28

Depth of cut- Feed interaction for cutting force

54

Fig. 5.29

Depth of cut- Step cut length interaction for cutting force

54

Fig. 5.30

Feed-Step cut length interaction for cutting force

55

Fig. 5.31

Spindle Speed-Depth of cut interaction for thrust force

55

Fig. 5.32

Spindle Speed-Feed interaction for thrust force

56

Fig. 5.33

Spindle Speed-Step cut length interaction for thrust force

57

Fig. 5.34

Depth of cut-Feed interaction for thrust force

57

vi

Fig. No.

Title

Page No.

Fig. 5.35

Depth of cut -Step cut length interaction for thrust force

57

Fig. 5.36

Feed-Step cut length interaction for thrust force

58

Fig. 5.37

SEM image of brass chip (Spindle Speed: 1000 rpm, Depth of cut: 200 m, Feed: 50 mm/min, Step cut length: 500 m)

59

Fig. 5.38

SEM image of brass chip (Spindle Speed: 3000 rpm, Depth of cut: 200 m, Feed: 50 mm/min, Step cut length: 500 m)

59

Fig. 5.39

SEM image of brass chip (Spindle Speed: 3000 rpm, Depth of cut: 50 m, Feed: 50 mm/min, Step cut length: 100 m)

60

Fig. 5.40

SEM image of microshaft of diameter of 0.5 mm (Spindle Speed: 3000 rpm, Depth of cut: 50 m, Feed: 50 mm/min, Step cut length: 500 m)

60

vii

LIST OF TABLES Table No.

Title

Page No.

Table 4.1

Level designation of cutting parameters

33

Table 4.2

Experimental observations

34

Table 5.1

ANOVA analysis table for cutting force

35

Table 5.2

ANOVA analysis for cutting force (After neglecting higher order interaction terms)

40

Table 5.3

ANOVA analysis table for thrust force

43

Table 5.4

ANOVA analysis for thrust force (After neglecting higher order interaction terms)

47

viii

CHAPTER 1 INTRODUCTION 1.1 Background The need for engineered component possessing three-dimensional micro/meso scale features and sub-micron surface finish keeps increasing rapidly in the fields such as optics, die and molds, semiconductor and biomedical devices, etc. Specific applications include micro scale pumps, valves and mixing devices, micro-fluidic systems, micro-molds, micro-holes for fiber optics and micronozzles for high-temperature jets. These applications require very tight tolerances, high quality surface finish. The functional and structural requirements of these devices demand the use of various engineering materials, including aluminum alloys, stainless steel, titanium, brass, plastics, ceramics, and composites. Mechanical micro-cutting is generally defined as a cutting process with uncut chip thickness varying from submicron to a few hundred microns [1]. This microcutting, being an ultra-precision machining process, is becoming increasingly important for its capability of producing parts with three dimensional features ranging from a few microns to a few hundred microns in a wide range of materials. Since the uncut chip thickness is mostly in the order of millimeters in conventional cutting processes, they are commonly classified as macro-scale processes. There are a number of issues in micro-scale cutting that are fundamentally different from macro-scale cutting. They include differences in the underlying mechanisms resulting in changes in the chip formation process, machining forces, specific energy and surface finish. For example, the tool cutting edge geometry becomes comparable in size to the uncut chip thickness, which causes the effective rake angle to be negative. This in turn, causes the ploughing

1

and associated elastic-plastic deformation of the workpiece material to become more dominant factors. Furthermore, at micrometer length scales of material removal, the well known size effect is observed in micro-cutting where the specific cutting energy/force increases non-linearly with decrease in uncut chip thickness [2,3]. However, there exists not much consensus on a phenomenological explanation of size-effect in micro-cutting.

1.2 Motivation and Problem Statement One group of tool based micromachining technology is microturning. It is a conventional material removal process that has been miniaturized. Microturning has the capability to produce 3D structures on microscale. As solid cutting tool is used in microturning, it can produce definite 3D shapes. In order to accurately and precisely control of cutting tool motions during machining, cutting path generation by CNC programming is employed. The major limitation of microturning process is that the machining force influences machining accuracy and the limit of machinable size. Therefore, control of the reacting force during cutting is one of the important factors in improvement of machining accuracy. The value of the cutting force must be lower than that cause plastic deformation of the workpiece. This is an effective method to overcome workpiece deflection in microturning process. With the available Micromachining Centre (Mikrotools, Singapore Make), no cutting data regarding selection of cutting parameters is available. Hence it has been decided to carry out detailed experimental studies in order to arrive a proper machining condition for standard workpieces. In the present study, the effects of depth of cut, feed rate, spindle speed and step cut length on cutting force and thrust force were analyzed.

1.3 Dissertation Outline Chapter 2 reviews the prior work in the field of micro machining processes. Previous attempts to explain the size effect and predict the surface roughness in micro cutting processes are discussed. A detailed description of microturning process is discussed in Chapter 3. The experimental setup and procedures are

2

discussed in Chapter 4. In Chapter 5, the results are analyzed detailed discussion are carried out.

3

CHAPTER 2 LITERATURE REVIEW 2.1 INTRODUCTION When an improvement is required in any machining process, the approach is usually different for every type of machining. The approach towards micromachining has, however, a common base for most existing machining methods. When the following two guidelines set, the approach is almost correctly directed toward micromachining. 1. Unit Removal (UR) 2. Improve equipment precision 3. Sufficient condition

2.1.1 Micromachining -Unit removal The concept of unit removal was introduced as ‘processing unit’ by N. Taguchi to explain the difference in removal phenomena between micromachining and conventional machining [1]. UR defined as the part of a workpiece removed during one cycle of removal action. For example, the volume of material removed from the workpiece by one pulse of discharge is UR in EDM (Electrical Discharge Machining). Depending on the dimensions of interest, UR can be expressed in terms of one, two, or three-dimensional values, i.e., length, area, cross sectional area or volume. Since UR gives the limit of the smallest adjustable dimensions of the product, it should be much smaller than the size of the product. For micromachining, since the object is smaller than 500 µm, UR must be smaller than several micrometers. UR of sub micrometer order is also required when the object size is very small or when high precision of the product is required.

4

2.1.2 Micromachining -Equipment Precision When a miniaturized product is requested, for example, a product whose size is 1/10 of the original one, it is desirable that the dimensional error of the product be likewise reduced to 1/10. Therefore, higher precision of the micromachining equipment is desired although it is often impossible to reduce the dimensional error in proportion to the size of the product.

2.1.3 Micromachining -Sufficient Condition The condition suggested by the two guidelines presented above is evidently necessary for micromachining. However, in many cases, it is also the sufficient condition. If the two requirements, small UR and high equipment precision, were satisfied, micromachining would be possible independent of the type of machining process. Since the theoretical, minimum UR’s possible in most processes are of the nanometer order, micromachining is theoretically possible in most existing machining processes. On the other hand, the theoretical, smallest UR is larger than the size of an atom. This suggests that in micromachining in the lower range of dimensions, for example, 1 to 10 µm, it may be more difficult to achieve the ideal UR and equipment precision because of the influence of this absolute limit.

2.2 TYPES OF MICROMACHINING PROCESSES Since the sufficient condition for realizing micromachining is simple as described above, many types of machining process can be utilized in order to realize micromachining. The basic characteristics of such process are classified according to the machining phenomena [1]. 1. Removal by mechanical force 2. Removal by melting and vaporization 3. Removal by ablation 4. Removal by dissolution 5. Plastic deformation 6. Solidification 7. Lamination

5

2.2.1 Typical Micromachining Technologies 1. Microcutting 2. Micro-EDM 3. Micro LBM 4. Micro-USM 5. LIGA 6. Micromolding/microcasting 7. Micro-ECM 8. Microgrinding 9. Micropunching

2.3. ADVANTAGES/CHALLENGES The motivation for micro-mechanical machining of micro-meso-scale components stems from the translation of macro-process knowledge to the micro-level. Similar to conventional machining operations, micro-mechanical machining shapes the surface of materials using miniaturized cutting tools. Micro-mechanicalmachining techniques bring many advantages to the fabrication of micro-sized features. They do not require the very expensive set-ups of lithographic methods. They can produce micro-components cost effectively because there is no need for expensive masks. The process is suitable for accommodating individual components rather than large batch sizes, and has the ability to monitor the inprocess quality of components so that problems can be corrected during fabrication. It is capable of fabricating 3D free-form surfaces. Moreover, it can process a variety of metallic alloys, composites, polymers and ceramic materials to form functional devices [2]. There are several critical issues associated with micro-fabrication that require a paradigm shift from macro-processes. These issues mainly come from the miniaturization of the components, tools and processes. The performance of miniaturized end mills is greatly influenced by small vibrations and excessive forces, which can be detrimental to the longevity of tools and the control of

6

component tolerances [3]. It is difficult to detect damage to cutting edges and even broken tool shafts. The majority of researchers who have investigated micro machining processes have used cutting force for monitoring or improving the quality of sculptured products [4-6]. In addition, the macro-cutting force predictions based on Merchant’s sharp-edge cutting theorem [7] cannot be used in micro-machining operations due to the effects of edge radius. Another significant challenge for micro cutting is tool/ workpiece interactions. Finally, due to their size, the handling, assembling and testing of small micro-machined components is difficult. Most macro approaches are not applicable in the micro-domain. Very limited work has been published related to micro-machined parts, especially in handling and testing. Since micro components difficult to see and handle manually, special instrumentation and packaging are needed.

2.4 CONCEPT OF MINIMUM CHIP THICKNESS In micromachining, a chip will not be generated if the uncut chip thickness is less than a critical value, viz., the minimum chip thickness because of the edge radius effect. Due to this minimum chip thickness effect, the micromachining process is affected by two mechanisms - chip removal and ploughing/ rubbing. The extent of ploughing/rubbing

and

the

nature

of

the

micro

deformation

during

ploughing/rubbing contribute significantly to increased cutting forces, burr formation, and increased surface roughness. Hence, knowledge of the minimum chip thickness is important to the selection of appropriate machining conditions. The concept of minimum chip thickness is that the depth of cut or feed must be over a certain critical chip thickness before a chip will form [2]. Fig. 2.1 depicts the chip formation with respect to chip thickness. When the uncut chip thickness, h, is less than a critical minimum chip thickness, hm, as shown in Fig. 2.1 (a), elastic deformation occurs and the cutter does not remove any workpiece material. As the uncut chip thickness approaches the minimum chip thickness, chips are formed by shearing of the workpiece, with some elastic deformation still

7

occurring, as illustrated in Fig. 2.1 (b). As a result, the removed depth of the workpiece is less than the desired depth. However, when the uncut chip thickness increases beyond the minimum chip thickness, the elastic deformation phenomena decreases significantly and the entire depth of cut is removed as a chip, Fig. 2.1 (c)

Fig. 2.1 Schematic of the effect of the minimum chip thickness [2]

2.5 ESTIMATION OF MINIMUM CHIP THICKNESS The knowledge of minimum chip thickness is very important in selecting the machining conditions. The relationship between the tool radius and minimum chip thickness depends on the cutting edge radius and the material of the workpiece. It is very difficult to directly measure the minimum chip thickness during the process, in spite of knowing the tool edge radius. X. Liu. [8] defined normalized chip thickness as the ration f minimum chip thickness to tool edge. Researchers have estimated the normalized chip thickness through molecular dynamic (MD) simulation, microstructure level finite element (FE) simulation and experimentation. Shimda et al. [9] used MD simulations and estimated minimum chip thickness for chip formation to be as small as 1/20 of the cutting edge radius for diamond turning of copper and aluminum with edge radii of 5-10 µm. Yuan et al. [8] experimentally studied the effect of minimum chip thickness on the surface roughness in diamond turning of aluminum alloys and estimated the

8

minimum chip thickness to between 20% and 40% of the cutting edge radius of 12 µm. Vogler et al. [10] determined the minimum chip thickness of steel by using an FE simulation tool. They reported the critical chip thickness is 0.2 and 0.3 times the edge radius for pearlite and ferrite, respectively. The experimental method for estimation of minimum chip thickness is tedious and expensive and the accuracy will be strongly affected by experimental uncertainties. MD simulation is only applicable for nanometric-scale machining. The microstructure-level finite element simulation is computational expensive and requires an experimentally characterized constitutive model of the material. Therefore, it is not suitable to be applied for a wide range of materials. One of the methods is determining from the normalized chip thickness [8]. The normalized minimum chip thickness is determined by its own thermo mechanical properties. Most of these thermo mechanical properties, such as yield strength and ductility, are very sensitive to the temperature, strain, and strain rate. Therefore, the cutting conditions that have strong influence on the cutting temperatures, strain, and strain rate such as cutting velocity and tool edge radius are expected to significantly influence the minimum chip thickness values. Fig 2.2 shows the flow chart to find the normalized chip thickness using slip line filed theory.

2.6 EVALUATION OF CRITICAL ISSUES Specific areas of focus include the size effect in micro machining and its impact on cutting forces, chip formation and morphology, and surface generation; the influence of built-up edge formation and elastic-plastic deformation while machining at this size-scale; and microstructure influences on machining performance at the micro scale. Some of the relevant work cited finds its origins in the study of ultra precision machining.

9

Fig. 2.2 Flow chart to determine normalized chip thickness.

2.6.1 Size Effect (i) Cutting Force/Specific Cutting Energy Lucca et al. [11] experimentally determined that the shearing process could not account for all of the observed energy when machining OFHC copper at small values of depth of cut. They showed that the ploughing and elastic recovery of the workpiece along the flank face of the tool play a significant role when machining with chip thickness values approaching the edge radii of the cutting inserts. They noticed that the specific cutting energy required to machine at very low chipthickness values could not be explained by the energy required for shearing and for overcoming friction on the rake face of the tool. The significance of ploughing under these conditions was used to explain the increase in the cutting energy. Taminiau and Dautzenberg also witnessed an increase in cutting energy while machining brass with decreased chip thickness. Both rough cutting, using conventional tools with edge radii varying between 50 and 200 µm, and highprecision cutting, using a ground diamond tool with an edge radius of 15 µm, were carried out. The authors discovered that the specific cutting forces depended

10

only on the ratio of the uncut chip thickness to the cutting edge radius when the uncut chip thickness was smaller than the edge radius [11].

(ii) Minimum Chip Thickness Ikawa et al. found the minimum chip thickness was strongly affected by the sharpness of the cutting edge than by tool-work interaction. The minimum chip thickness of cut might be on the order of 1/10 of the cutting edge radius [11].

2.6.2 Built-up Edge Elastic-Plastic Deformation In micromachining, the size effect and the minimum chip thickness phenomenon cause ploughing, and, therefore, the associated material flow pattern and elasticplastic deformation along the rounded cutting edge plays a dominant role in machining performance. Therefore, it is useful to examine the relevant research that has examined how the material deforms in the vicinity of the cutting edge, e.g., built-up edge formation and the nature and extent of elastic plastic deformation.

2.6.3 Elastic-Plastic Deformation The extent of ploughing/rubbing and the nature of micro deformation during ploughing/rubbing

contribute

significantly

to

burr

formation

and

the

corresponding increase in surface roughness. In order to accurately model the micromachining process, it is important to develop methodologies to quantify the elastic-plastic deformation of workpiece material. Scratch testing [12] has been shown to be an effective tool to assess the elastic-plastic deformation of materials. Since the scratching process resembles the micromachining process in that the workpiece material experiences normal pressure and lateral relative motion in both processes, a brief review of scratch testing is helpful in gaining a better understanding

2.6.4 Microstructure Effects Since the length scale of the crystalline grain size of most commonly used engineering materials, such as steel, aluminum, etc., is between 100 nm and 100

11

mm and the feature size of micro machined component is of a comparable order, material microstructure effects will play an important role in micromachining. In ultraprecision machining, a typical cutting depth of a few micrometers is common. With such a small depth of cut, chip formation takes place inside the individual grains of a polycrystalline material. The effect of the crystallographic orientation on the mechanism of chip formation, surface generation, and the variation of the cutting forces were studied [11].

2.7 SURFACE FINISH IN MICROTURNING In a conventional turning operation, the surface finish left on the machined part is produced by the cutting tool with a nose radius. The use of a tool with nose radius introduces several complications: 1. Ridges corresponding to the geometry of the tool nose and having a pitch equal to the axial feed are left behind on the finished surface. 2. The uncut chip thickness gradually goes to zero at the secondary cutting edge and this causes uncertainty in the geometry of the cut at the trailing edge, since for a given edge sharpness there is minimum uncut chip thickness that will be removed. 3. The metal at the trailing edge of the tool is subjected to high normal stress and will flow to the side to relieve this stress. This in turn produces a furrow that contributes to the roughness, particularly in the case of a soft, ductile metal. In addition to these special roughness components, built up edge (BUE) roughness, roughness of the cutting edge, and roughness due to tool vibration may also be present. The first component of surface roughness is simply the geometric contribution of the tool nose geometry and the tool feed. This geometric component of surface roughness, also called kinematic or theoretical surface roughness, can be approximated by the following equation,

12

f2 Rth ≅ 8rn

(2.1)

Where f is the feed and ‘rn’ is the tool nose radius. The kinematic surface roughness is considered to be the main factor responsible for the tool marks left on the machined surface and is commonly used to estimate the theoretical surface roughness in conventional cutting at large feeds. The revised model for surface roughness prediction in microturning consists of three components, kinematic surface roughness Rth, roughness associated with plastic side flow Rp and roughness of the cutting edge Redge.

Rtotal = Rth + Rp + Redge

(2.2)

2.8 MICROSHAFT FABRICATION A microshaft is a useful tool for other micromachining process such as microEDM. During turning operation, the thrust force is important in determining the deflection (d) of the workpiece. The work is easily deflected by the reacting force with a reduction in its rigidity according to the decrease in its diameter as shown in Fig. 2.3. Thus, by reducing the thrust force to a sufficiently low level, workpiece deflection can be minimized [14]. If Ft is the thrust force on the tool at the tip and d is the diameter of the cylindrical workpiece, the deflection of the workpiece and the produced maximum stress can be estimated by a simple material strength equation as follows: Deflection, δ =

FX l 3 64 FX l 3 = 3EI 3π Ed 4

(2.3)

3FX l 3 πd3

(2.4)

Bending stress, σ =

By measuring the thrust force at a particular workpiece dimension, the deflection and maximum stress can be estimated. The maximum stress which emerges in the workpiece should be restrained below the level that causes plastic deformation. Miniature shafts were fabricated using microturning by applying step cutting

13

process [15]. The step size (l), for which the shaft will not deflect plastically, was determined by applying Eqs. (2.3) and (2.4).

Fig. 2.3 Workpiece deflection in microturning

2.9 APPLICATION OF MICROTURNING One of the major applications is machining of thin electrode using microturning. Fig. 2.4 illustrates the concept of turning-EDM hybrid machining. In this hybrid machining process [16], EDM is carried out using a turned shaft. An electrode of required length is fabricated using the micro-turning process. Using this hybrid machining, clamping error can be avoided and deflection of electrode can be minimized, consequently the accuracy of machining can be improved. When different diameters of electrode are preferred, turning can significantly reduce the electrode preparation time as compared to the WEDG method. This hybrid machining technology also can be used to fabricate a cylindrical micro component with non-rotational portion such as a key slot or flat bar by the help of EDM process followed by turning. One of the problems in machining thin electrode using turning is the deflection of the workpiece during machining. Another factor that affects the deflection of the

14

workpiece is the step size, which is given to minimize the deflection. Fig. 2.5 shows the effect of step size on the step error and deflection of the workpiece. It is

Fig. 2.4 turning-EDM hybrid machining [16]

observed that the effect of step size on step error is more dominant than depth of cut when the step size is increased. For the thin electrode turning, reducing step size is a promise to reduce the overall error of the electrode.

Fig. 2.5 Effect of step size on step error and deflection [16]

Some samples of micro-turned parts are shown in Fig. 2.6. Fig. 2.6 (a) Shows a typical microelectrode with diameter of 33µm. Fig. 2.6 (b) Shows a high aspect ration microshaft with diameter of 0.1mm and length of 15 mm. This high aspect ratio could be achieved using the microturning with minimized step size of less

15

than 0.5 mm. Fig. 2.6 (c) Shows microshafts with different diameter and feature. The shafts are being used as main shaft for ultrasonic micro motors.

Fig. 2.6 Microturned parts. (a) 33 µm diameter electrode, (b) high aspect ratio shaft, (c) micromotor shaft [16]

2.10 SUMMARY AND CONCLUSIONS 1. Micro-mechanical machining is a new fabrication method for creating miniature devices and components with features that range from tens of micrometers to a few millimeters in size 2. Due to this minimum chip thickness effect, the micromachining process is affected by two mechanisms: chip removal and ploughing/ rubbing.

16

3. Iterative method is one of the best methods to find the minimum chip thickness using slip line filed theory. 4. Merchant’s sharp-edge cutting theorem cannot be used in micromachining operations due to the effects of edge radius. 5. One group of tool based micromachining technology is microturning. It is a conventional material removal process that has been miniaturized. 6. One of the major applications in microturning is machining of thin electrode. 7. One of the problems in machining thin electrode using microturning is the deflection of the workpiece during machining. Step size is the major factor that affects the deflection of the workpiece. 8. Cutting conditions for micro turning for different workpieces is not yet fully developed.

2.11. RESEARCH OBJECTIVES Based on the literature review, the following are taken as the main objectives for the present study. 1. To identify various parameters that contributes to the cutting forces during microturning and to understand the various mechanism of material removal. 2. Conduct proper experiments to evaluate the significant factors and its effects on the various responses such as cutting force and thrust force. 3. Develop mathematical models to predict the cutting force and thrust force.

17

CHAPTER 3 MICROTURNING: CONCEPTS AND PROCEDURES 3.1 INTRODUCTION Microturning is a conventional material removal process that has been miniaturized. Microturning has the capability to produce 3D structures on microscale. As solid cutting tool is used in microturning, it can produce definite 3D shapes. In order to accurately and precisely control of cutting tool motions during machining, cutting path generation by CNC programming is employed.

3.2 PROCESS DEVELOPMENT For carrying out the process of cutting, the workpiece and the cutting tool must be moved relative to each other in order to separate the excess layer of material in the form of chips. Hence the motion of the cutting tool with respect to the workpiece is important. In this respect, the cutting path generation has been given emphasis. A microshaft with a high aspect ratio and micron range diameter cannot be machined by a parallel cut to the axis of the job as in conventional machining shown in Fig. 3.1. As the machining goes on the shaft tends to deflect because the diameter reduces and the unsupported length of the workpiece increases. Fig. 3.2 describes one possible way of the fabrication of miniature shafts by the step cutting process. Unlike the conventional parallel cut turning, in this work, turning is done in a step wise manner, which will help in minimizing the deflection of the shaft.

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Fig. 3.1 Turning by parallel cut to workpiece axis.

Fig. 3.2. Turning by step cutting process

3.3 MICROTURNING CUTTING PATH SCHEMES For NC code generation SLICER software developed by Mikrotools Pte Ltd. Singapore, is used in this work. There are four cutting path schemes are available in the SLICER. They are 1. Whole element 2. Step cut 3. Reverse cutting 4. Disk-2 The four microturning path schemes are shown in Fig. 3.3. In the present study step cut cutting path scheme is used. As the diameter of the shaft selected is very less in this study, if whole element path scheme is not used in microturning since it leads to deflection of the shaft.

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Fig. 3.3 Microturning path schemes. (a) Whole element, (b) Step cut, (c) Reverse cutting, (d) Disk-2

3.4 FORCES IN MICROTURNING Typical forces developed in the microturning are depicted in Fig. 3.4. The cutting tool used was SumiDIA PCD positive insert type TCMD73X (0.1 mm nose radius, 70 side relief angle and 80 rake angle). The tool shank used was Sumitomo type STGCR1010-09.

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Fig. 3.4 Force directions in microturning Fx – Thrust force, Fy – Cutting force, Fz – Feed

21

CHAPTER 4 EXPERIMENTATION 4.1. MACHINE TOOL The experiments were carried out in a three-axis multipurpose miniature machine tool, developed for high precision micromachining. The machine tool is shown in Fig. 4.1. It is possible to perform different micromachining process like micromilling, microturning, micro drilling, micro-EDM, micro-ECM and micro grinding in the same machine.

Fig. 4.1 Machine tool. The machine tool has dimensions of 560 mm W X 600 mm X D 660 mm H, and the maximum travel range in X 210 mm, in Y 110 mm and 110 mm in Z direction.

22

Each axis has an optical linear scale with resolution of 0.1 mm, and close loop feed back control ensures accuracy to submicron dimensions. The motion controller of this machine can execute CNC program from the host computer.

4.2. WORKPIECE AND CUTTING TOOL The workpiece material used was commercially available brass of 6.5 mm diameter. The cutting tool used was SumiDIA PCD positive insert type TCMD73X (0.1 mm nose radius, 70 side relief angle and 80 rake angle). The tool shank used was Sumitomo type STGCR1010-09.

4.3. DYNAMOMETER AND WORKPIECE SETUP The workpiece was clamped in the spindle unit of the machine. Cutting tool insert was attached to the tool shank, which was mounted below the tool holder. Cutting tool was kept stationary and the rotational and the feed motions of the spindle carried out the machining process. The cutting force signals were measured with a three component dynamometer (KISTLER Type 9256C2), mounted below the tool holder. To clamp the dynamometer on the bed a fixture was made, with dimensions of the dynamometer. The dynamometer was mounted on the machine bed (Fig. 4.2.) and was connected to the cutting force data acquisition system. The force signals were subsequently amplified by a Kistler charge amplifier and then passed through an analog-digital interface. Signals of frequency 300 Hz were taken. Finally the real time cutting force was displayed on a computer screen.

4.4 Minidyn 9256C2 4.4.1 Description KISTLER Multicomponent dynamometer for measuring the three orthogonal components of a force is used. Its very low threshold allows measuring even extremely small forces and main features of this are; • Designed for cutting force measurements in ultra precise machining • Small design

23

• High sensitivity and natural frequency • Small Temperature error

Fig. 4.2 Schematic sketch of Experimental setup

Fig. 4.3 Experimental setup

24

4.4.2 Principle of operation The dynamometer consists of four 3-component force sensors mounted under high preload between the cover plate and the two lateral base plates. A low temperature error is obtained by this special mounting of the sensors. Each force sensor contains three crystal rings, of which one is sensitive to pressure in the y-direction and the two others to shear in the x- and z-directions. The forces are measured practically without displacement.

Fig. 4.4 9256C2 Dynamometer

4.5 DynoWare DynoWare is a general-purpose data acquisition and display software package suitable for cutting force and general dynamometer applications. It is designed to combine the performance of the proven line of KISTLER quartz dynamometers with modern computer technology. DynoWare lets you quickly setup, record, and display reaction forces and moments. Using Instacal software, A/D board is selected properly as per the hardware configurations. With the help of RS-232C the charge amplifier is interfaced with the PC. Sensitivity values are given as stated in calibration chart. The ranges of measuring forces are 100 N. After analyzing the various options, the frequencies of 300 Hz were selected as filter for the signals. As stated the forces are measured with the help of the mindyn 9256C2, accordingly this dynamometer is selected in force moment calculations.

25

Fig. 4.5 Typical Hardware setup details of charge amplifier.

Fig. 4.6 Selection of Dynamometer details.

26

4.5.1 Analysis The cutting force signals were analyzed by using Mean value and Cursor tool which are available in DynoWare software. In the present wok, the force values were taken from mean value analysis.

4.5.1.1 Mean value analysis Statistical data can be displayed on an active y-t time based graph. The statistical data will appear on the bottom of the graph (up to 8 curves can be displayed). Statistical data will include: Maximum value, Minimum Value and Mean (average) and Integral (area under curve) value between two user identified points.

Fig. 4.7 Mean Value analysis in DynoWare

4.5.1.2 Cursor tool analysis Cursor data will include: start time (t1), ending time (t2), delta time (t2-t1, dt), 1/ dt (s-1) and 1/ dt (min-1). Also, the starting value Y (t1), ending value Y (t2), and dY, ie: Y (y2)-Y (t1) of all displayed curves will be displayed. The tool window can be moved and resized as needed.

27

Fig. 4.8 Cursor tool analysis in DynoWare

4.6 MACHINING PROCESS Computer numerical control machines may be the only equipment that can provide quick and accurate machining operations for workpieces that involve complex shapes such as three dimensional surfaces. The CNC machine responds to programmed signals from the machine control unit and manufactures the part. To achieve micron range dimensions, precise control of the machine as well as machining parameters such as feed rate, depth of cut and rpm is important. The machining of the micropin requires hundreds of lines of NC code. Commercially available CAM software is not suitable for fabrication of the micropin of compound shape. Windows based programs were written using Borland C++ Builder 6.0, for generation of the NC codes. Such a NC code generator facilitates the machining process for taper and cylindrical microturning. An enlarged view of microturning process is shown in Fig. 4.9.

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Fig. 4.9 Microturning operation One sample program is explained below.

Fig. 4.10 A typical turned component 0, 6.5, -15.0, 6.5, 0, 0 1, 4, -15.0, 4, -10.0, 0 2, 1, -10.0, 1, -5.0, 0 3, 0.5, -5.0, 0.5, 0.0, 0 It is summarized as follows. The first digit in each row indicates the block numbers. In ‘0’ block, the initial diameter of the bar and the machining length is specified. In ‘1’ block, the largest diameter of bar to be machine and its machining length is specified. In ‘2’ block,

29

the next largest diameter and the machining length of that diameter is specified. Like this blocks are specified and these files are saved in ‘.txt’ format. The next step is importing this file to ‘SLICER’. SLICER output formats are shown in the Fig. 4.9, Fig. 4.10, Fig. 4.11, and Fig. 4.12

Fig. 4.11 Selection of cutting conditions in SLICER

Fig. 4.12 Loading the workpiece dimensions into SLICER

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Fig. 4.13 Cutting path scheme generated by SLICER

Fig. 4.14 Generated NC program by SLICER One of the problems faced during the machining process was centering of the workpiece. In this work, the centering has been done by trial and error method. If

31

the centering of the workpiece is not perfect, during the process the produced shaft may break.

4.7 DESIGN OF EXPERIMENTS A scientific approach to planning of experiments must be incorporated in order to perform the experiment most effectively. Hence, Design of Experiments, a statistical technique, which allows to run the minimum number of experiments to optimize the product or process is selected in this work. The factors effecting the forces are shown in the Fig. 4.15. The controllable factors are cutting speed, feed, depth of cut, and step cut length. Two levels, 4 factorial and 2 replicate design developed using Design-Expert package 6.1 version. The total number of experiments was 2X24=32. The cutting parameters are speed, depth of cut, feed and step cut length. The high and low level of feed and depth of cut are selected by considering the nose radius of tool, and suggestible maximum spindle speed for micromachining centre is 3000 rpm. The response variables are cutting force and thrust force.

Fig. 4.15 Ishikawa diagram The level designation of cutting parameters and the set of experiments developed through Design-Expert are shown in Table 4.1, Table 4.2.

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Table 4.1 Level designation of cutting parameters

Factors

Unit

Low level

High level

Spindle Speed (N)

rpm

1000

3000

(Cutting Speed)

(m/min)

(20)

(60)

Depth of cut (D)

µm

50

200

Feed (f)

mm/min

10

50

Step cut length (l)

µm

100

500

33

Table 4.2 Experimental observations. Factors Std Run

12 26 1 20 27 6 24 23 22 7 14 30 8 29 9 19 18 5 21 15 25 31 11 28 16 2 13 4 10 17 3 32

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32

Block

Block 1 Block 1 Block 1 Block 1 Block 1 Block 1 Block 1 Block 1 Block 1 Block 1 Block 1 Block 1 Block 1 Block 1 Block 1 Block 1 Block 1 Block 1 Block 1 Block 1 Block 1 Block 1 Block 1 Block 1 Block 1 Block 1 Block 1 Block 1 Block 1 Block 1 Block 1 Block 1

A: Spindle Speed

B: Depth of cut

(rpm) 3000 1000 1000 3000 3000 1000 3000 3000 1000 3000 1000 1000 3000 1000 1000 3000 1000 1000 1000 3000 1000 3000 3000 3000 3000 1000 1000 3000 1000 1000 3000 3000

(µm) 50 50 50 50 50 200 200 200 200 200 200 200 200 200 50 50 50 200 200 200 50 200 50 50 200 50 200 50 50 50 50 200

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Responses D: Step Thrust Cutting C: Feed Force Force cut length (mm/min) (µm) (N) (N) 50 100 1.73 0.2 50 500 11.88 0.74 10 100 2.79 0.8 10 500 4.92 0.36 50 500 8.48 0.37 10 100 1.34 0.92 10 500 3.99 0.63 10 500 3.6 0.53 10 500 4.8 0.86 10 100 0.76 0.82 50 100 0.64 0.82 50 500 6.06 3.31 10 100 0.72 0.79 50 500 5.75 3.39 50 100 2.18 0.53 10 500 4.89 0.38 10 500 6.51 0.71 10 100 1.39 0.95 10 500 4.92 0.94 50 100 1.01 0.78 50 500 11.59 0.77 50 500 5.36 1.02 50 100 1.61 0.16 50 500 8.05 0.44 50 100 1.11 0.81 10 100 2.81 0.79 50 100 0.56 0.92 10 100 1.47 0.63 50 100 2.34 0.51 10 500 6.47 0.8 10 100 1.53 0.65 50 500 5.29 1.11

CHAPTER 5 RESULTS AND DISCUSSION Design-Expert is used for analysis of the experimental result. The factors considered are spindle speed, depth of cut, feed, step cut length and the response variables are cutting force and thrust force. The results were analyzed the effective factors and regression model were obtained.

5.1 ANOVA ANALYSIS FOR CUTTING FORCE Table 5.1 ANOVA Analysis table for cutting force Mean F % Sum of Squares DF Square Value Prob > F Contribution Model 14.8696 15 0.991307 525.1956 < 0.0001 A 2.0402 1 2.0402 1080.901 < 0.0001 13.69 B 2.9768 1 2.9768 1577.113 < 0.0001 19.97 C 0.5832 1 0.5832 308.9801 < 0.0001 3.91 D 0.8712 1 0.8712 461.5629 < 0.0001 5.84 AB 0.31205 1 0.31205 165.3245 < 0.0001 2.09 AC 0.53045 1 0.53045 281.0331 < 0.0001 3.56 AD 0.8712 1 0.8712 461.5629 < 0.0001 5.84 BC 1.5842 1 1.5842 839.3113 < 0.0001 10.63 BD 0.68445 1 0.68445 362.6225 < 0.0001 4.59 CD 1.78605 1 1.78605 946.2517 < 0.0001 11.98 ABC 0.3872 1 0.3872 205.1391 < 0.0001 2.59 ABD 0.5832 1 0.5832 308.9801 < 0.0001 3.91 ACD 0.405 1 0.405 214.5695 < 0.0001 2.71 BCD 0.6272 1 0.6272 332.2914 < 0.0001 4.20 ABCD 0.6272 1 0.6272 332.2914 < 0.0001 4.20 Pure Error 0.0302 16 0.001888 Cor Total 14.8998 31 Source

The Model F-value of 525.1956 implies the model is significant. There is only a 0.01% chance that a "Model F-Value" this large could occur due to noise. Values of "Prob > F" less than 0.0500 indicate model terms are significant. In this case A, B, C, D, AB, AC, AD, BC, BD, CD, ABC, ABD, ACD, BCD,

35

ABCD are siginficant model terms. Values greater than 0.10 indicate the model terms are not significant. R-Squared

0.9980

Adj R-Squared

0.9961

Pred R-Squared

0.9919

The "Pred R-Squared" of 0.9919 is in reasonable agreement with the "Adj RSquared" of 0.9961. "Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. In this case, model ratio of 103.188 indicates an adequate signal.

5.2 DIAGNOSTIC RESULTS FOR CUTTING FORCE Normal probability plot used to choose significant effects. A plot of the ordered values of a sample versus the expected ordered values from the true population will be approximately a straight line. Thus, if the effects represent a sample from a normal population, they form an approximate straight line on a normal probability plot of the effects. Usually only a few effects turn out to be important. They show up as outliers on the normal probability plot. For the full normal probability plot, the line should be fit to the center set of points, those whose effects appear to be near zero. Effects are selected by clicking on the plotted points (the outliers). In the Design-Expert software, the color of the point and the letter label will change when the point is selected or de-selected. Normal probability plot of effects for cutting force is shown in Fig. 5.1. From this plot it is clear that, A (Spindle Speed) and B (Depth of cut) are the largest effects. An alternative to the normal probability plot of the factor effects is the half normal plot. This is a plot of the absolute value of the effect estimates against their cumulative normal probabilities. Fig. 5.8 shows the half normal plot of the effects for cutting force. The straight line on the half normal plot is passing through the

36

origin. Half normal plot also shows the A (Spindle Speed) and B (Depth of cut) have the largest effects on the cutting force. The green triangles both in the normal plot and half normal plot indicates the pure error from the replicate designs. The normal plot of residuals is shown Fig. 5, most of the points nearer to the straight line.

Fig. 5.1 Normal Plot of effects for cutting force

Fig. 5.2 Half Normal Plot of effects for cutting force

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Fig. 5.3 Normal plot of residuals for cutting force

Fig. 5.4 Diagnostic plot of Predicted Vs Actual values for cutting force A graph of the actual response values versus predicted response values. It helps to detect a value or group of values, which are not easily predicted by the model. The graph of actual vs. predicted for cutting force is shown in Fig. 5.4. It shows that the actual values and predicted values are not having significant variation.

38

Final Equation in Terms of Coded Factors: Cutting Force = +0.86-0.25 * A+0.30 * B+0.14 * C+0.17 * D-0.099 * A * B -0.13 * A * C-0.17 * A * D+0.22 * B * C+0.15 * B * D+ 0.24 * C * D-0.11 * A * B * C-0.14 * A * B * D -0.11 * A * C * D+0.14 * B * C * D-0.14 * A * B * C * D

Final Equation in Terms of Actual Factors: Cutting Force =+0.80344 +1.20833E-005 * Spindle Speed +2.12500E-003 * Depth of cut +6.56250E-004 * Feed +7.96875E-004 * Step cut length -6.16667E-007 * Spindle Speed * Depth of cut -6.33333E-006 * Spindle Speed * Feed -6.06250E-007 * Spindle Speed * Step cut length -1.25000E-004 * Depth of cut * Feed -1.42500E-005 * Depth of cut * Step cut length -5.96875E-005 * Feed * Step cut length +6.66667E-008 * Spindle Speed * Depth of cut * Feed +5.00000E-009 * Spindle Speed * Depth of cut * Step cut length +3.02083E-008 * Spindle Speed * Feed * Step cut length +1.40000E-006 * Depth of cut * Feed * Step cut length -4.66667E-010*Spindle Speed*Depth of cut*Feed* Step cut length Neglecting the higher order terms of having contribution less than 5%, the model redesinged and analyzed the results. The results of the new design are discussed in the following section. The results are shown in the Table 5.2. From the Table 5.2, the Model F-value of 31.96796 implies the model is significant. There is only a 0.01% chance that a "Model F-Value" this large could occur due to noise. In this case A, B, D, BC, CD are significant model terms. R-Squared

0.8847

Adj R-Squared

0.8570

Pred R-Squared

0.8111

Adeq Precision

22.328

The "Pred R-Squared" of 0.8111 is in reasonable agreement with the "Adj RSquared" of 0.8570."Adeq Precision" measures the signal to noise ratio.

39

Table 5.2 ANOVA Analysis for cutting force (After negelcting higher order interaction terms) Sum of Source Squares Model 9.956287 A 2.474641 B 3.751721 C 0.006555 D 0.430487 BC 1.488148 CD 1.804736 Residual 1.297691 Lack of Fit 1.215807 Pure Error 0.081884 Cor Total 11.25398

DF 6 1 1 1 1 1 1 25 9 16 31

Mean F Square Value 1.659381 31.96796 2.474641 47.67392 3.751721 72.27685 0.006555 0.126281 0.430487 8.293322 1.488148 28.66915 1.804736 34.76821 0.051908 0.13509 0.005118

Prob > F < 0.0001 < 0.0001 < 0.0001 0.7253 0.0080 < 0.0001 < 0.0001

A ratio greater than 4 is desirable. In this case the model ratio of 22.328 indicates an adequate signal. This model can be used to navigate the design space. The normal probability and half normal plots are shown in the Fig. 5.5 and Fig. 5.6. From these gaprhs it is noticed that the depth of cut (B) and Spindle Speed (A) are the major influencial factors on the cutting force. The normal plot of residual and predicted vs. actual values graphs are shown in the Fig. 5.6 and Fig. 5.7.

Fig. 5.5 Normal Plot of effects for cutting force (after neglecting higher order interaction terms)

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Fig. 5.6 Half Normal Plot of effects for cutting force (after neglecting higher order interaction terms)

Fig. 5.7 Normal Plot of residuals for cutting force (after neglecting higher order interaction terms)

41

Fig. 5.8 Diagnostic plot of Predicted Vs Actual values for cutting force (after neglecting higher order interaction terms)

Final Equation in Terms of Coded Factors: Ln(Cutting Force) = -0.35-0.28 * A +0.34 * B+0.014 * C+0.12 * D +0.22 * B * C+0.24 * C * D

Final Equation in Terms of Actual Factors: Ln(Cutting Force) =

+0.51626 -2.78087E-004 * Spindle Speed +2.52416E-004 * Depth of cut -0.035066 * Feed -1.20119E-003 * Step cut length +1.43766E-004 * Depth of cut * Feed +5.93707E-005 * Feed * Step cut length

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5.3 ANOVA ANALYSIS FOR THRUST FORCE Table 5.3 ANOVA Analysis table for thrust force Source

Sum of Squares

Model A B C D AB AC AD BC BD CD ABC ABD ACD BCD ABCD Pure Error

17.922206 0.4879302 2.0346011 0.4030196 13.589672 0.1543147 0.0643675 0.0378638 0.1443607 0.0121578 0.7510752 0.0575561 0.001305 0.0977082 0.0861805 9.327E-05 0.0171062

Cor Total

17.939312

DF

Mean Square

F Value

Prob > F

15 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 16

1.194814 0.48793 2.034601 0.40302 13.58967 0.154315 0.064368 0.037864 0.144361 0.012158 0.751075 0.057556 0.001305 0.097708 0.086181 9.33E-05 0.001069

1117.549 456.3772 1903.029 376.9574 12710.87 144.3356 60.20505 35.41524 135.0253 11.37156 702.5053 53.83414 1.220646 91.38964 80.60748 0.087238

< 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.0039 < 0.0001 < 0.0001 0.2856 < 0.0001 < 0.0001 0.7715

31

% Contribution 2.7198 11.341 2.246 75.753 0.86 0.358 0.211 0.804 0.0677 4.186 0.32 0.00727 0.5446 0.48 0.0005

The Model F-value of 1117.55 implies the model is significant. There is only a 0.01% chance that a "Model F-Value" this large could occur due to noise. Values of "Prob > F" less than 0.0500 indicate model terms are significant. In this case A, B, C, D, AB, AC, AD, BC, BD, CD, ABC, ACD, BCD are significant model terms.Values greater than 0.1000 indicate the model terms are not significant. R-Squared

0.9990

Adj R-Squared

0.9982

Pred R-Squared

0.9962

Adeq Precision

114.677

The "Pred R-Squared" of 0.9962 is in reasonable agreement with the "Adj RSquared" of 0.9982. "Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. In this case, ratio of 114.677 indicates an adequate signal. This model can be used to navigate the design space.

43

5.4 DIAGNOSTIC RESULTS FOR THRUST FORCE The normal plot of effects for thrust force is shown in Fig. 5.9. Fig. 5.8 shows the half normal plot of the effects for cutting force. From these plots it is clear that the largest effects are B (Depth of cut) and D (Step cut length). The normal plot of residuals is shown Fig. 5.3, most of the points nearer to the straight line.

Fig. 5.9 Normal Plot of effects for thrust force A graph of the actual response values versus predicted response values. It helps to detect a value or group of values, which are not easily predicted by the model. The graph of actual vs. predicted for cutting force is shown in Fig. 5.6. It shows that the actual values and predicted values are the same.

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Fig. 5.10 Half Normal Plot of effects for thrust force

Fig. 5.11 Normal plot of residuals for thrust force

45

Fig. 5.12 Diagnostic plot of Predicted Vs Actual values of thrust force

Final Equation in Terms of Coded Factors: Sqrt(Thrust Force) = +1.84 -0.12 * A -0.25 * B +0.11 * C +0.65 * D+ 0.069 * A * B +0.045 * A * C -0.034 * A * D -0.067 * B * C -0.019 * B * D+0.15 * C * D+0.042 * A * B * C-6.386E-003 * A * B * D-0.055 * A * C * D-0.052 * B * C * D+1.707E-003 * A * B * C * D

Final Equation in Terms of Actual Factors: Sqrt(Thrust Force) =

length

+2.03052 -2.95568E-004 -3.69904E-003 -0.012901 +9.89086E-004 +2.56642E-007 +3.06600E-006 +3.16997E-007 -4.60144E-005 +5.08303E-006 +8.89752E-005 +2.65663E-008 -5.96465E-010 -1.45257E-008 -1.84367E-007 +5.69081E-012

* Spindle Speed * Depth of cut * Feed * Step cut length * Spindle Speed * Depth of cut * Spindle Speed * Feed * Spindle Speed * Step cut length * Depth of cut * Feed * Depth of cut * Step cut length * Feed * Step cut length * Spindle Speed * Depth of cut * Feed * Spindle Speed * Depth of cut * Step cut length * Spindle Speed * Feed * Step cut length * Depth of cut * Feed * Step cut length * Spindle Speed * Depth of cut * Feed * Step cut

46

By ignoring the higher order interaction terms, having the contribution <5%, the model was analyzed again. The results were discussed in the following section. Table 5.4 ANOVA Analysis for thrust force (After negelcting higher order interaction terms) Source Model A B C D CD Residual Lack of Fit Pure Error Cor Total

Sum of Squares 17.2663 0.48793 2.034601 0.40302 13.58967 0.751075 0.673014 0.655908 0.017106 17.93931

DF 5 1 1 1 1 1 26 10 16 31

Mean Square 3.45326 0.48793 2.034601 0.40302 13.58967 0.751075 0.025885 0.065591 0.001069

F Value 133.407 18.84981 78.6011 15.56953 524.9988 29.01568

Prob > F < 0.0001 0.0002 < 0.0001 0.0005 < 0.0001 < 0.0001

The Model F-value of 133.41 implies the model is significant. Values of "Prob > F" less than 0.0500 indicate model terms are significant. In this case A, B, C, D, CD are significant model terms. R-Squared

0.9625

Adj R-Squared

0.9553

Pred R-Squared

0.9432

Adeq Precision

33.890

The "Pred R-Squared" of 0.9432 is in reasonable agreement with the "Adj RSquared" of 0.9553."Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable.In this case a model ratio of 33.890 indicates an adequate signal. This model can be used to navigate the design space.

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Fig. 5.13 Normal Plot of effects for thrust force (after neglecting higher order interaction terms)

Fig. 5.14 Half Normal Plot of effects for thrust force (after neglecting higher order interaction terms)

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Fig. 5.15 Normal plot of residuals for thrust force (after neglecting higher order interaction terms)

Fig. 5.16 Diagnostic plot of Predicted vs. Actual values of thrust force (after neglecting higher order interaction terms)

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Final Equation in Terms of Coded Factors: Sqrt(Thrust Force) =+1.84-0.12 * A -0.25 * B +0.11 * C+0.65 * D +0.15 * C * D

Final Equation in Terms of Actual Factors: Sqrt(Thrust Force)= +1.70838 -1.23482E-004 * Spindle Speed -3.36204E-003 * Depth of cut -5.87898E-003 * Feed +2.10934E-003 * Step cut length +3.83007E-005 * Feed * Step cut length

5.5 Model graphs

Fig. 5.17 Variation of Spindle Speed on cutting force

Fig. 5.18 Variation of Spindle Speed on thrust force

From Fig. 5.17 and Fig. 5.18, at low level of spindle speeds the cutting force and thrust force are having maximum values and at high levels it is less. At high speeds, the material removal rate less so the friction between the tool and the workpiece less which reduces the forces. From Fig. 5.19 and Fig. 5.20, at low values of depth of cut thrust force is dominant and cutting force showed distinctly lower value. At very small depth of cut, the plastic deformation such as rubbing and burnishing is dominant rather

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than cutting in the chip formation processes which generates relatively large thrust force.

Fig. 5.19 Variation of Depth of cut on cutting force

Fig. 5.20 Variation of Depth of cut on thrust force

Fig. 5.21 Variation of Feed on cutting force

Fig. 5.22 Variation of feed on thrust force

From Fig. 5.21 and Fig. 5.22, at higher values of feed the forces are more, this is true because with increase of feed rate the contact area between tool and workpiece increases.

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Fig. 5.23 Variation of Step cut length on cutting force

Fig. 5.24 Variation of step cut length on thrust force

From Fig. 5.23 and Fig. 5.24 at higher values of step cut length the thrust force is dominant. The workpiece is deflected by the thrust force with reduction in rigidity according to the decrease in its diameter. During turning operation, the thrust force is important in determining the deflection of the workpiece.

X = A: Spindle Speed Y = B: Depth of cut Actual Factors C: Feed = 30.00 D: Step cut length = 300.00

Fig. 5.25 Spindle Speed- Depth of cut interaction for cutting force

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X = A: Spindle Speed Y = C: Feed Actual Factors B: Depth of cut = 125.00 D: Step cut length = 300.00

Fig. 5.26 Spindle Speed- Feed interaction for cutting force

X = A: Spindle Speed Y = D: Step cut length Actual Factors C: Feed = 30.00 B: Depth of cut = 125.00

Fig. 5.27 Spindle Speed- Step cut length interaction for cutting force

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X = B: Depth of cut Y = C: Feed Actual Factors A: Spindle Speed = 2000.00 D: Step cut length = 300.00

Fig. 5.28 Depth of cut- Feed interaction for cutting force

X = B: Depth of cut Y = D: Step cut length Actual Factors A: Spindle Speed = 2000.00 C: Feed = 30.00

Fig. 5.29 Depth of cut- Step cut length interaction for cutting force

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X = C: Feed D Y = D: Step cut length Actual Factors A: Spindle Speed = 2000.00 B: Depth of cut = 125.00

Fig. 5.30 Feed-Step cut length interaction for cutting force

Fig. 5.31 Spindle Speed-Depth of cut interaction for thrust force

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Fig. 5.32 Spindle Speed-Feed interaction for thrust force

Fig. 5.33 Spindle Speed-Step cut length interaction for thrust force

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Fig. 5.34 Depth of cut-Feed interaction for thrust force

Fig. 5.35 Depth of cut -Step cut length interaction for thrust force

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Fig. 5.36 Feed-Step cut length interaction for thrust force

5.6 SEM ANALYSIS In this section SEM (Scanning Electron Microscope) images of some samples were given. JEOL Model JSM – 6380LA Analytical Scanning Electron Microscope, Japan is used for the analysis. This is used to get the concept of smoothness and roundness of the hole and to find the taper of the hole by measuring the diameter of the hole at inner and outer surface of the plate. The image is taken with a magnification of 30 and a voltage of 30 kV. Fig. 5.37, Fig. 5.38 shows the SEM image of chips of brass material produced in microturning operation with different cutting parameters. Fig. 5.39 shows the shaft of diameter 500 m.

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Fig. 5.37 SEM image of brass chip (Spindle Speed: 1000 rpm, Depth of cut: 200 m, Feed: 50 mm/min, Step cut length: 500 m)

Fig. 5.38 SEM image of brass chip (Spindle Speed: 3000 rpm, Depth of cut: 200 m, Feed: 50 mm/min, Step cut length: 500 m)

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Fig. 5.39 SEM image of brass chip (Spindle Speed: 3000 rpm, Depth of cut: 50 m, Feed: 50 mm/min, Step cut length: 100 m)

Fig. 5.40 SEM image of microshaft of diameter of 0.5 mm (Spindle Speed: 3000 rpm, Depth of cut: 50 m, Feed: 50 mm/min, Step cut length: 500 m)

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CHAPETER 6 CONCLUSIONS AND SCOPE FOR FUTURE WORK 6.1 CONCLUSIONS At present not much data is readily available for selection of cutting conditions in microturning. In this work, an attempt has been made to conduct experiments with minimum number of trails. The statistical model obtained for determining the cutting force and thrust force was as follows, Ln(Cutting Force) = +0.51626 -2.78087E-004 * Spindle Speed +2.52416E-004 * Depth of cut -0.035066 * Feed -1.20119E-003 * Step cut length +1.43766E-004 * Depth of cut * Feed +5.93707E-005 * Feed * Step cut length Sqrt(Thrust Force) = +1.70838-1.23482E-004 -3.36204E-003 -5.87898E-003 +2.10934E-003 +3.83007E-005

* Spindle Speed * Depth of cut * Feed * Step cut length * Feed * Step cut length

From the study, it was found that depth of cut and step cut length are the most influential cutting parameters on thrust force, depth of cut and spindle speed are the major parameters influencing the cutting force in microturning. At low depth of cuts the thrust force is the dominant force and cutting force is comparatively less. Spindle speed and depth of cut contributing 22%, 33% respectively on cutting force. The thrust force was high at higher values of step cut length. The contribution of step cut length, depth of cut on thrust force was 75%, 11% respectively.

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6.2 SCOPE FOR FUTURE WORK It has been observed that after machining process, the previous step cut ridges were visible on the shaft. There are two ways to overcome this problem; one method is reducing the step cut length value and another one is by modifying the SLICER program so that it will consider the previous step cut length machining. In the present study the depth of cut was taken in the range of 50-200 µm. To study the effect of ploughing and rubbing actions, it is suggested that more experiments may be conducted at lower depth of cuts of 0.5 to 10 µm. The edge radius being a crucial factor for size effect in micromachining, more studies may be conducted with different edge radius inserts. Also more care has to be taken to minimize the errors due to centering of the tool.

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