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CHAPTER 2

LITERATURE SURVEY

2.1 INTRODUCTION

Machining of miniature parts by micromachining is quite different

from traditional machining of parts of identical materials. The difference is in

the way of chip production, the order of specific cutting pressure encountered

and surface integrity of machined parts. For better understanding, a detailed

literature survey has been carried out and presented in this chapter. A general

synopsis of micro and nano scale cutting experiments, ultraprecision

machines, and metrology in micromachining and workpiece materials

employed in past studies will also be presented. The emphasis of this

literature review is placed on experimental studies of cutting forces, size-

effect, chip geometry and surface morphology, process modeling, ductile

machining, tool wear and optimisation and tool condition monitoring using

Acoustic Emission (AE) signals.

2.2 MICROMACHINING PROCESSES

Micromachining processes have been limited to the machining of

simple features such as holes and slots with work piece generally requiring

multiple machining processes using different machines. However, with

technology moving rapidly towards the development of micro devices from

millimeter to sub-millimeter range, demand for more complex miniaturized

and high precision parts is accelerating. Thus there is a need for miniaturizing

conventional machining processes to achieve the complex features. The

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processes include Micro-Turning, Micro-Milling, Micro-Grinding, Micro-

Electro Discharge Machining, Micro Wire-Electro Discharge Machining,

Micro-Electro Discharge Grinding and Micro-Electro Chemical Machining

(Azizur Rahman et al 2005).

2.3 MICROSCALE MACHINING ISSUES

There are a number of issues that prevail in microscale machining

that are fundamentally different from macroscale machining and influence the

underlying mechanisms of the process, resulting in changes in the chip-

formation process, cutting forces, vibrations and process stability, and the

generation and subsequent character of the resulting machined surface (Liu

et al 2004). The fundamental issues are discussed below.

2.3.1 Tool Geometry

The tool geometry, viz., edge radius, is comparable in size to the

cutting geometry, viz., chip load. As a result, the effective rake angle becomes

highly negative, which, in turn, causes plowing and associated elastic-plastic

deformation of the workpiece material which are more dominant factors in the

process. In fact, if the chip load is of the same order or less than the edge

radius of the tool, then a chip may not be formed during each tooth passing.

This phenomenon, known as the minimum chip thickness effect, has a

profound impact on the cutting forces, process stability, and resulting surface

finish in microscale machining.

2.3.2 Chip Load

In processes such as end milling, where the chip load varies during

a single engagement of a tooth in the cut, the cutting mechanism may change

from ploughing-dominated to shearing dominated and back to ploughing-

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dominated again within a single excursion of a tooth through the cut.

Furthermore, owing to the very small chip loads in micromachining, the well-

known size effect plays a significant role.

2.3.3 Microstructure

In micro scale machining, the relationship of the cut geometry to

the workpiece microstructure is also markedly different from macroscale

machining. In micro scale machining, where chip loads may range from

submicron levels to a few microns and depths of cut may be in the range of a

few microns to 100 µm, the cut geometry and the grain sizes of the workpiece

material are now comparable in size. As a result when cutting ferrous

materials, for example, the cutter engagement may be completely in ferrite,

then pearlite, thereby significantly altering the cutting mechanisms and

associated process response, e.g., forces and surface roughness.

Vogler et al (2001) have noted significant frequency content in the

experimental cutting force signal at wavelengths equal to the average grain

size of the material being cut. Microstructural effects in micro scale cutting

are requiring quite different assumptions to be made concerning underlying

material behavior during micromachining and have precipitated the need for

new modeling approaches to account for such effects.

2.4 EXPERIMENTAL STUDIES IN MICROMACHINING

2.4.1 Size Effects in Micromachining

Micro-cutting is characterized by very small amounts of material

removal with uncut chip thickness values that vary from a few microns (or

less) to several hundred microns. At these length scales of material removal,

the well-known size effect phenomenon is expected to be prominent. In

machining, the size effect is typically characterized by a non-linear increase in

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the specific cutting energy (or specific cutting force) as the uncut chip

thickness is decreased.

Kopalinsky and Oxley (1984) conducted turning tests on plain

carbon steel of chemical composition 0.48%C, 0.3%Si, 0.13%S, 0.8%Mn and

0.019%P. The cutting tool used was black ceramic indexable tip with -5º rake

angle and 2º clearance angle. The cutting edge radius of the tool was ground

by a fine grit diamond wheel to a radius much smaller than 6 µm, which was

the smallest value of uncut chip thickness used in their tests. A cutting speed

of 420 m/min was used. The result shows a clear nonlinear scaling effect in

the specific cutting energy with decrease in uncut chip thickness.

Schimmel and Endres (2002) investigated the effect of tool edge

geometry on cutting forces in orthogonal cutting with different edge radius

cutting tools. Orthogonal cutting experiments were performed on materials

such as pure zinc, cast iron and Al-2024 at a cutting speed of 56.4 m/min,

with carbide tools having edge radii ranging from a few microns to a few

hundred microns. Their results also clearly show the nonlinear scaling effect

in the specific cutting energy with decrease in uncut chip thickness.

Furukawa et al (1988) also reported the presence of size effect in

the specific cutting energy over an uncut chip thickness ranging from 0.5 to

10 µm in their investigation of micro-cutting of several different materials

including Aluminium alloy, Oxygen Free Copper, Germanium, Fluorite

(CaF2) and Acryl resin (PMMA). The aluminium alloy is considered to be

isotropic in a macro sense. Germanium is difficult to finish precisely because

of its high hardness and brittleness. Fluorite is a single crystal used for

ultraviolet ray components, and is not very hard but is very brittle. Acryl resin

is a soft amorphous material used for optical components. A single crystal

diamond tool with 0º rake angle and 2º ~3º relief angle was used to cut all

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these materials with a cutting speed of 6 m/min. It is also reported that

nonlinear effect exists.

Lucca et al (1994) have experimentally determined that the

shearing process could not account for all of the observed energy when

machining OFHC (oxygen- free, high-conductivity) 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 have noticed that the specific cutting energy required to

machine at very low chip-thickness values could not be explained by the

energy required for shearing and for overcoming friction on the rake face of

the tool. But the significance of ploughing under these conditions was used to

explain the increase in the cutting energy.

Lucca and Seo (1991) have studied the effect of single crystal

diamond tool edge geometry rake angle and edge radius on the resulting

cutting and thrust forces and specific energy in ultraprecision orthogonal fly

cutting on TECU® copper. Both the nominal rake angle and the tool edge

profile were found to have significant effects on the resulting forces and

energy dissipated over a range of uncut chip thicknesses from 20 m to

10 nm. When the uncut chip thickness approaches the size of the edge radius,

the effective rake angle appears to determine the resulting forces. At small

uncut chip thicknesses, the effective rather than the nominal rake angle

dictates the direction of the resultant force.

Diamond turning tests by Lucca et al (1993) on ductile Al 6061-T6

revealed the transition from a shearing dominated cutting process to a

ploughing-dominated process in orthogonal cutting by studying the angle of

the resultant cutting forces. As the chip thickness decreases, the measured

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resultant force vector is found to be closer to the thrust direction than the

cutting direction. The tool edge (condition) wear is found to have a significant

effect on the thrust forces when the depth of cut is below the tool edge radius.

This can result in the form of nose wear observation in the present study.

Chen et al (1996) have found that the specific cutting forces depend

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. Based on the

measured specific cutting energy, the yield shear stress of the workpiece

material was estimated. The value of the yield shear stress in high-precision

cutting was found to be almost twice as high as the value in conventional

rough cutting for the same workpiece material. The authors attributed the

difference to possible strain hardening effect in high precision cutting.

Nakayama and Tamura (1968) analyzed size effect in machining

through microcutting experiments performed at a very low cutting speed

(0.1m/min) to minimize temperature and strain rate effects. They observed

plastic flow in the subsurface layer of the workpiece and suggested that its

contribution to size effect becomes important with reduction in the uncut chip

thickness. The main cause of this subsurface plastic flow is believed to be the

extension of the shear zone below the machined surface. Therefore, they

attribute the size effect to the fact that the energy consumed in plastic flow in

the subsurface layer is not proportional to the uncut chip thickness and to the

decrease in shear angle with a reduction in the uncut chip thickness.

Zone-Ching Lin et al (2007) established a cutting force calculation

model in terms of nanoscale orthogonal cutting, and investigated the stress–

strain distribution in single-crystal copper that occurs in terms of nano cutting.

The cutting force that occurs during the nanoscale cutting of single-crystal

copper, and also its changes under different situations, had been discussed.

The molecular dynamics (MD) model was proposed to evaluate the

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displacement components of the atom in any temporary situation on the nano-

scale cutting. The atom and lattice were regarded as the node and element,

respectively. The shape function concept of the finite element method (FEM)

is used to calculate the equivalent strain of the nodal atom and element. The

equivalent stress–strain relationship equation was acquired by nanoscale thin-

film tensile simulation in this study, and was used to further calculate the

equivalent stress that occurs under the equivalent strain. Subsequently, a

stress–strain distribution during nano scale orthogonal cutting can be

acquired. When calculating the cutting force, the results from this

investigation show that the workpiece atoms that fall into the diamond tool

can be viewed as a small number of the singular points and hence overlook its

influence. It is only necessary to discuss the force caused by all of the copper

atoms in the block material of the outer edge of the tool towards the carbon

atoms of the tool. When calculating the cutting force, researchers only need

to calculate the condition of force of two layers of carbon atoms on the

surface of tool, but not that of the entire carbon atom structure in the tool, so

as to effectively reduce the calculation time.

Several efforts have been made to explain and predict the size

effect in microcutting. Most of the explanations offered to date can be

classified as follows: 1) Material strengthening due to factors that vary with

the uncut chip thickness, 2) Sub-surface deformation of the workpiece

material, 3) Tool edge radius effects, and 4) Energy required to create new

surfaces via ductile fracture in brittle materials.

2.4.2 Chip Formation

The minimum thickness of a cut is defined as the minimum

undeformed thickness of the chip removed from a work surface at a cutting

edge under perfect performance of the metal-cutting system (no system

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deflection). The minimum chip thickness was found to be more strongly

affected by the sharpness of the cutting edge than by tool-work interaction.

Ikawa et al (1991) have noted that the minimum thickness of cut

might be on the order of 1/10 of the cutting edge radius. Weule et al (2001)

have pointed out the existence of the minimum chip thickness and its

significant influence on the achievable surface roughness in micro-end

milling. A saw tooth like surface profile was observed which was attributed to

the minimum chip thickness effect. The minimum chip thickness to edge

radius ratio for micromachining was estimated to be 0.292. They further noted

that the minimum chip thickness was strongly dependent on material

properties.

Kim et al (2002) performed full slot cutting on brass using 635 m

micro-end mills with feed rates from 0.188 to 6 m /flute and compared the

chips with the nominal chip volume for different feed rates. It was found that

for very small feed rates, the measured chip volume is much larger than the

nominal chip volume, indicating that a chip is not formed with each pass of

the cutting tooth, which was also established by examining the difference

between the feed marks on the machined surface (upsetting and material

removal in blocks).

Sumomogi et al (2002) conducted a series of microturning

experiments on single crystal silicon in order to see the effect of the

crystallographic orientation on surface and subsurface crack generation. A

micro-Vickers hardness indenter was used as a tool for turning with

decreasing depths of cut. Machining was considered to be ductile mode when

surface cracks disappeared. The depth of cut where no subsurface crack was

observed was smaller than ductile depth of cut and depended on the

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crystallographic orientation of silicon. This explain the dependency of cutting

stress on crystallographic orientation.

Simoneau and Elbestawi (2006) investigated chip formation during

micro scale cutting. The insert chosen was a TiN coated, tungsten carbide.

Based on the chip formation process a new chip has been identified as ‘quasi-

shear-extrusion chip’. Analysis of the microchips illustrated that the

individual role of different grains in a material’s microstructure to the plastic

deformation process cannot be ignored or simply averaged out when

considering the plastic deformation process such as in metal cutting. A

general FE model was developed to illustrate the micro scale cutting process

across alternating grains of hard and soft material. While not an optimisation

tool, the model proved useful in shedding light on some of the mechanics of

quasi-shear-extrusion chip formation during microcutting. From the FE model

developed, it was found that in the micro-chip formation process, plastic

deformation will occur throughout the entire chip thickness well beyond the

primary shear deformation zone which is unique to the microcutting process.

This is attributed to the effect of change in shear angle.

Leonardo et al (2009) studied the behaviour of machining forces

and surface finish when microturning PA66-GF30-reinforced polyamide with

various tool materials under distinct cutting conditions. The PCD tool

provided the lowest turning forces, followed by the uncoated carbide inserts.

The CVD diamond-coated carbide was responsible for the highest forces;

therefore, especially designed cutting edge preparations should be devised for

coated tools in microturning in order to keep the forces low and to make the

chip breaker effective at low feed and depth of cut values. As far as the

surface roughness of the machined part is concerned, the same trend

previously reported was observed, i.e., the lower the tool edge radius, the

lower the surface roughness. The PCD tool and the plain carbide without chip

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breaker were responsible for the best results. In general, continuous coiled

microchips were produced, becoming shorter when the tools presented chip

breaker.

2.4.3 Surface Morphology

The need to create surfaces of exceptional accuracy and quality for

micro components is the driving force behind research into surface generation

from micro and nano scale machining. An improved understanding of the

effect and dominant mechanisms that govern surface generation in micro and

nano scale machining aids in the fabrication of micro-components with ultra-

smooth functional surfaces and highly precise dimensions, which is essential

in many electronics and optics applications. Specific applications include

micro-scale fuel cells, micro-holes for fiber optics and micro-moulds for

optical lenses.

Nakayama (1997) suggested that the quality of the surface finish

generated in micro and nano scale machining can be attributed to the

inaccurate motion of the cutting tool relative to the workpiece, as well as the

presence of a built-up edge. The inaccuracies of the cutting tool’s motion can

be eliminated through a combination of the use of higher precision machines

and designing a more rigid experimental setup. In addition, built-up edge can

similarly be avoided by (i) selecting mutually non-adhesive materials for tool

and work material, (ii) machining the work material at cutting temperatures

above the recrystallization temperature of the work material, (iii) using a high

rake angle (> 30º), (iv) maintaining a sharp cutting edge and (v) machining at

very high cutting speeds. The pursuit of better surface finish has promoted

continued investigation in surface morphology in micro and nano scale

cutting. The constituents of the work materials and the crystallographic

orientation of the work material are other factors found to have an influence

on the surface finish of the machined surface.

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Experimental investigations by Eda et al (1985) on single point

diamond machining of aluminium and copper alloys, within the undeformed

chip thickness range of 2 – 70 µm, suggest that the quality of the surface

finish is influenced by the alloys and constituents of the work material and the

deformation of the crystal boundary and separations. Alloyed particles that

were cracked and fractured by the tool during the cutting process and voids

observed on the machined surface supported this deduction. In addition, it

was verified that the machined surface roughness values are close to the

theoretical roughness values, in conformance with the form of the diamond

tool. The smoothest surface finish achievable on pure aluminium workpiece

in this investigation was reported to be 50Å. The surface finish of the work

material is also influenced by the crystallographic orientation of the work

material.

Sato et al (1991), To et al (1997) described similar findings - that

surface roughness and flatness are affected by the cutting direction in

machining single crystal aluminium. Sato et al (1991) reported that when the

single crystal aluminium is machined along the [0 1 1] direction,

corresponding to its sliding direction, the surface finish produced has the

lowest roughness values. Alternatively, machining perpendicular to the

sliding direction along the [1 2 1] direction generates a surface finish with the

highest roughness values. To et al (1997) reported that machining the

aluminium single crystal workpiece along (1 0 0) plane yields the best surface

finish when compared to machining along the (1 1 0) and (11 1) planes. Sato

et al (1991) together with To et al (1997) concluded from their respective

investigations that controlling the crystallographic orientation of the work

material during the machining operations is effective in improving the surface

finish.

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Moriwaki et al (1993) describes similar findings in machining

single crystal copper. However, they further highlighted that the influence of

crystallographic orientation on surface roughness is significantly reduced

when the undeformed chip thickness is reduced. They suggested that the

improvement of the quality of the surface finish at small undeformed chip

thicknesses was because the surface was not generated at the grain

boundaries. This is attributed to the upsetting of material associated with

smaller undeformed chip thickness.

Lee et al (2007) presented and experimentally verified a dynamic

surface topography model, used to predict the local variation of surface

roughness in diamond turning of crystalline materials. The model,

incorporates the micro-plasticity theory, theory of system dynamics and

machining theory to account for materials induced vibration in ultra-precision

machining. The model predicts both the magnitude and the effect of

materials-induced vibration to provide quantitative estimates in the local

variation of the surface roughness.

Surface generation in the micro-end milling process was studied by

Vogler et al (2004). The surface roughness was found to be strongly affected

by the tool edge radius and significantly by the feed rate. It was observed that

for the 2 m edge radius, as the feed rate was reduced to a certain value, the

surface roughness started to increase, indicating that an optimal feed rate

exists that will produce the smallest surface roughness value which was

attributed to the minimum chip thickness effect without any dwelling of the

tool wedge.

Experimental studies on microburr formation in milling aluminium

and copper were carried out with a range of chip loads, tool diameters and

depths of cut by Lee and Dornfeld (2002). Different types of burr formation in

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micromilling and conventional milling such as flag-type, rollover-type, wavy-

type, and ragged-type burrs were observed. At low cutting speeds, bending of

the chip is more dominant than fracture. As the cutting edge exits from the

workpiece, the chip rolls over forming a burr. In addition, a large tool edge

radius-to-chip load ratio causes rubbing and compression (up setting) instead

of cutting and generates blocky burrs. As the depth of cut and feed rate

increased within the studied range, the burr size was found to increase.

Schmidt et al (2002) investigated the influence of material structure

on the surface quality in micromilling. In the case of mould fabrication where

highly wear resistant materials are often used, the material has to be heat

treated before microcutting to achieve reasonable surface finish.

Paulo et al (2009) studied the machinability of PA 66 polyamide

with and without 30% glass fibre reinforcing, when precision turning at

different feed rates. Four different tool materials were tested: chemical vapour

deposition diamond (CVDD) coated carbide, polycrystalline diamond (PCD)

and ISO grade K15 uncoated cemented carbides with (K15-KF) and without

(K15) chip breaker. Dry micro-turning experiments were carried out. The

addition of 30% glass fibres reinforcing on PA66 polyamide significantly

affected the performance of the tooling used in comparison with the material

without reinforcing. The radial force component presented highest values,

followed by the cutting and feed forces, owing to the fact that the depth of cut

is smaller than the tool nose radius, thus resulting in a smaller effective

cutting edge angle. In general, the PCD tool was responsible for the lowest

force values when turning both materials, followed by the K15 carbide tool,

which gave intermediate results. When cutting polyamide, the K15-KF tool

promoted higher forces, whereas the CVD diamond coated tool provided

highest forces when machining reinforced polyamide. The specific cutting

force decreased as feed rate was elevated and presented values, comparable to

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metallic alloys, nevertheless, the PA66 polyamide presented a threefold

increase in specific cutting pressure compared with the PA66–GF30

composite. Moreover, within the cutting range tested, the surface roughness

of the reinforced polyamide was shown to be insensitive to changes in the

feed rate. This trend was not observed for the polyamide, whose roughness

increased with feed rate. The PCD tool gave the lowest force values

associated with best surface finish, followed by the ISO grade K15 uncoated

carbide tool with chip breaker when machining reinforced polyamide.

Continuous coiled micro-chips were produced, irrespectively of the cutting

parameters and tool material employed. Unreinforced polyamide can be upset

in micromachining, resulting in high specific cutting pressure which accounts

for the observations on surface finish during machining.

2.4.4 Elastic-plastic Deformation

Due to the minimum chip thickness effect, the micromachining

process is affected by two mechanisms - chip removal and plowing / rubbing.

The extent of ploughing/rubbing and the nature of micro deformation during

ploughing / rubbing contribute significantly to burr formation and

corresponding variation in surface roughness (Waldorf 1999). 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 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 of

the elastic-plastic deformation in micromachining processes.

Jardret et al (1998) have performed instrumented scratch testing, on

a wide range of materials, to understand and characterize the elastic-plastic

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deformation of materials during scratching. It was observed that the common

residual scratch morphology exhibits a groove with two pile-up pads formed

by the plastic flow around the indenter. The proportion of the plastic

deformation was found to increase with the Young’s modulus (E) over the

hardness (H) ratio E/H for both polymers and metals. Especially, rigid plastic

materials like ceramics having higher E/H value can be machined in ductile

mode (upsetting and dislodgment).

2.4.5 Microstructure Effects on Machining Performance

Performance in micromachining is influenced by tool geometry,

cutting conditions and related feed rates. Micromachining related features are

also influenced by micromachining process. Since the length scale of the

crystalline grain size of most commonly used engineering materials, such as

steel, aluminium, etc., is between 100 nm and 100 m and the feature size of

micromachined 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 are discussed.

Vogler (2001) showed the presence of high-frequency components

surface profile in the ductile iron experiments but not in the ferrite or pearlite

tests as evidence that, these high-frequency components are due to the

multiphase microstructure. The surface roughness value (Ra values) for

multiphase ductile iron was larger than that for the single-phase material over

the examined range of cutting conditions. The increased surface roughness

was attributed to interrupted chip formation that occurs, as the cutting edge

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moves between the multiple phases. This hypothesis was supported by the

frequency spectrum of the surface trace. Pearlite is a mixture of phases and is

prove to generate wavy texture.

2.4.6 Process Modeling

The majority of work in this area involves the formulation and

development of either FEM techniques or MDS techniques to investigate the

influence of cutting conditions on the process parameters in two-dimensional

orthogonal cutting. Material behaviour, friction characteristics and tool

geometry are incorporated into these process models with the aim of better

describing the complex nature of micro and nano scale cutting operations.

Experimental studies are subsequently conducted to verify the applicability of

the finite element or molecular dynamics models developed. Some of the

notable findings from these modelling studies are summarized as follows:

Kim et al (1999) analyzed the effect of the tool edge radius on the

cutting process using the finite element method. The model is based on an

Eulerian formulation with tools of finite edge radius and a rigid-viscoplastic

workpiece material. The cutting forces obtained from their finite element

simulation are found to be in good agreement with their experimental data.

They therefore concluded that the major cause of size effect is the tool edge

radius. It is known that edge preparation in cutting inserts plays a significant

role in tool performance.

Woon et al (2009) performed finite element analysis (FEA) of

micromachining using the arbitrary Lagrangian–Eulerian (ALE) method. The

assumptions made in modelling of conventional machining as the undeformed

chip thickness a is very much larger than the tool edge radius r, by at least

three orders of magnitude is certainly not appropriate for micromachining

when a approaches r in the micro scale. In this regard, the differences

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between conventional machining and micromachining are believed to be

originated from the great size differences between a and r. They investigated

the chip formation mechanism and its corresponding stress states of AISI

4340 steel with finite element method (FEM) using the ABAQUS suite of

software coupled with the ALE method. They showed that chip is formed

through material extrusion under a critical a/r < 1. The changes in chip

formation behaviour are driven by intense deviatoric and hydrostatic stresses

that are highly localized around the deformation zone. The onset of such chip

formation mechanism is signified with a constant changing negative effective

rake angle that becomes stable in a later stage, when chip formation reaches a

stable tool-chip contact length i.e. upto certain size material, is upset ahead of

the cutting wedge, above which lumped upset mass is dislodged as chip.

Liang et al (1994) employed the FEM to analyze the influence of

the crystallographic characteristics of the material on the micro-cutting

process. The analysis indicated that grain orientation has a significant effect

on the (yielding) cutting force for both aluminium and copper. The cutting

force also becomes a minimum when cutting is performed along the (1 1 1)

plane when compared with the (0 0 1) and (1 1 0) planes. Furthermore, the

cutting force also changes at the grain boundary of polycrystalline materials.

This again shows, uni-direction cutting is proven to upsetting and increased

force.

In the study of tribological phenomena in nano scale machining

using MDS, Maekawa et al (1995) reported that friction and tool wear exert

the same influence in nano-cutting as that observed in macro-scale cutting.

Komaduri et al (1998) investigated the effect of tool geometry in

nano scale cutting using MDS and reported that the tool edge geometry has

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significant influence on nano scale cutting. The tool edge geometry is found

to have significant influence on the cutting and thrust forces, force ratio,

specific energy and the sub-surface deformation.

Kim et al (1999) proposed a FEM technique to predict the stress

and temperature distribution in micro-scale machining of oxygen-free-high-

conductivity copper. The results indicated that the temperature effect is a very

important factor to be considered in micro-scale cutting process due to its

influence on the flow stress distribution. The cutting force and flow stress

were over-predicted when the temperature effect was neglected. i.e thermal

induced reduction in flow stress results in lower order specific cutting force,

attributable to higher effective stress in deformation zone.

2.4.7 Ductile Mode of Machining - Observation

Machining brittle materials at the high depths-of-cut (DOC) found

in conventional machining tends to cause excessive surface and subsurface

cracking. To overcome this, machining in a ductile mode at low enough DOC

has been proposed by many researchers, including Bifano (1991). When

cutting below a critical DOC, brittle materials can be machined in a ductile

fashion with good surface finish and no surface pitting or cracking. Since the

chip thickness in micromachining can be on the order of the critical DOC,

micromachining can serve as a novel means of fabricating unique features in

brittle materials not achievable by polishing or other techniques.

Ueda et al (1991) found that some ceramic materials can be

machined in a ductile mode by decreasing the depth of cut and/or increasing

cutting speed. ZrO2 and WC-Co were easily machined in a ductile mode due

to their high fracture toughness. But Al2O3 and SiC exhibited only brittle

mode cutting with smallest possible depth of cut at that time, 2µm. The

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machining mode of Si3N4 changed from brittle to ductile as cutting speed

increased.

Blake and Scattergood (1990) investigated ductile regime diamond

turning of brittle optical components (silicon and germanium) and suggested

optimal cutting parameters such as critical depth of cut, tool geometry and

cutting speed based on an analytical model and experiments.

Nakasuji et al (1990) did similar tests with a focus on critical depth

of cut for ductile mode cutting and surface finish in machining of Ge and Si

depending on crystallographic orientation, Egashira and Mizutani (2002)

studied critical depth of cut for ductile mode microdrilling of single crystal

silicon.

Fang et al (2000) studied brittle-ductile transition of glass materials

using indentation and reported that unlike metals, glass viscously deforms

only in a very small region under hydrostatic compressive stresses at

temperatures below the softening point. They also conducted ultra-precision

turning operations on glass materials and found that a negative rake face angle

generates the necessary hydrostatic compressive stress and enables ductile

regime cutting but resulted in rapid tool wear.

Diamond turning is used as a thinning technique for silicon non-

insulator (SOI) wafers by Shibata et al (1996). Ductile regime turning based

on plastic deformation is due to phase transformations to an amorphous state

and deformation related to the {111} <110> slip system. A transmission

electron microscopic (TEM) analysis of the surface damage revealed that the

crystallographic orientation dependence of surface features was governed by

the ease with which slip deformation occurs. A slip orientation factor to

predict the surface features in the diamond turning of silicon crystal, which is

useful for the evaluation of machinability, was developed. The turning

27

mechanism in the ductile brittle transition is described qualitatively using the

slip model and slip orientation factor. Minimum cutting force along {111}

direction favours ductile machining.

Jiwang et al (2003) studied the performance of diamond cutting

tools during single point diamond turning of single-crystal silicon substrates

at a machining scale smaller than 1µm. They found that the tool wear could

be generally classified into two types: micro-chippings and gradual wear, the

predominant wear mechanism depending on undeformed chip thickness. The

tool wear causes micro-fracturing on machined surface, yields discontinuous

chips and raises cutting forces and force ratio. Experimental results also

indicate that it is possible to prolong the ductile cutting distance by using an

appropriate coolant. Even the tool wear can be treated on the lines of brittle-

ductile transition.

A theoretical analysis for the mechanism of ductile chip formation

in the cutting of brittle materials is presented by Liu et al (2004). It was found

that the ductile chip formation was a result of large compressive stress and

shear stress in the chip formation zone. Additionally, ductile chip formation in

the cutting of brittle materials can result from the enhancement of material

yield strength in the chip formation zone. The large compressive stress can be

generated in the chip formation zone with two conditions. The first condition

is associated with a small, undeformed chip thickness, while the second is

related to the undeformed chip thickness being smaller than the radius of the

tool cutting edge. Experiments for ductile cutting of tungsten carbide are

conducted. The results show that ductile chip formation can be achieved as

the undeformed chip thickness is small enough, as well as the undeformed

chip thickness is smaller than the tool cutting edge radius. More than the size

of the undeformed chip thickness, the ratio is important in deciding brittle-

28

ductile transition. It could be that working between the two feeds may be over

glass transition regime.

Jiwang Yan et al (2004) conducted single-point diamond turning

experiments to investigate the nanometric machining characteristics of CaF2.

It was found that two major types of micro fractures occur during wet cutting,

namely, Type A and Type B. The A-type micro fracture is one to 10 microns

in size and occurs under high tool feed conditions, whereas the B-type micro

fracture is on the order of 100 microns in size and occurs under extremely low

tool feed conditions. The A-type micro fracture is due to the size effect and

the crystallographic effect of the ductile-brittle transition, while the B-type

micro fracture results probably from the thermal effect. As a result, ductile

regime machining is only possible when the tool feed is between the two

critical tool feeds of these two types of micro fracturing.

Liu and Li (2001) experimentally studied ductile cutting of

tungsten carbide. An energy model for ductile chip formation in the cutting of

tungsten carbide was developed, in which the critical undeformed chip

thickness for ductile chip formation can be predicted from the workpiece

material characteristics, tool geometry and cutting conditions. The model was

verified with experimental results. The results show that when the

undeformed chip thickness was smaller than a critical value, the chip

formation was in the ductile mode, and when the undeformed chip thickness

increased in excess of the critical value, the mode of chip formation changed

from ductile to brittle. The predicted results for the critical undeformed chip

thickness corresponding to ductile cutting agree well with the experimental

results.

29

2.5 ULTRA PRECISION MACHINE TOOLS – A REVIEW ON

DEVELOPMENT

Early development of ultra precision machine tools was largely

geared towards the machining of large-scale optical devices. Precision

diamond turning machines are a typical example.

In recent years, multi-axis control ultraprecision machining centres

with varying degrees of freedom are commercially available. They are used to

produce small workpiece with complex geometries and micro scale patterns

and texture such as moulds and dies for CD pickup lenses, contact lenses,

Fresnel lenses, etc., driven by increasing marked trends in consumer products.

The efficient fabrication of these components is a matter of concern/interest

for miniaturization and integration of consumer products along with the rapid

development of micro and optical electronics (Weck et al 1988).

Currently available multi-axis controlled ultraprecision machining

centers are in fact a progressive developmental form of traditional machine

tools. These ultraprecision machine tools can be classified into several types,

based on the type of positioning mechanism used mechanisms include a

screw-based system driven by a rotary motor, linear motor drives, and a ball

screw or aero-hydrostatic screw-based system. With respect to the table slide

mechanism, two common configurations include the roller slide system or

aero-hydrostatic slides in order to feed the table with low friction and high

straightness. Bearings for rotational elements are similar to those found in the

table slide mechanism.

Young-bong et al (2005) developed a PC-based 5-axis micro

milling machine, which can be used for machining micro- sized parts, and be

easily constructed at low cost. The micro milling machine presented in this

paper is mainly composed of commercially available micro stages, and an air

30

spindle and PC-based control board. An effective method for initializing the

spindle position is proposed. Test results of the micro milling machine are

presented, which include machining of micro walls, micro columns and micro

blades.

Furukawa et al (1986) built a machine using alumina-based

ceramics for the structural members because of their high rigidity and thermal

reliability and surface-restricted type aerostatic slide ways to avoid friction.

Takeuchi et al (2000) developed a 5-axis ultraprecision milling

machine using non-friction servomechanisms for the creation of 3D micro

parts with translational resolution of 1 nm, rotational resolution of 0.00001º,

and slideway straightness of about 10 nm 200nm. The ultraprecision

machining center employs aerostatic guideways and coreless linear motors to

provide non-contact, high resolution drive mechanisms achieving 1nm motion

accuracy. To ensure thermal stability, alumina ceramics were used for

structural components.

Moriwaki and Shamato (1995) developed an elliptical vibration

milling algorithm in order to achieve additional machining precision over that

of other ultraprecision machines. The elliptical vibration milling machine

used a double spindle mechanism to generate circular vibratory motion of the

cutting tool, which resulted in improved surface finish, even with a diamond

tool on ferrous materials possibly due to intermittent contact with associated

reduction in cutting temperature.

Hara et al (1990) developed a high stiffness microcutting machine

with dynamic response up to 2 kHz, stiffness of 80 N/µm and in-feed

resolution of 5 nm. The contact between tool and the work piece was detected

through a piezoelectric actuator positioning system and a two axis micro-

pulse system controller. Werkmeister and Slocum (2003) developed a

31

mesoscale mill using wire capstan drives, ball-screw splines, and an air

bearing spindle with an integral Z-axis.

2.6 METROLOGY IN MICROMACHINING

Umeda (1996) conducted surveys on measurement technology

related to micromachining and found that measurements of material

properties, force and displacement dynamics and shape in fabrication at the

micro level were the most interesting. As the scale of features and machined

parts decreases, the resolution of techniques used to measure and quantity

these parts must increase as well.

Howard and Smith (1994) modified conventional AFM technology

to cover long ranges of surface metrology. They used a precision carriage and

slide way mechanism to cover about 20mm of travel and the AFM force

probe, which utilizes the repulsive atomic force, to generate the surface

contour.

In many cases microparts include inside features such as pockets,

holes and channels. No technology exists to measure such features. Hence,

Masuzawa et al (1993) developed a vibroscanning method to measure the

inside dimensions of micro-holes. This method is limited only to conductive

materials because it uses a sensitive electrical switch by contacting a vibrating

micro-probe onto workpiece. They added another probe utilizing contact by

bending of the probe (Kim et al 1999).

Miyoshi et al (1996) developed a profile measurement system using

inverse scattering phase retrieval method. The system was able to conduct in-

situ measurement of a surface profile with submicron accuracy. The tests on

symmetric and non-symmetric fine triangular grooves showed promising

results in reconstructing measured profiles.

32

Many researchers developed a precision CMM (Coordinate

Measuring Machine) device with micron or submicron level resolution, but

they were not sufficient for present levels of micromachining capability.

Further developments improved the resolution up to few tens of nanometer

and finally Jager et al (2000) developed a 3D-CMM with a resolution of

1.3nm using a probe and laser interferometers with angle sensors for guiding

deviation.

Cao et al (2002) developed a three dimensional micro-CMM for

precise three dimensional micro-shape measurements. For this, they also

developed a 3D opto tactile sensor for the probe using a silicon boss-

membrane with piezo resistive transducers which can simultaneously measure

deflections of the probe and force in three dimensions. The system consists of

two stage measurements; coarse and fine measurements with a resolution up

to 1.22nm and uncertainty less than 100nm.

Okuda et al (2003) demonstrated that ductile and brittle cutting

modes could be detected by use of an AE sensor and tool force measurements,

and ductile-mode cutting for brittle materials were achieved.

2.7 TOOL WEAR IN TURNING

Dutta et al (2006) fabricated Alumina-based composites with

different amounts of silver as second phase using conventional powder

metallurgical route. Cutting tool inserts fabricated from these composites

were subjected to dry turning operations. Within the investigated range,

abrasion and plastic deformation are considered to be the active wear

mechanisms for the developed inserts.

Samir et al (2007) investigated the tribological influences of PVD-

applied TiAlN coatings on the wear of cemented carbide inserts under dry and

33

wet machining. Micro-wear mechanisms identified in the tests through SEM

micrographs include edge chipping, micro-abrasion, micro-fatigue, and

micro-attrition.

Konig and Neises (1993) examined basic wear mechanisms of

polycrystalline diamond (PCD) and polycrystalline cubic boron nitride

(PCBN) composite cutting materials The diminution of the abrasion

resistance of the cutting material owing to graphitization seems to be the

dominant wear mechanism when turning a titanium alloy at elevated cutting

speed with PCD composites. PCBN samples, exposed to diffusion tests with

steel as the workpiece material at elevated temperatures up to 950º C suffered

significant changes in the structure of the binder. This seems to result in a

reduction of the bulk material resistance to abrasive wear.

Tansel et al (2000) introduced a Neural-Network-based Periodic

Tool Inspector (N2PTI) to evaluate tool condition periodically on a test piece

during the machining of non-metal workpiece. The cutting forces are

measured when a slot is being cut on the test piece and the neural network

estimates the tool life from the variation of the feed- and thrust-direction

cutting forces. The performances of three encoding methods (force variation,

segmental averaging and wavelet transformations) and two neural networks

[backpropagation (BP) and probabilistic neural network (PNN)] are

compared.

Venkatesh et al (1996) machined low carbon steel using three tools

with side cutting edge angles (SCEA's) of -5 °, 0 °, and +15 °. Cutting forces

were measured and friction and shear plane angles determined. Surface

roughness and roundness measurements were made. Results indicate that the

tool with -5 ° SCEA performed better than the other two.

34

Based on orthogonal cutting data from machining and two wear

characteristic constants Usui et al (1984) developed an analytical method to

predict the crater and flank wear of tungsten carbide tools for a wide variety

of tool shapes and cutting conditions in practical turning operations. Using

these predicted results, stress and temperature on the wear faces have been

calculated. The predicted wear progress and tool life are in good agreement

with experimental results.

Manoj Kumar et al (2007) investigated the performance of cermet

cutting tools against boiler steel under orthogonal cutting conditions. With the

increase in speed and feed rate, cutting becomes steady with a consequent

reduction in the cutting force. Based on the detailed SEM investigation, it was

found that tribochemical wear and abrasion are the general crater wear

mechanisms of the investigated cermets.

Kevin Chou and Jie Liu (2005) investigated turning of Metal

Matrix Composites (MMC) of aluminium-alloy reinforced with silicon-

carbide particles using CVD diamond-coated tools. The results show that tool

wear is sensitive to cutting speed and feed rate, and the dominant wear

mechanism is coating failure due to high stresses. High cutting temperatures

induced greater interfacial stresses at the bonding surface due to different

thermal expansions between the coating and substrate, and plausibly result in

the coating failure. Possibility of SiC-diamond interaction leads to

graphitisation of diamond can also lead to failure.

Abu Zhara and Yu (2003) used discrete wavelet transforms of

ultrasound waves to measure the gradual wear of carbide inserts during

turning operations. Normalized signals were used to search for a neural

network architecture that correlates the ultrasound measurements to the wear

level on the tool. A three-layer Multi-Layer Perceptron architecture yielded

35

the best correlation (95.9%) using the wave packets from the fourth level of

decomposition with frequencies 3.75–4.375 and 5.625–6.875 MHz.

Luo et al (2005) investigated the intrinsic relationship between tool

flank wear and operational conditions in metal cutting processes using carbide

cutting inserts. A new flank wear rate model was developed to predict tool

flank wear land width. A set of tool wear cutting tests, using hard metal

coated carbide cutting inserts, were performed under different operational

conditions. The wear constants in the proposed wear rate model were

determined based on the machining data and simulation results. A good

agreement between the predicted and measured tool flank wear land width

showed that, the developed tool wear model can accurately predict tool flank

wear to some extent.

Muammer et al (2007) studied the effects of cutting speed and

cutting tool geometry on cutting forces in orthogonal cutting of nickel-base

super alloy, Inconel 718 with dry cutting conditions with ceramic cutting tools

in two different geometries and three different material qualities. Plastic

deformation, flank edge wear, notch and build-up edge are observed in high

cutting speeds.

Tugrul and Yigit (2005) developed a neural network model to

predict surface roughness and tool flank wear over the machining time for

variety of cutting conditions in finish hard turning. Predictive neural network

models are found to be capable of better predictions for surface roughness and

tool flank wear within the range that they had been trained. Decrease in the

feed rate resulted in better surface finish but slightly faster tool wear

development, and increasing cutting speed resulted in significant increase in

tool wear development but resulted in better surface finish. Increase in the

workpiece hardness resulted in better surface roughness but higher tool wear.

36

If the tool wear is over the nose region, increased nose wear can impart good

finish at the cost of lay pattern and cutting forces.

Wang et al (2003) conducted orthogonal machining on mild carbon

steel using grade P20 carbide flat-top inserts with 8µm TiN coating. The

study showed that tool flank wear does not statistically affect the basic cutting

quantities such as the shear angle and shear stress, both qualitatively and

quantitatively, but results in an additional rubbing or ploughing force on the

wear land. The study also showed that tool flank wear results in a substantial

increase in the force components and that the thrust force is more sensitive to

tool flank wear.

Reginaldo et al (2007) discussed the results of tool wear, cutting

force and surface finish obtained from the turning operation on hardened AISI

4340 using PCBN coated and uncoated edges. Three different coatings were

tested using finishing conditions: TiAlN, TiAlN-nanocoating and AlCrN. The

lowest tool wear happened with TiAlN-nanocoating followed by TiAlN,

AlCrN and uncoated PCBN. Usually coatings will not sustain stretching over

the flank region. They fail under flank wear, however regarding crater wean

coatings are successful.

Meng et al (2004) studied the effects of tool nose radius and tool

wear on residual stress distribution in hard turning of bearing steel JIS SUJ2.

Three types of CBN tools with different nose radii (0.4, 0.8 and 1.2 mm) were

used in this study. The results show that the tool nose radius affects the

residual stress distribution significantly. Especially the effect on the residual

stresses at the machined surface at early stage of cutting process is

remarkable. For the tool wear, as the tool wear increases, the residual stress at

the machined surface shifts to tensile stress range and the residual

compressive stress beneath the machined surface increases greatly. During

early stage of machining CBN tool undergoes nose deformation involving

37

larger nose radius, this causes larger cutting force and appreciable residual

stress.

Klimenko et al (1992) investigated wear of PCBN tools when

machining hardened steels and found formation of a built-up layer at the

work–flank interface due to chemical interactions of tool with work material

and atmosphere. These reactionary products were found to consist of borides,

carbides, nitrides and oxides of elements iron/chromium/titanium from work

piece/tool. They concluded that wear of PCBN tools is mainly due to

chemical wear.

Arsecularatne et al (2006) investigated the wear mechanisms of

cutting tools made of tungsten-carbide (WC), PCBN and PCD using the tool

life and temperature results available in the literature. It was concluded that

the most likely dominant tool wear mechanism for WC is diffusion and that

for PCBN is chemical wear. They conclude that for PCD, more experimental

results and hence further research is required to determine the dominant wear

mechanism.

Che-haron (2001) investigated turning of Ti-6Al-2Sn-4Zr-6Mo

using uncoated cemented carbide tools. The results have shown that the

inserts with finer grain size have a longer tool life. Majority of the tool failure

mechanisms was due to flank face wear and excessive chipping on the flank

edge. The machined surfaces experience microstructure alteration and

increment in micro-hardness on the top white layer of the surface. Machined

surfaces have shown severe plastic deformation and hardening after

prolonged machining time with worn tools, especially when machining under

dry cutting conditions.

Ming et al (2006) carried out cutting tests on glass to investigate the

wear of diamond tools under different cutting conditions. The microstructure

38

of the tool wear zone shows strong evidence that cleavage and micro-chipping

could be the dominant wear mechanisms in cutting without vibration. It was

confirmed experimentally that the cutting performance, in terms of the tool

wear and surface finish was improved significantly by applying ultrasonic

vibration to the cutting tool. The ultrasonic vibration can induce

erosion/cavitation effects in glass, thereby reducing the load on diamond tool

and improving the tool performance.

Hakan et al (1993), investigated the wear behaviour of Ti(CN)

based cermet tool material. The study revealed the presence of conventional

flank wear, varying degrees of crater wear and negligible notch wear, while

plastic deformation of the cutting edge appears to be the life-limiting factor.

Metallurgical interaction was also observed between the constituents of the

tool material and the chip material or reaction products of inclusions, resulting

in the embrittlement of the tool material and facilitating rapid wear.

Yuanyuan et al (2003) used M2 high-speed steel tool and YW1

cemented carbide tool to turn a high strength wear resisting aluminium bronze

(KK). The adhered bronze on each of the samples is a single non-layered

structure with a thickness of not more than 100 µm. It shows no

characteristics of a built-up-edge. The thickness and degree of adhesion

depend on the temperature distribution on the tool surface. Groups of droplet

like particles originating from partial melting of the HSS tool surface were

found adhered on the open surface of the adhered KK layer. The adhesion that

was found on the YW1 cemented carbide tool is more even and thinner than

that found on the HSS tool.

Jawaid et al (1999) investigated the behaviour of titanium alloys

during the processes of turning. The dominant wear mechanisms of cemented

WC-Co tools were dissolution-diffusion and attrition, causing the

plucking/pulling-out of carbide from the tool. Abrasion wear mechanisms

39

dominated in wear at the flank face and tool nose. At higher cutting speeds,

the tool failure was due to maximum flank face wear and excessive chipping

on the flank edge. The results have shown that inserts with fine grain size and

a honed edge have a longer tool life.

Kevin Chou and Hui Song (2004) investigated tool nose radius

effects on finish turning of hardened AISI 52100 steels. Results show that

large tool nose radii only give finer surface finish, but comparable tool wear

compared to small nose radius tools. Specific cutting energy slightly increases

with tool nose radius. Smaller tool nose radius gives larger uncut chip

thickness, and thus, greater shear plane heat source that may induce deeper

white layers for new tool conditions. With larger nose radius, cutting

force/specific cutting pressure will increases and finish will improve.

Venugopal et al (2006) evaluated the tool wear in machining

titanium alloys and the wear due to adhesion-dissolution-diffusion of tool

material into the flowing chip at the tool-chip interface. Prolonged machining

at high cutting velocities will lead to increase in thickness of the interface

layer on the rake face on account of increased chemical reactivity of titanium.

At some point, this layer of adherent material is torn and transported by the

flowing chip underside. This inevitable leads to pulling out of the grains from

the tool resulting in aggressive crater wear.

Hughes et al (2006) observed that the predominant wear

mechanism observed under all cutting condition of machining titanium was

attrition wear. A built-up layer of titanium was seen welded to the tool surface

on both flank and crater faces, due to high pressure and temperature

developed during cutting. This adhering layer formed due to titanium’s high

chemical affinity for the tool material is eventually pulled away from the tool

surface taking with it carbide grains. This process of attrition exposes fresh

carbide grains and the cycle repeats.

40

Gregory et al (2005) proposed a new method for measuring tool

wear using a high-frequency wireless transmitter/receiver alone, without a

vibrating string. The authors have not conclusively determined the reason that

the Doppler radar detector responds to metal cutting operations. However the

authors believe that the sensor responds to metal-metal contact noise

generated during the cutting process. The authors have conducted simple

experiments to show that the sensor responds when two pieces of metal are

struck against each other, outside the CNC lathe cutting environment. They

have also conducted experiments to show that the Doppler radar detector

responds to metal-metal contact noise rather than, or more strongly than, to

signals generated by a vibrating string.

2.8 MODELING AND OPTIMISATION STUDIES

Tian-Syunglan et al (2009) selected the L9 (34) orthogonal array of

a Taguchi technique for optimizing the finish turning parameters on ECOCA-

3807 CNC lathe. The surface roughness (Ra) and tool wear ratio are primarily

observed as independent objectives for developing two combinations of

optimum single objective cutting parameter. From the machining results, it is

shown that both tool wear ratio and material removal rate (MRR) from

optimum competitive parameters are greatly advanced with a minor reduction

in the surface roughness in comparison to those of benchmark parameters.

Anne et al (2004) developed a new chip-thickness model for the

performance assessment of silicon carbide grinding by incorporating the

modulus of elasticity of the grinding wheel and the work piece in existing

basic chip-thickness model, to account for elastic deformation. The new chip-

thickness model is more accurate in predicting the surface roughness and its

trend when compared with the existing model. The new model has been

validated by conducting experiments, taking the surface roughness as a

parameter of evaluation.

41

Ramon et al (2008) developed two models to predict tool wear for

different values of cutting speed, feed and time, one of them based on

statistical regression, and the other based on a multilayer perception neural

network. Parameters of design and the training process, for the neural

network, have been optimised using the Taguchi method. Outcomes from the

two models were analyzed and compared. In comparing the neural network

model with the statistical multiple regression, it has been shown that the

neural network allows obtaining more accurate predictions for the tool wear

under the conditions studied.

Ojha and Dixit (2005) developed a procedure to estimate tool life in

an economical and reliable manner. Neural networks were used to find lower,

upper and most likely estimates of tool life. It is observed that in most of the

cases, the experimental value is close to the predicted, most likely estimate

and within the upper and lower estimates. In a few cases, the predicted values

differ considerably from experimental values. This is because of the presence

of many random factors. However, it is seen that neural networks provide

much better estimates of tool life as compared to multiple regression.

Aravindan et al (2006) investigated the machining characteristics of

GFRP pipes based on surface roughness and tool wear. Experiments were

conducted through the established Taguchi’s design method. Both simple

regression and cross product regression methods were employed and their

suitability was also studied. The simple regression model deviates the

combined objective by 6.31% and the cross product regression model deviates

by 15.27%, and, hence, the simple regression model is suggested as being

well suited model for this particular application.

Aminollah et al (2008) investigated the effects and the optimised

the machining parameters on surface roughness and roundness in the turning

wire electrical discharge machining (TWEDM) processes. The optimum

42

machining parameters combination was obtained by using the analysis of

signal to-noise (S/N) ratios. The variation of surface roughness and roundness

with machining parameters was mathematically modeled using the regression

analysis method. Finally, experimentation was carried out to identify the

effectiveness of the proposed method. The confirmation tests indicate that it is

possible to decrease the surface roughness and roundness significantly by

using the proposed statistical technique.

Sanjay and Jyothi (2004) conducted a study to identify suitable

parameters for the prediction of surface roughness. Back propagation neural

networks were used for the detection of surface roughness. The best structure

of neural network was selected based on criteria including the minimum of

sum of squares with the actual value of surface roughness. The estimated

values of surface roughness obtained by neural network analysis matched well

with the actual values, as compared to the estimated values obtained by

mathematical analysis. The results obtained by neural network structures are

more accurate and reliable for all the combinations of cutting speed and feed.

Dilbagh Singh and Venkateswara Rao (2007) conducted an

experimental investigation to determine the effects of cutting conditions and

tool geometry on the surface roughness in finish hard turning of the bearing

steel (AISI 52100). Mathematical models for the surface roughness were

developed using the response surface methodology. First order surface

roughness prediction model has been found to represent the hard turning

process very well. This model would be helpful in selecting the tool geometry

and cutting conditions for the required surface quality. This was also used for

optimisation of the hard turning process.

Eyup Bagci and Babur Ozcelik (2006) studied the effects of

drilling parameters on the twist drill temperature and thrust force in the dry

drilling of Al 7075-T651 material. The settings of drilling parameters were

43

determined using the Taguchi experimental design method. An orthogonal

array, the signal-to-noise (S/N) ratio, and the analysis of variance (ANOVA)

are employed to analyze the effect of drilling parameters. The drilling depth

has a greater influence on the twist drill bit temperature. The feed rate and

spindle speed has no significant influence on the drill temperature. The

spindle speed has a greater influence on the thrust force value. The feed rate

has a smaller influence on the thrust force value. The study shows that the

Taguchi method is suitable to solve the problems with a minimum number of

trials as compared with a full factorial design. It is surprising to observe that

drill temperature is insensitive to drill speed and feed.

Srijib et al (2008) studied Laser Micromachining of tungsten-

molybdenum high speed steel (Rex M2) and used an advanced optimisation

strategy to determine the optimal combination of control parameters.

A cascade forward back-propagation neural network is used to construct the

LBM process model. It has been found that among several neural

configurations, a cascade forward back-propagation ANN of type 4-25-2, one

hidden layer with 25 neurons can provide the best prediction. The validation

experiments for the developed model were conducted and it was found that

prediction accuracy of the model is quite good.

Oktem (2009) proposed a surface roughness model and optimised

the cutting parameters during end milling of AISI 1040 steel material with

TiAlN solid carbide tools under wet condition. Artificial neural network

(ANN) based on Back-propagation (BP) learning algorithm is used to

construct the surface roughness model exploiting a full factorial design of

experiments. Genetic algorithm (GA) supported with the tested ANN is

utilized to determine the best combinations of cutting parameters to lower

surface roughness through optimisation process. GA improves the surface

roughness value from 0.67 to 0.59 µm with approximately 12% gain. Then,

44

machining time has also decreased from 1.282 to 1.0316 min by about 20%

reduction based on the cutting parameters before and after optimisation

process using the analytical formulae.

Bouzid Sai (2005) investigated tool wear in high speed turning of

AISI 4340. A wear equation is proposed to describe the three stages of the

wear process. In order to find tool life, the wear criterion is defined in

connection to cutting speed. For cutting speeds higher than 650 m/min, the

tool life remains constant, so it is advantageous to use high values of cutting

speed. A mathematical relationship has also been developed to determine the

tool life for any value of cutting speed. A good correlation was found between

this result and experimental values of the wear.

Natarajan et al (2007) used back-propagation feed forward artificial

neural network (ANN) for tool life prediction. A particle swarm optimisation

technique instead of back propagation algorithm has been used, with

speed(S), feed (F), depth of cut (D) and flank wear width (Vb) as input vectors

and cutting tool life as an output vector. The network was trained using

25 patterns for various numbers of nodes in hidden layers. Using weights

obtained during the training, fresh patterns were tested and compared with the

experimental results. The results of the proposed method are more or less the

same and better than the back propagation method.

Palanikumar (2008) applied Taguchi and response surface

methodologies for minimizing the surface roughness in turning glass fiber

reinforced (GFRP) plastics with a polycrystalline diamond (PCD) tool. The

effect of cutting parameters on surface roughness is evaluated and the

optimum cutting condition for minimizing the surface roughness is

determined. A second-order model has been established between the cutting

parameters and surface roughness using response surface methodology. The

developed model can be effectively used to predict the surface roughness with

45

95% confidence intervals. The predicted values are confirmed by using

validation experiments.

Aris and Cheng (2008) investigated the characteristics of the

surface functionality generated from precision turning processes with an

integrated modeling, simulation and machining verification approach. The

machined trials show the modelling approach can accurately present the

machining process and the effects of machining process variables and surface

generation. They concluded with discussions on the applications and potential

of this approach for the achievement of high quality surfaces, optimisation

and control of their functional performance at the machining stage.

Saravanan and Sachithanandam (2001) developed a genetic

algorithm (GA) based optimisation procedure to optimise the surface grinding

process using a multi-objective function model. The procedure evaluates the

production cost and production rate for the optimum grinding conditions,

subject to constraints such as thermal damage, wheel-wear parameters,

machine-tool stiffness and surface finish. A worked example is used to

illustrate how this procedure can be used to produce optimum production rate,

low production cost, and fine surface quality for the surface grinding process.

Yang (1998) in his study, used orthogonal array, the signal-to-noise

(S/N) ratio, and the analysis of variance (ANOVA) to investigate the cutting

characteristics of S45C steel bars using tungsten carbide cutting tools. Based

on the DOF the L9 orthogonal array (4 Columns and 9 Rows) having 8 DOF

is selected and thus only 9 experiments are performed. S/N Ratio is selected

for the Tool Life and The Lower- the- Better is selected for the surface

roughness. Machining using the optimal cutting parameters arrived at resulted

in tool life and surface roughness improvement up to 2.5 times compared to

the initial parameters.

46

2.8 MACHINING OF COPPER – A REVIEW

Kim and Kim (1995) have shown analytically that in macro-

machining, shear takes place along the shear plane, and in micromachining,

the shear takes place around the cutting edge ( ahead of stagnant zone).This

supports the occurrence of upsetting of the material ahead of the cutting

wedge in micromachining. Their orthogonal microcutting force analytical

model considered the recovery of work piece along the clearance of the tool

and the ploughing effect by the tool edge radius. They have estimated the

elastic effect by simulating the cutting forces and concluded that the cutting

forces are different from the sharp edged model.

Lucca et al (1993) have studied the effect of a single crystal,

diamond tool edge geometry rake angle and tool edge radius on the resulting

cutting and thrust forces and specific energy in ultra precision orthogonal

flycutting on copper. Both the nominal rake angle and the tool edge profile

were found to have significant effects on the resulting forces and the energy

dissipated over a range of uncut chip thicknesses from 20 µm to 10 nm .When

the uncut chip thickness approached the size of the edge radius, the effective

rake angle appeared to determine the resulting forces. At small uncut chip

thickness, the effective rather than the nominal rake angle dictates the

direction of the resultant force. With very fine feed (uncut chip thickness) the

cutting edge dominates the cutting, with large negative angle and high force.

King et al (1976) have studied the imperfections in the finish of

high reflectivity copper material surfaces and of motor car pistons machined

with diamond tools, inspected by optical interference and scanning electron

microscopy, and related to the shape and morphology of the diamond. They

have also mentioned the interesting problem posed by the mechanism

responsible for the production of the negative-rake wear land occurs while

machining copper. It is clear that this orientation of the wear land results from

47

the tendency of the billet to impose its own shape on the tool, combined with

the diamond's well known property of being much easier to abrade in some

directions than others. The exact shape of the land is thus a compromise

between these opposing factors, as is shown by the fact that the angle of the

wear land varies with the crystallographic orientation of the diamond. Finally

it has been concluded, that the most obvious defects on the machined copper

disc are the patches, spiral lines and scratches, arise whether the tool is cutting

with or without lubricant, the fine machined structure is adversely affected by

lack of lubricant and mainly crystallographic orientation of the diamond

tipped cutting tool. Diamond is known for its rapid cleavage action, so when

the crystallographic orientation tends to resist machining, the diamond

undergoes cleavage/self-sharpening, resulting in wear land.

Lee et al (1994) have experimentally studied on the heat transfer

analysis and life of metal cutting tools in the process of turning the work piece

materials like copper, cast iron, aluminium and steel. In this paper, the heat

analysis is performed using both thermocouples and infrared thermo vision to

monitor time wise temperature variations in the tool and work piece in

orthogonal cutting. Further, the semi-empirical formula is derived to express

the temperature in time history of the tool surface, using a local element

lumped conduction equation with experimental data-fitting. Finally, Infrared

thermo vision identifies the location of the maximum tool temperature slightly

inward from the cutting edge. An optical and electronic microscope reveals

the formation of micro pits (crater), originates in the region around the

maximum tool temperature. It is disclosed that the progression of wears is

accompanied by a consistent increase in the tool temperature which in turn

accelerates the wear process, i.e. crater wear is mostly temperature dependent.

Takashi et al (1998) have conducted experimental and also

theoretical investigations of the temperature on the rake face of a single

48

crystal diamond tool in precision turning of copper work material. The

infrared rays radiated from the contact area between the chip and rake face,

and transmitted through the diamond tool, are accepted by a chalcogenide

fiber and led to a two-color detector which consists of lnSb and HgCdTe

detectors, which can monitor the temperature variations in accurate manner.

The temperature distribution in the tool and in the copper work piece is

calculated theoretically (numerically) using FEM. From the results they have

found that the maximum temperature on the rake face is found to be

approximately 220°C for copper at a cutting speed of 620 m/min. The

temperature increases with the increase of cutting speed for the range of

cutting speeds.

Pei et al (2006) have studied Molecular Dynamics (MD)

simulations of the nanometric metal cutting process of copper. In their

approach, the many-body embedded atom method (EAM) potential was used

for the atom interaction in the copper work piece. The effect of the tool

geometry on the cutting process was investigated. Finally it was observed that

with negative rake angle of the cutting tool and also due to the larger plastic

deformation (up setting) generated in the copper work piece material made

the size of the chip smaller. It was shown that as the rake angle changed from

-45° to +45°, the machined surface became smoother. Besides, both the

cutting forces and the ratio of normal force to tangential force decrease

considerably with the rake angle changing from negative to positive. In

addition, MD simulations with the two-body Morse potential instead of the

EAM potential were also carried out to study the effect of different potentials

on the simulation results. It was found that there is no big difference in the

simulated chip formation and the machined surface under the two different

potentials. However, the Morse potential results in about 5–70% higher

cutting forces than the EAM potential. It is recommended that the EAM

potential should be used for the MD simulations of nanometric machining

49

processes. In this paper it has been clearly demonstrated the effect of rake

angle on the rate of cutting force needed, for the machining of copper

materials, increasing rake angle of the tool increases (in negative regime)

cutting force.

Su et al (2006), have utilized the tribological characteristics and

cutting performance of coating on the carbide cutting tools while micro

drilling of (dry machining) of copper work piece material. The coating

provided on the carbide tool is chromium carbide (Cr-C). The microstructures

and mechanical properties of Cr-C coatings have been evaluated through

scanning electron microscope (SEM), nano-indentation and scratching.

Experimental results indicate that the coating microstructure, mechanical

properties and wear resistance vary according to OEM set values. From the

process analysis they have finally concluded that service life of the carbide

cutting tool with 50% of Cr-C coating is four times higher than that of an

uncoated cutting tool in the micro-hole drilling of copper experiments. Also

the comparative statement of coating effect on machining of copper and steel

is like all % of Cr-C coatings with 1 and 5 µm thickness perform poorly.

Cutting tools of 3 µm with 10% of Cr-C film have the best wear resistance in

turning AISI 1045 steel. On the other hand, 3 µm with 50% of Cr-C coating

permits an increase of two times the service life of the carbide tool in turning

copper.

Sinan et al (2007), have conducted experiments and did effective

investigation of micro-machinability during micro milling of oxygen-free

high conductivity (OFHC), commercially pure copper 101 (work piece

material) using tungsten carbide micro-end mills (cutting tools). The cutting

forces, surface roughness, tool wear, and burr formation are analyzed under

varying cutting speeds (40, 80, and 120 m/min) and feed rates (0.75, 1.5, 3

and 6 mm/flute). The experiments included full-immersion cutting with

50

254 mm micro-end mills with an axial depth of cut of 30 mm traverse. The

variation of forces, surface roughness, and burr formation with wear

progression is also studied. It was seen that the minimum chip thickness and

associated plowing/indentation effects induce erratic variations in micro

milling of copper materials, increasing forces with feed rates in the vicinity of

edge radius of the micro end mills. At larger feed rates, the micro milling

forces resemble those of conventional milling in the presence of tool-tip run

out. The surface roughness was observed to be nearly constant at feed rates up

to 3 mm /flute, and to increase with feed rate for larger feed rates. Unlike

conventional milling, greatest tool wear was experienced at the lowest feed

rate and lowest speed, and the lowest wear was seen at the highest feed rate.

The main mechanism of wear was concluded to be attrition wear in its most

basic form, whereby the tungsten–carbide grains on the cutting edges were

dislodged from the cobalt matrix. The smallest top burrs were experienced at

40 m/min surface speed at higher feed rates. The lowest feed rate of

0.75 mm/flute resulted in large burrs at any speed. Progressive wear was seen

to induce an increase in forces, a reduction in surface finish, and a strong

increase in burr formation. Also the specific energies in micromachining

exhibit the well-known size effect phenomenon. In general, lower specific

energies are seen in lower speeds, possibly due to the strong effect of strain

hardening in the absence of significant thermal softening of the work piece

material.

Panda et al (2006) have experimentally predicted the drill tool flank

wear while machining copper alloys using radial basis function neural

network methods. In their work different types of artificial neural network

(ANN) architectures have been used in an attempt to predict flank wear in

twist drill. In their work, a standard radial drilling machine was used for the

drilling operation. High speed steel (HSS) drills with different diameters have

been used for drilling copper work piece at different cutting conditions. Root

51

mean square (RMS) values of thrust force and torque signal were recorded

through a piezo-electric dynamometer (Kistler). Signals from the

dynamometer were passed through low pass filter, amplified through charge

amplifier (B&K, 2525), and stored in the computer through a data acquisition

system (Advantech, PCL 818 HG, 100 KHz sampling rate). The digital

microscope along with Carl-Zeiss software interfacing has been used to

measure flank wear. The maximum flank wear was used as the criterion to

characterize the drill condition, and was obtained by measuring the wear at

different points on either of the cutting edges. Flank wear in drill bit depends

upon speed, feed rate, and drill diameter and hence these parameters along

with other derived parameters such as thrust force and torque have been used

to predict flank wear using ANN. It has been observed that radial basis

function neural network can be trained well and the trained network can

predict drill were within an error of and 15%. It has also been concluded from

the present work that fixed center radial basis function neural network can

learn much faster when the trained data is fed in sample mode compared to

batch mode data feeding and also compared to self organized method (SOM).

Lee et al (2000) have studied the effect of crystallographic

orientation in diamond turning of copper single crystals. The face cutting

experiments were basically divided into three groups, i.e. Groups A, B and C.

Group A included those cutting tests for studying the mechanisms of the

surface roughness generation and the patterns of the variation of surface

roughness with crystallographic orientation of the copper work piece. In

Group B, the effects of crystallographic orientation on the surface roughness

were investigated under various feed rate conditions. For measuring

quantitatively the local variation of surface roughness, a parameter named

Degree of Roughness Anisotropy (DRA) was used which is the ratio of the

standard deviation and the mean of the arithmetic roughness values at a finite

number of equally angular spaced radial sections of the machined surfaces.

52

Experimental results indicate that the chip thickness and shear angle varies

considerably with the changing crystallographic orientation of materials being

cut. Anisotropy in surface roughness occurs when the cutting direction

relative to the crystal orientation varies successively as encountered in facing.

There exists cyclic variation of surface roughness per work piece revolution

which is closely related to the crystallographic orientation of the crystals

being cut. Such anisotropy of surface roughness can be minimized with an

appropriate selection of the feed rate. A lamella-structure is observed on the

free surface of all the chips examined, which indicates a highly

inhomogeneous strain distribution in the chips. The surface underneath, which

is in contact with the tool, is found to be much smoother, and possesses long

scratch marks on it which is similar to those observed on a freshly machined

surface. Considerable variation in the chip thickness affords evidence that the

shear angle varies with the changing crystallographic orientation of materials

being cut.

Tanaka et al (2000) have investigated the mechanism of cutting

edge chipping in diamond turning of copper in terms of the change in strength

of diamond specimens subjected to thermal histories. The study suggests that

the strength of diamond decreases as the result of the propagation of existing

surface micro cracks caused by the thermo-chemical erosion of oxygen at the

crack tips. The catalytic reaction involving copper is also shown to accelerate

the crack propagation. Then, a cutting technique with reduced oxygen

atmosphere is proposed to suppress the cutting edge chipping in diamond

turning of copper over an extended cutting time. The results suggest that the

strength of diamond in contact with copper at elevated temperatures decreases

due to a thermo-chemical erosion at the atomic scale at the crack tip, together

with a catalytic reaction of copper and ambient oxygen.

53

Marcos et al (2005) have studied on the surface roundness error of

cylindrical bars turned of copper and aluminium alloys. In their work, macro

geometric deviations of Cu-Al cylindrical bars are analyzed (roundness),

when turned dry under some conditions of cutting (cutting speed and feed).

Also, an exponential relation between roundness and the cutting parameters is

established for the dry turning process of alloy bar. The obtained relationship

allows prediction of the behavior of the deviations in the range of cutting

speed and feed considered. Roundness deviation has been analyzed in the

turning of copper– aluminium alloy with constant depth of cut. This study has

revealed that as speed increases and feed decreases loss of precision in

roundness tends to increase. The data obtained have allowed to establish a

parametric model for the deviation of roundness that shows that the largest

variation in this variable is caused by the feed. With reduction in feed the

tendency to rubbing increases, inducing tool-tip oscillations and change of

depth of cut/dimension error.

Choudhury et al (2003) have investigated role of temperature and

surface finish in copper turning and also prediction of tool wear using neural

network. In the present work, flank wear, surface finish and cutting zone

temperature were taken as response (output) variables measured during

turning and cutting speed, feed and depth of cut were taken as input

parameters. Predictions for all the three response variables were obtained with

the help of empirical relation between different responses and input variables

using design of experiments (DOE) and also through neural network (NN)

program. Predicted values of the responses by both techniques, (DOE and

NN) were compared with the experimental values and their closeness with the

experimental values was determined. Relationship between the surface

roughness and the flank wear and also between the temperature and the flank

wear were found out for indirect assessment of the flank wear through surface

roughness and cutting zone temperature. From their results they found the

54

average error is 5.66% for NN as compared to 6.87 in case of DOE

predictions of flank wear. While in case of temperature and surface roughness,

both values, give results within 2% error and therefore both of them can be

termed as equally reliable. Flank wear does not influence much on surface

roughness.

Rahman et al (2001) have studied the failure mechanism and

factors which affect the micro end mill, during machining of pure copper

work piece material; the machining operations were performed at various

cutting speed, depth of cut and feed rates to identify the failure mechanisms

using different helix angles. Chips observed were spiral in shape based on the

cutter geometry. The chip size drastically differs from the conventional

cutting, but the chip shape remains the same. Both continuous and broken

chips were formed. Tool wear increases with the machining time and has a

significant effect on the cutting forces. The cutting forces were small

compared to conventional cutting. Both feed and radial forces were

proportional to the feed rate and depth of cut. Cutting forces increase with

time as the wear progresses. From this experiment, it is concluded that the

helix angle plays an important role in chip disposal. It is also observed that

increase in depth of cut increases the tool life, which is quite unique and

differs from the mechanics of the machining of copper materials. Increased

depth of cut could have minimized the ploughing tendency and consequent

improvement in tool performance.

2.10 MACHINING OF BRASS

El-Axir (2002) developed a experimental model which has the

capability of predicting residual stress profile of five different materials

namely; stainless steel-304, steel-37, 7001 and 2024-aluminium alloys and

brass machined by turning utilizing one of experimental design techniques

based on response surface methodology. The residual stress distribution in the

55

machined surface region was determined using a deflection-etching

technique. It was proposed that the residual stress profile is a deterministic

function of the three input parameters used. Also, it was postulated that the

residual stress profile along the depth beneath surface is a polynomial

function of the depth beneath surface and the coefficients of this polynomial

are, in turn, functions of the input parameters. The model developed has been

checked for accuracy.

Vilarinho et al (2005) studied the effects of the chemical

composition of brasses, considering each alloying element and the effective

copper content, upon the machinability. Machinability tests have been carried

out on a CNC lathe under lubricated conditions. The study includes both

commercial alloys and samples prepared in laboratory. The experimental

procedure consists of turning operations, during which cutting forces and

surface roughness obtained with brass workpiece are measured. The chip

class is accordingly evaluated. Hardness of ternary pure alloys, namely of the

Cu–Sn–Zn and Al–Cu–Zn systems, seems to play an important role in

machining characteristics. However, no correlation has been found between

this property and the machining behaviour of commercial brasses. Concerning

chip class, only one of the ternary pure alloys, from the Al–Cu–Zn system,

reveals a loose arc chips class similar to that obtained in the machining of

commercial brasses. The literature refers that the presence of phase is

responsible for promoting the chip fragmentation; however, the obtained

results show that this is true only if lead is present, otherwise the chips belong

to washer-type helical or tubular chip classes. Presence of aluminium can lead

to segmented chip formation.

Choudhury (1999) experimentally investigated orthogonal turn-

milling of brass and mild-steel workpiece using the planning of experimental

technique to study the surface-finish achieved. Experimental results show that

56

in orthogonal turn-milling, the surface-finish of the machined surface

improves with increase in cutter speed and deteriorates with increase in axial

feed rate. The surface finish achieved by orthogonal turn-milling is about 10

times higher than that achieved by turning. Also, the chips produced in

orthogonal turn-milling are very small as compared to the chips produced in

turning. The helical lay attached to normal turning facilitates longer chips.

Childs et al (1979) conducted orthogonal planning experiments on

70/30 brass workpiece in the cutting speed range 0.3-150 ft/min

(0.1-50 m/min). Measurements of chip radius, shear plane angle and the

average coefficient of friction between chip and tool have been made and

their inter-relationships are successfully explained in terms of a slip line field

theory. Lathe turning experiments on a large number of different metals in the

speed range 200-800 ft/min (60-250 m/min) indicate that for these materials,

chip shape is also determined by the friction stress distribution between chip

and tool. The friction stress distribution is determined both by the mechanical

strength, under the deformation conditions of the cutting experiment, of the

chip material adjacent to the rake face of the tool and by the distribution over

the rake face of the ratio of the real to apparent area of contact between chip

and tool.

Gaitonde et al (2008) determined optimum amount of MQL

(minimum quantity of lubrication) and the most appropriate cutting speed and

feed rate during turning of brass using K10 carbide tool. Taguchi technique

with the utility concept, a multi-response optimisation method, has been

proposed for simultaneous minimization of surface roughness and specific

cutting force and the experiments were planned as per L9 orthogonal array.

The optimum amount of MQL and the most appropriate cutting speed and

feed rate were determined using ANOM and the relative significance of the

parameters was identified through ANOVA.

57

Seah et al (1986) conducted a series of experiments on steel,

aluminium and brass in a lathe without using any lubricant or coolant and

under various conditions of relative humidity. The results show that if other

parameters are kept constant, all three (axial, radial and tangential)

components of tool force in dry turning decrease quite significantly with

increasing relative humidity. There is presently insufficient evidence to

indicate which of the two above-mentioned mechanisms best explains the

effect of atmospheric humidity on tool force, the mechanisms being (a) the

exothermal oxidation of pristine metal on the back face of the chip exposed

during chip formation and (b) the lubrication of the tool-chip interface by

water vapour. Normally with adsorbants it is likely that surface energy drops

down as well as friction. This can result in observed reduction in force with

humidity.

Gerry Byrene (1986) investigated the herbert/Gottwein dynamic

thermocouple method of temperature measurement in order to experimentally

evaluate the average interfacial temperatures arising in the external cylindrical

turning of aluminium, brass, mild steel and stainless steel using high speed

steel cutting tools. The thermoelectric characteristics of each material in

conjunction with HSS were determined by means of the furnace method of

calibration. A critical appraisal of each phase associated with the dynamic

thermocouple method of cutting temperature measurement was undertaken in

this paper and interfacial temperatures under a wide range of machine setting

parameters for each workpiece material were presented.

Nakayama and Tamura (1968) performed an experimental

investigation on the cutting of brass with high speed steel tools with edge

radii measuring 3 – 4 µm at different rake angles (0º, -20º, -40º). The cutting

and thrust forces were reported to have a positive intercept at a zero depth of

cut, indicating the existence of size-effect, except when cutting with a 0º rake

58

angle tool. It was suggested that the disproportional consumption of energy in

plastic flow in the subsurface layer, together with the change in undeformed

chip thickness and the blunting of the cutting edge, might be possible sources

contributing to size-effect.

2.11 TOOL CONDITION MONITORING

Numerous types of sensors are available for monitoring the

machining environment. Among those sensors, Acoustic Emission (AE)

sensor is considered to be one of the practical and potential candidate (Inasaki

et al 1987). The major advantage of using AE to monitor tool condition is that

the frequency range of the AE signal is much higher than that of the machine

vibrations and environmental noises, and does not interfere with the cutting

operation. Also acoustic emission is an active signal associated with every

event of occurrence in the material.

Using an AE signal to monitor tool wear condition in cutting

processes was started by Iwata and Moriwaki (1977). They have arrived at

two important conclusions: the power spectrum of AE signals up to 350 kHz

increased with the tool wear and then reached saturation; and the total AE

count was closely related to the tool wear. Since these initial reports,

numerous studies have established the effectiveness of AE-based sensing

methodologies for tool condition and cutting process monitoring.

The first comprehensive study of AE phenomenon was published in

1950 by Kaiser based upon the tests during the tensile stressing of

conventional engineering materials. He observed the irreversible phenomenon

of AE, known as ‘Kaiser Effect’. He also proposed a distinction between burst

and continuous emissions. The frequencies that are commonly used for AE

testing lie between 50 kHz and 1 MHz.

59

Application of AE in tool health monitoring was proposed in 1977

by Iwata and Moriwaki, where they have reported a close relationship

between tool wear and total count of Acoustic Emission. Sampath and

Vajpayee (1986) have used AE parameters viz. ring down counts and events

obtained during machining to correlate the tool wear.

Yon Lee et al (2004) have performed micro-scratching tests on

coarse-grained, oxygen-free high-conductivity (OFHC) copper using both

increasing and constant depths of cut. Acoustic emission (AE) was used to

explore the grain orientation and grain boundary effect in the precision cutting

process and differences in material properties such as change in grain

orientation and grain boundary crossing are monitored during diamond

cutting of OFHC copper using AE sensing. The result reveals that AE shows

good sensitivity to the micro-cutting mechanism such as the detection of the

initial engagement between the tool and workpiece, grain orientation and

grain boundary crossing (change of force/stress and consequent emission

rate).

2.11.1 AE in Cutting Process - Applications

Research has shown that AE, which refers to stress waves

generated by the sudden release of energy in deforming materials, has been

successfully used in laboratory tests to detect tool wear and fracture in single

point turning operations. Byrne et al (1995) have pointed out the following

possible sources of AE during metal cutting processes shown in Figure 2.1.

(a) plastic deformation during the cutting process in the workpiece;

(b) plastic deformation in the chip;

(c) frictional contact between the tool flank face and the

workpiece resulting in flank wear;

60

(d) frictional contact between the tool rake face and the chip

resulting in crater wear;

(e) collisions between chip and tool;

(f) chip breakage;

(g) tool fracture.

Figure 2.1 AE generation during metal cutting

In precision machining, AE can be attributed to several sources at

the tool/chip interface is shown in Figure 2.2. It is seen that with increased

precision, lower order material removal, the AE frequency increases i.e. the

material removal mode changes from plastic deformation shearing to

rupture/upsetting associated shearing i.e. AE signal changes from relatively

continuous mode to burst mode. At the tool/chip interface in conventional

machining, AE is typically generated in the primary shear zone (shear region

ahead of the tool), secondary shear zone (region of contact between tool and

chip), and the tertiary shear zone (contact between the machined surface and

flank). While at the conventional machining scale where the DOC is typically

on the order of tens of microns and higher, AE is largely due to rubbing and

friction at the tool/chip interface, including both the secondary and tertiary

shear zones. At the precision scale and below, however, it is believed that the

61

majority of AE signal generation is generated through the interaction of the

tool tip with microstructural features of the workpiece, such as voids,

inclusions, grain boundaries, and bulk dislocation interactions in the shear

zone (Lee et al 2006).

Figure 2.2 Sources of AE at various stages of material removal

Several studies have focused on the effect of various machining

parameters on the resultant AE signal. Liu (1996) established a good match

between modelled and experimental AE root-mean-square (RMS) signal for

diamond turning, and found a strong correlation between chip thickness,

62

cutting speed, and AE RMS. Likewise, further work by Chen et al (1996)

established an exponential reduction in specific AE RMS (analogous to

specific cutting energy, energy density, or energy/chip volume) vs chip

thickness. In normal machining, with chip thickness the chip strain decreases

as well as the AE energy. However this is true only when uncut chip thickness

(feed rate) is far higher than edge radius. AE sensitivity, down to chip

thicknesses of 0.01 mm was found, with a sharp difference in specific AE

RMS between worn and sharp tools. The increase in specific cutting energy

with decreasing chip thickness and tool wear, was believed to be due to the

ratio of tool edge radius to uncut chip thickness; as the uncut chip thickness

decreases below the edge radius of the tool, a transition in the cutting

mechanism takes place where an effective negative rake angle is established,

which shifts the cutting mode to a more energy-consuming one, dominated by

rubbing and burnishing. This effective negative rake angle also helps explain

the increase in specific cutting energy for worn tools, as worn tools have a

larger edge radius and require more energy than sharp tools to initiate chip

formation. This is true for precision machining also.

Based on the analysis of AE signal sources, AE derived from metal

turning consists of continuous and transient signals, which have distinctly

different characteristics. Continuous signals are associated with shearing in

the primary zone and wear on the tool face and flank, while burst or transient

signals result from either tool fracture or chip breakage. Therefore, from (a) to

(d) sources generate continuous AE signals, while from (e) to (g) generate

transient AE signals. The AE signal types in cutting process is shown in

Figure 2.3.

Uehara (1984) has found that the amplitude of an AE signal from

the workpiece is decisively due to workpiece to tool interaction. As a result,

the friction between workpiece-tool and the tool fracture can be regarded as

the most important sources of continuous and transient AE signals during

63

turning, respectively. Therefore, the amplitude of the continuous-type AE

signal could be used to monitor the wear of a cutting tool.

Figure 2.3 AE signal type in cutting process

Naerheim et al (1984) have used continuous and discontinuous AE

signals in turning to test gradual wear and intermittent degradation of cutting

tools, respectively. The effects of machining parameters on characteristic AE

obtained during machining were studied based on the frequency analysis of

AE signal.

The characteristic features of acoustic emission signals were

analyzed during turning of medium carbon steel with a coated tool and

uncoated tools. It was found that the AE signal changed from the burst type to

a continuous type as the wear of the coated tool progressed and the ceramic

coating was removed (Moriwaki et al 1988).

2.11.2 AE Signal Processing

An AE signal is non-stationary and often comprises overlapping

transients, whose waveforms and arrival times are unknown. A common

problem in AE signal processing is to extract physical parameters of interest,

such as tool wear, when these involve variations in both time and frequency.

64

Many quantifiable characteristics of AE can be displayed as follows

(Krishnamurthy et al 1997):

Ring down count: the number of times the signal amplitude exceeds

the set reference threshold;

AE event: a micro-structural displacement that produces elastic

waves in a material under load or stress;

Rise time: the time taken to reach peak amplitude from the first

present threshold voltage crossing of the signal;

Peak amplitude: this can be related to the intensity of the source in

the material producing an AE signal;

RMS voltage: a measure of signal intensity.

Many signal processing methods have been used to analysis AE

signals, with the aim to extract the features of AE signals for testing or

monitoring. The main methods include time series analysis, fast Fourier

transform (FFT), Gabor transform (or window (local) Fourier transforms),

Wigner–Ville distribution, and wavelet transform.

2.11.3 Tool Wear Estimation through AE Signals

The relationship between the AE signal and tool wear is not simple.

Kim et al (1986) have observed purely progressive tool wear in turning

operations. They have also found that in most experimental results the refined

mean level (RML) of the averaged AE signal increases at first with an

increase of flank wear, and then stays at an approximately constant level even

with further increase of flank wear while the fluctuation of the RML across

the constant level becomes rather high. Clearly, the relationship between the

65

AE signal and tool wear condition is nonlinear, so the general mathematical

relation cannot be used to map this relation. If we can look for an effective

mathematical model to map the relationship between the AE signal (some

features) and tool wear, the AE signal could be used to monitor tool wear

condition in real time for turning.

Kim at el (2005) developed a 3-axis controlled micro grooving

system for the machining of PDP barrier rib moulds. The workpiece of

STD11 that is an alloy tool material was used. The shape of the microgroove

according to various conditions was observed using diamond and CBN blade

tools. To obtain the optimised grooving conditions, machining parameters of

spindle revolution speed (25 000–40 000 rpm), feed rate (0.4–0.8mm/s), depth

of groove (100–300 µm), etc., were taken into consideration. An AE sensor

was attached using grease on the side of the work piece to obtain the AE

signal generated during the grooving process. The type of AE sensor is a wide

band that has uniform sensitivity at the wide frequency range. The signal was

amplified (52 db) and filtered (100–1200 kHz) using the pre-amp

[1220A(PAC)] and main-amp [AE1A(PAC)]. The digital signal obtained

from the AE sensor at a sampling speed of 5MHz was digitized and stored

using the A/D converter (Gagescope CS1012), which has a 12 bit-resolution.

And, the characteristic of the AE signal, in accordance with groove

machining, was analyzed. As a result of the experiment for evaluation of

machining characteristics for developed micro grooving machine in this

study, it was possible to find a depth of groove not exceeding 200 µm and

feed rate of 0.6mm/s were the optimum process conditions of a developed

grooving machine within 30000–35000 rpm of spindle revolution speed when

using the CBN blade. It was found through comparing the production of burr

with microscopic analysis. From the analysis of signal using the RMS of AE

signal, it was possible to find that AE well reflects the general mechanism of

the cutting process when grooving, and that the signal of frequency domain

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shows consistent frequency sensitivity regardless of the processing condition

under a stable process situation. However, when a burr is generated on a

material, it shows irregular frequency characteristics. Consequently, it was

possible to confirm the possibility of monitoring process situation using RMS

of AE signal by the method of recognizing patterns and frequency

characteristics that use fuzzy theory.

Cho et al (1997) have proposed correlation between intrinsic

frequencies and AE sources as identified by examining the RMS, dominant

amplitude, type, and count rate of the AE signals. The tool life estimated from

the RMS of the AE signal is shown to be in good agreement with that

determined from measurements of the maximum wear and width on the tool

nose. The results obtained demonstrate that AE is an effective technique for

in-process wear monitoring and wear mechanism identification of multilayer

ceramic-coated tools.

From the literature survey carried out, it can be summarized as:

The literature survey on micromachining is concerned with

size effect, relative high specific energy, chip formation,

morphology, surface texture formation and plastic-elastic

recovery of work machined.

Micromachining can serve as a novel means of fabricating

unique features in brittle materials not achievable by other

techniques

The survey on the available limited literatures on

micromachining indicates the application of FEA in

micromachining, and size effects in micromachining.

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Most of the study has been carried out on machining of single

crystal material with a view to assess the role of

crystallographic orientation on machining performance.

Among the tool materials, mostly poly crystalline diamond

and k-type cemented carbides have good applications

especially for machining of heterogeneous materials.

The studies on monitoring of machining have illustrated the

applicability of acoustic emission in sensing Microturning

process.

Mathematical Modeling and optimisation of machining

parameters for microturning process is of recent origin.

Studies on machining of copper:

is restricted to application of single crystal diamond tool.

mostly concerned with temperature in machining and its

consequences on crater wear.

indicates the mixed influence of increased specific

energy due to size effect as opposed to thermal softening.

indicates performance of coatings on machining of

copper.

Surface texture in precision turning is related to feed/edge

radius, and crystallographic orientation. There is no reference

to the role of nose radius of tool in surface texture production.

Ductile/brittle transition in precision machining has been

studied, critical depth and hydrostatic stress ahead of the

cutting wedge are factors considered.

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These challenges posed to machining of copper and its, alloys

provide good scope for further machining study.

2.12 SCOPE FOR THIS STUDY

From the literature survey, there is a good scope for a detailed

study on micromachining of copper and its alloys for a better understanding

of the machinability in terms of surface texture production and response of

cutting tool. The scope of this study is

Microturning process is a potential process for production of

micro components such as micro pin, microelectrodes and

microshafts over other processes.

This study on micromachining of OFHC copper and brass is to

be carried out in the Integrated Multi-process Micromachine tool

with due consideration for manufacture of quality

microelectrodes for subsequent electro discharge machining.

Tool flank wear and surface roughness are important features

in microturning since the former influences dimensional

accuracy while the latter influences the performance of

machined surface. Hence detailed monitoring and analysis of

data is required.

It is necessary to develop efficient models which facilitate the

operation of machine tools at optimal conditions to improve

the efficiency considering multi performance criteria such as

tool wear and surface roughness (response of both tool and

workpiece to microturning).

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2.13 OBJECTIVE

Accordingly the objectives of the present study are to:

Evaluate the machinability of copper and brass in

Microturning.

Asses the responses to micromachining environment in terms

of tool wear and surface roughness.

Monitor the tool wear through indirect indicator such as AE

and to analyze the critical region during tool wear.

Evaluate the mechanism of tool wear and surface generated

through Scanning electron micrographs.

Analyze the data for process modeling with combined

objective optimisation using Genetic Algorithm.

The methodology adopted for the present study is illustrated in

Figure 2.4.

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Combined-objective OptimisationUsing Genetic Algorithm

Comparison of Experimental values with Predicted values

Mathematical Modeling Non linear regression model

Selection of the Process (Microturning )

Selection of Material (OFHC Copper and Brass)

Selection of the Process Parameters Cutting speed, Feed and Depth of

cut

Literature Survey on the existing Process Parameters

Experimental Investigations (Full factorial experiments)

Significance of Tool Traverse on Machining Performance

Observations Flank wear, Surface Roughness,

and Acoustic Emission signals (AE rms)

SEM analysis of chips produced, worn out tool and surface generated

Conclusion

Figure 2.4 Research methodology