19

CHAPTER 2

LITERATURE REVIEW

2.1 OPERATIONAL DATA FOR CYLINDRICAL GRINDING

In cylindrical grinding, similar to other metal cutting processes, the

applied speed and feed rates must be adjusted to the operations as well as to

the objectives of the process. Grinding differs, however, from other types of

metal cutting methods in regard to the cutting speed of the wheel in grinding.

Generally cutting speed is not variable and should be maintained at, or close

to the optimum rate.

In establishing the proper process values for grinding, the prime

consideration is the work materials, its condition (hardened or soft) and the

type of operation (roughing or finishing). The other influencing factors are the

characteristics of the grinding machine (stability, power), the specifications of

the grinding wheel, the material allowance, the rigidity and balancing of the

workpiece, as well as several grinding process conditions, such as wet or dry

grinding, the manner of wheel truing etc.

Variables of the cylindrical grinding process, often referred to as

grinding data, comprise the speed of work rotation (measured as the surface

speed of the work), the infeed (in inches per pass for traverse grinding, or

inches per minute for plunge type grinding). This data is for the purpose of

stating the values in setting up a cylindrical grinding process. A brief listing

20

of basic data for common cylindrical grinding conditions and frequently used

materials is presented in Table 2.1 (Erik Oberg et al 2008).

Table 2.1 Basic process data for cylindrical grinding

Workmaterial

Material condition

Worksurfacespeed (fpm)

Infeed (inch / pass)

Traverse for each work revolution, in fraction of

the wheel width

Roughing Finishing Roughing Finishing

Plain carbon steel

Annealed 100 0.003 0.0005 ½ 1/6

Hardened 70 0.0030.0003

to0.0005

¼ 1/8

Alloy steel

Annealed 100 0.003 0.0005 ½ 1/6

Hardened 70 0.0030.0002

To0.0005

¼ 1/8

Tool steel

Annealed 60 0.003 0.0005 ½ 1/6

Hardened 50 0.0030.0001

To0.0005

¼ 1/8

2.2 OPERATING CONDITIONS

The success of any grinding operation depends on the proper

selection of various operating conditions like wheel speed, traverse feed,

infeed, grinding fluid, etc.

2.2.1 Wheel Speed

If the wheel speed is increased at a constant longitudinal or rotary

feed rate, the size of the chips removed by a single abrasive grain is reduced.

21

This reduces the wear of the wheel. If the wheel speed is reduced, the wear is

increased. From this it is clear that from the point of view of wear, it is better

to operate at higher wheel speeds (Optiz and Guhring 1968). However, this is

limited by the allowable speeds at which the wheel can be worked, as well as

the power and rigidity of the grinding machine. Normally, the grinding wheel

speed ranges from 20 to 40 m/sec. The wheel speed also depends upon the

type of grinding operation and the bonding medium of the grinding wheel.

2.2.2 Work Speed

Work speed is the speed at which the workpiece traverses across

the wheel face or rotates between centres. If the work speed is high, the wear

is increased but the heat produced is reduced. Hahn (1956) has stated that

high work speeds are effective in reducing cracking of heat sensitive

materials and may also influence the life of the tool or part. On the other

hand, if the work speed is low the wheel wear decreases but the heat produced

is more. The ratio of wheel speed to work speed is of much importance and it

should be maintained at the proper value. Low work speeds result in local

overheating and bring about deformation or tempering of the hardened

workpiece. This in turn affects the mechanical properties of the workpiece

and very often micro cracks will appear on the workpiece. The increase in

work speed is limited by premature wheel wear and vibrations induced by

wear. Generally, if the wheel wears increases the work speed should be

reduced. If the heat produced is high and clogging occurs, especially with

hard wheels, the work speed should be increased.

2.2.3 Infeed

If the infeed is high, the wheel wear increases and the surface finish

deteriorate, thus affecting the dimensional and geometrical accuracy of the

22

ground workpiece. The material removal rate, however, increases if the infeed

is high.

2.2.4 Traverse Feed

The traversed feed or cross feed rate is governed by the width of

the wheel and the work speed. Normally, the traverse feed rate is adjusted to

two thirds to three fourth of the wheel while grinding steels and three fourth

to five sixths of the wheel width while grinding cast iron. Heavier cross feed

increases the wheel wear and produce rougher finish and slower cross feeds

reduce the wheel wear and produce finer finish. (HMT 1980).

2.2.5 Grinding Fluids

The goal in all conventional metal removal operations is to raise

productivity and reduce costs by machining at the highest practical speed,

consistent accuracy and finish. Many machining operations can be performed

in dry conditions. The proper application of a cutting fluid generally makes

possible, higher cutting speed, greater depth of cut, lengthened tool life,

improved surface finish, increased dimensional accuracy and reduced power

consumption.

Selection of proper cutting fluid for a specific machining situation

requires knowledge of fluid functions, properties and limitations. Cutting

fluid selection deserves as much attention as the choice of machine tool,

tooling speeds and feeds.

In recent years a wide range of grinding fluids has been developed

to satisfy the requirements of new materials of construction and new tool

materials and coatings. The most commonly used grinding fluids are water

based emulsions and grinding oils. Nearly all grinding operations can be

23

carried out with emulsifiable oils. It is important that the fluid is directed to

the interfaces so that it can enter and create a film of low shear strength

between the wheel and work. The quantity of fluid should be ample and may

amount of 15 to 20 litres/min for a medium sized grinding machine.

Generally, the feed quantity of the coolant depends on the length of contact

between the grinding wheel and the workpiece. Larger the contact area, more

should be the quantity of coolant. The width of the stream should be more

than the width of the grinding wheel.

Because of the very high speeds involved in grinding, a film of air

encloses the wheel surface and prevents the penetration of the fluid to the

cutting zone. This air stream can be pierced by supplying the fluid under

pressure or by the design of special nozzles. The nozzle should be as near the

workpiece as possible. Another method of supplying the grinding fluid is

through the voids in the grinding wheel. The fluid is supplied at the center of

the wheel and it moves out through the wheel under the action of centrifugal

force. The main disadvantage is that the fluid is continuously expelled all

along the perimeter of the wheel, instead of only at the cutting zone. Further,

since the pores in the wheel are extremely small, the fluid should be finely

filtered to prevent clogging in the wheel (Guo and Malkin 1992).

2.3 WORK HOLDING ON CYLINDRICAL GRINDING

MACHINES

The manner in which the work is located and held in the machine

during the grinding process determines the configurations of the part which

can be adapted for cylindrical grinding and affects the resulting accuracy of

the ground surfaces. The method of work holding also affects the attainable

production rate, because the mounting and dismounting of the part can

represent a substantial portion of the total operation time.

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Whatever method is used for holding the part on cylindrical type of

grinding machines, two basic conditions must be satisfied:

(i) the parts should be located with respect to its correct axis of

rotation, and

(ii) the work drive must cause the part to rotate at a specific

speed around the established axis.

The lengthwise location of the part, although controlled, is not too critical in

traverse grinding, however, in plunge grinding, particularly when shoulder

sections are involved, it must be assured with great accuracy.

2.4 IMPORTANCE OF CUTTING FORCE

The cutting force generated in metal cutting has a direct influence

on generation heat, the tool wear or failure, quality of machined surface and

accuracy of the workpiece. It is one of the important characteristic variables

to be monitored in the cutting process. Grinding force is one of the most

important parameters in evaluating the whole process of grinding. Especially,

grinding force is a crucial issue in rough grinding. A large depth of cut and

fast feed rate will cause a high grinding force. They can lead to some

undesired problems, such as errors in size and shape, chatter and burn during

grinding. In general, if only the wheel speed is increased, the average cross-

section of the ground chips decreases, so the grinding force and surface

roughness decrease. The decrease in grinding force results in an improvement

of grinding accuracy. Monitoring the grinding forces is advantageous in

optimizing the process conditions, monitoring the sharpness of the wheel,

grinding fixture design, deformation calculation, improving the process

control, and producing high quality parts.

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Figure 2.1 Illustration of the force components in cylindrical grinding (Malkin and Guo 2008)

Grinding forces are developed between the wheel and the

workpiece owing to the grinding action. Generally the grinding force is

resolved into three component forces, namely, normal grinding force,

tangential grinding force and a component force acting along the direction of

longitudinal feed which is usually neglected because of its insignificance. The

normal grinding force has an influence upon the surface deformation and

roughness of the workpiece, while the tangential grinding force mainly affects

the power consumption and service life of the grinding wheel. The force

components in cylindrical grinding are illustrated in Figure 2.1.

2.5 FORCE MEASUREMENT IN MACHINING PROCESSES

Measurement of cutting forces is based on three basic principles

i) Measurement of elastic deflection of a body subjected to the

cutting force.

ii) Measurement of elastic deformation, i.e. strain induced by

the force

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iii) Measurement of pressure developed in a medium by the

force.

The basic principle and general method of measurement is

schematically shown in Figure 2.2.

The cutting force developed in machining operations is estimated

indirectly from a variety of metal cutting dynamometers, mechanical,

hydraulic, pneumatic or several types of electro-mechanical dynamometers.

In the majority of current force measuring techniques, the cutting force is

applied to some elastic member of the dynamometer and the resulting

deflection of the member is measured by using strain measurement

equipment.

Figure 2.2 General principle of measurement

2.5.1 Turning, Milling and Drilling Operations

Generally the dynamometer is strain gauge or piezoelectric type for

measuring three component cutting forces. Oraby and Hayhurst (1990)

designed a compact three component tool shank dynamometer in turning. In

which, the load bearing section has its stiffness reduced by two holes

Reading and recording

Physical variable

Conversion into another suitable variable

Amplification, filtering and

stabilization

Signal

Conditioned signal

27

symmetrically positioned about the center-line, and connected by a narrow

slit. The holes are positioned to enable strain gauges to record the highest

local strains. The electrical strain gauges are mounted on the circular holes to

provide an electrical output proportional to the cutting force. The output

signals due to force have been processed using a computer through strain

gauge amplifiers and analog to digital converter.

Yaldiz and Unsacar (2006) developed a dynamometer such that the

strain gauges are cemented in the octagonal rings. Three full bridges

comprising sixteen strain gauges are used for measuring the component forces

along the primary axes. The strain gauges are connected with the strain

measuring bridges for detection and measurement of strain in terms of

voltage. This voltage provides the magnitude of the cutting force through

calibration.

Korkut (2003) constructed a strain gauge based dynamometer

capable of measuring three-force components during milling operation. In

which the strain gauges are mounted on the octagonal rings. The octagonal

rings are fixed between two plates. The forces on each octagonal ring are

summed up correspondingly to get the total magnitude of all the three forces

in X, Y and Z direction respectively. Although these kind of table

dynamometers provides accurate and effective force measurement, they are

more suitable for laboratory use rather than practical application on

production machines, due to the limitations of workpiece size, mounting

constraints, high sensitive to overload, and high cost. To overcome the

limitations of workpiece mass and size, Albrecht et al (2005) presented a

method of measuring cutting forces from the displacements of rotating

spindle shafts. The cutting forces are measured via a capacitance sensor

installed in the spindle housing using the radial displacements of the rotating

spindle shaft. It is also concluded that, this measurement system is insensitive

28

to overload and not subjected to wear because the sensors are not in contact

with the rotating spindle.

Karabay (2007) developed a drilling dynamometer to measure the

torque and thrust force with three octagonal rings. One of these was used as a

support and the other two were cemented with eight strain gauges. The strain

gauges are connected in this way to measure the thrust force and torque

without any interference. Finally it is concluded that, the force measured

using the dynamometer was good agreement with the force obtained from the

empirical relation.

Totis et al (2010) developed a dynamometer for measuring

individual cutting edge forces in face milling. The device is capable of

providing independent tri-axial cutting force information from each cutting

edge. In addition, cutting inserts are interchangeable and adjustable, and the

device is compatible to different spindle adaptors.

2.5.2 Grinding

There are certain problems which are peculiar to cylindrical

grinding operation. Due to rotation of both the wheel and the workpiece there

are difficulties in physically locating the dynamometer. But, in surface

grinding the dynamometer can be mounted on the table without any

difficulties. In cylindrical grinding, if the dynamometer forms a part of the

rotating workpiece, a transmitter is essential to pick up the signals prior to

amplification and processing. Nagpal and Bhattacharyya (1976) proposed a

dynamometer for cylindrical grinding. It consists of holding block, load

washer and small workpiece. The dynamometer is located in a slot machined

in the main workpiece. When grinding is in progress, the forces experienced

by a small workpiece are picked up by the load washer. As the small

workpiece comes into contact with the wheel for only a part of the workpiece

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rotation, the force signal obtained is discontinuous. Since the dynamometer is

considered as an insert it can be used only in the laboratory. The detailed

construction of the dynamometer is shown in Figure 2.3.

Figure 2.3 Details of the dynamometer and its complete setup (Nagpal

and Bhattacharyya 1976)

The sharpness of the grinding wheel in cylindrical grinding

predicted from the measured grinding force was described by Brenner and

Torrance (1993). The normal and tangential grinding forces were measured

by a 3-axis piezoelectric Kistler dynamometer fitted beneath the tailstock and

is shown in Figure 2.4.

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Figure 2.4 Schematic view of force measurement experimental setup (Brenner and Torrance 1993)

Drew et al (2001) investigated the variation of grinding force

with oscillating work speed. A special setup was used to support the

workpiece and mount the dynamometer in the table of cylindrical grinding.

Varghese et al (2000) developed a sensor integrated intelligent grinding wheel

for in-process monitoring. The intelligent grinding wheel consists of an

aluminium core embedded with piezoceramic sensors, fitted with diamond

abrasive segments on its periphery. The instantaneous normal grinding force

was measured using the sensors placed around the wheel core periphery.

Li et al (2002) evaluated the unsteady state grinding through the

measurement of the tangential grinding forces and normal grinding forces. A

sensor based support device made up of two hollow centers with four

electrical resistance foils pasted onto the quadrangular surface was used to

make the measuring bridge and receive signals of the grinding force. The

general view of sensor based force measurement system is shown in

Figure 2.5. The evaluation concluded that in the unsteady state grinding

process, the values of the grinding forces are much lower than those of the

steady-state grinding process and the grinding force ratio shows a non linear

fluctuation.

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Figure 2.5 Sensor based force measurement system (Li et al 2002)

Couey et al (2005) monitored the forces generated during precision

cylindrical grinding using aerostatic spindles, where high stiffness and very

low error motions were required. The changes in the gap between the rotor

and stator with respect to the grinding force were sensed by incorporating

non-displacement sensors into an aerostatic spindle. The force resolution of

the instrumented spindle was 25 mN, which correspond to detecting a 2-nm

spindle deflection. Using the same logic, Marsh et al (2008) measured a form

error and force, Moerlein et al (2009) implemented a force measurement

based approach for controlling workpiece diameter in cylindrical grinding.

2.6 GRINDING FORCE MODEL

Modelling can quantitatively describe the relationships between

grinding parameters and helps in understanding the influence of these

parameters on grinding performances. The grinding force model describes the

correlation between input and output quantities in order to predict the

grinding force. The relative work is really extensive; still it can be subdivided

into empirical models and analytical models. The empirical models are

established through the measured data from grinding tests, in which, the

32

correlation is established between the selected input and output parameters

with a chosen model format, and then verified in further grinding tests. But

the analytical models are established based on the conformity of the process

of physical laws, using a mathematical formulation of the qualitative model.

Therefore, the analytical models rely on the understanding of the physics

behind the process and the subsequent physical models selected to describe

the mechanism. The accuracy of the models typically depends on the

assumptions and how closely the model describes the physics.

2.6.1 Analytical Model

In recent years, considerable works have been carried out to

develop a mathematical model for the grinding force, by considering the

grinding process as an interaction between the surfaces of the grinding wheel

and the workpiece, rather than a shearing action in the conventional cutting

process.

Lichun and Jizal (1980) developed a grinding force mathematical

model, which consist of two terms corresponding to chip formation forces and

frictional forces respectively. The relationship between grinding forces and

grinding kinematic process parameters with workpiece of different materials

was determined experimentally. They also analyzed the ratio between

tangential forces and normal forces with respect to both chip formation forces

and frictional forces and found that the ratio was within the range of 0.2 to

0.59.

Younis et al (1987) developed a grinding force model based on the

fact that the chip formation during grinding consists of three states:

ploughing, cutting and rubbing. Expressions for the total normal and

tangential force components during these three stages were derived. These

components were expressed in terms of the chip thickness coefficient, the

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frictional coefficient between the grit tip area and the workpiece, the stress

coefficient arising during ploughing and, finally, the loading coefficient. The

development of mathematical models in this work particularly focused on the

variation of the geometrical form and the structure of the grinding wheel

surface resulting from loading and/or grinding wheel wear in the time

domain. It was concluded that these variations affect the rubbing component

of the main grinding force and consequently the stability of the grinding

process.

Badger and Torrance (2000) proposed two kinds of grinding force

model in surface grinding, the first model based on two-dimensional plane

strain slip-line field theory and the second based on three- dimensional

pyramid-shaped asperity model.

Hecker et al (2003) presented a 3D methodology to evaluate the

static parameters of the wheel topography and to calculate the dynamic

parameters based on a probabilistic chip thickness approach. They calculated

the dynamic grain density from the static grain density and dynamic effects

such as the kinematic shadow generated by active grains and the grain local

deflection. The normal and the tangential force per grain were calculated from

the indentation model using the hardness of the material. The dynamic grain

density and the forces per grain were used to predict the total grinding forces.

Micro-grinding with small scale grinding wheels is a micro-

machining process in precision manufacturing of miniature part features such

as those in micro sensors and micro actuators. As the diameter of grinding

wheels decreases, the negligible effects in conventional such as ploughing

forces and grinding wheel deformation become more significant in micro-

grinding. Park and Liang (2008) developed a force model for the micro

grinding process by combined consideration of mechanical and thermal

effects within a single grit interaction model at the micro-scale level of

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material removal while the size effect of micromachining was incorporated.

In the model the forces are expressed as functions of the process

configuration, workpiece material properties and micro-grinding wheel

topography.

Hecker et al (2007) presented the predictive modelling of grinding

force and power based on the probabilistic distribution of undeformed chip

thickness as a function of the kinematic conditions, material properties, wheel

microstructure, and dynamic effects.

In most of the grinding force model deals with the average grinding

force based on a uniform grit distribution or an estimated average grit density

on the wheel without considering the random nature of the grinding force.

The random distribution of the grits on the wheel surface makes the grinding

process stochastic in nature. The random grit distribution of the rotating

wheel was described by Chang and Wang (2008) and introduced the

stochastic grit density function in the grinding force model.

Another different grinding force model by considering the chip

formation force and sliding force was proposed by Tang et al (2009). In this

model, ploughing force was neglected and the chip formation energy was

divided into static chip formation energy and dynamic chip formation energy,

mainly influenced by shear strain, shear strain rate and heat in the metal

removal process.

Most of the grinding force models neglected the effect of

ploughing, considering it to be very low in comparison with the chip

formation force. Also the coefficient of friction was taken as constant but in

reality; it varies with process parameters during the grinding process.

Durgumahanti et al (2010) developed a force model by considering the effect

35

of the input process parameters and grain size on the ploughing force

component and on the coefficient of friction.

2.6.2 Empirical Model

Normally, empirical models are used in all fields of grinding

technology due to the fact that the physical interrelationships in grinding

cannot be accurately defined. Empirical models possess simple and easy-to-

get characteristics but depend heavily on particular circumstances. The

empirical models are established by means of measured values which have

been obtained in grinding tests. The popular approach for empirical models is

to integrate expert knowledge into the process design or model system. The

expert knowledge is represented by a domain of heuristic rules which are

captured in a knowledge base. With the rapid advent of computers, expert

systems have been a substantial growth in many grinding applications. To

understand the real complexity of the grinding force, experimental

observations under different grinding conditions are necessary. In most of the

force models, grinding force is principally determined by the diameter of

grinding wheel ds, depth of cut d, work speed Vw, grinding wheel speed Vs and

contact length lc as input variables.

Liu et al (2008) developed a model of the grinding force for

aerospace alloys using an empirical approach in surface grinding. This work

pointed out that the experiment design, data processing and regression aspects

are to be concentrated on the development of empirical model.

2.7 MONITORING OF GRINDING PROCESS

A successful grinding process monitoring depends to a great extent

on reliable and robust sensors used for this purpose. In the absence of human

operators, the sensors must have the ability to recognize process

36

abnormalities and initiate corrective action. There are various signals which

correlate to the condition of the process and they are the subject of different

sensing and processing techniques. Each of these signals is able to provide a

feature related to the phenomenon of interest although at varying reliability.

So to collect the maximum amount of information about the state of a process

from a number of different sensors is the best solution.

Inasaki and Yokohama (1985) investigated the possibility of

monitoring the grinding process by using the acoustic emission (AE) signals.

The contact between the grinding wheel and the workpiece was observed to

increase the productivity by reducing non-machining time. It was monitored

by the signal of an acoustic emission (AE) sensor fixed to the tailstock center

of the workpiece and confirmed by the measured grinding wheel motor power

using a power meter.

Alfares and Elsharkawy (2000) studied the effect of dynamic

changes in the grinding force components due to changes in the grinding

wheel wear flat area and the workpiece material on the vibration behaviour of

the grinding spindle in surface grinding. Lezanski (2001) presented an

efficient way of application of neural network and fuzzy logic based systems

combining the outputs of several sensors for grinding wheel condition

monitoring during external cylindrical grinding process.

Lee and Kim (2001) investigate the external plunge grinding

process using the current signals of a spindle motor through a hall sensor. The

current signal of the spindle motor was analysed and induced a relationship

between current signals and the metal removal rate in terms of the infeed rate.

It was inferred that (i) the current value of the spindle motor decreased with

an increase of the grinding wheel speed and it increases in accordance with

the increase in not only the infeed rate but also the work speed, (ii) the current

37

signals of the spindle motor reflect the qualitative characteristics of grinding

force and the metal removal rate, and (iii) both the current signal of the

spindle motor and AE energy against material removal rate are similar.

Shih et al (2001) presented the truing and grinding forces and the

wear mechanism of particle and rod diamond blade tools were used to

generate precise and intricate forms on rotating vitreous bond silicon carbide

grinding wheels. A Hall effect sensor was used to measure the change of

grinding spindle power during truing and grinding. The truing and grinding

forces are obtained from the change of grinding wheel spindle power using

the following formula.

Tangential truing force, = (2.1)

Specific tangential grinding force, = (2.2)

where, Wt Change of spindle power in truing

Wg Change of spindle power in grinding

Vs the surface speed of grinding wheel

b the width of the grinding wheel

Kruszynski and Lajmert (2005) presented a supervision system that

uses techniques of artificial intelligence to monitor, control and optimise the

traverse grinding operation. The supervision system consists of two levels

which act in parallel to produce parts satisfying the geometrical and surface

finish requirements with maximum possible productivity. The first

optimisation level was developed to maximize the material removal rate,

simultaneously satisfying restrictions on surface roughness, out of roundness

and waviness errors. The second optimisation level considered for

38

geometrical control and is responsible for the removal of the initial shape

error by stabilising the motion trajectory of the grinding wheel in relation to

the part being produced.

Kwak et al (2006) analyzed the grinding power spent during the

process and the surface roughness of the ground workpiece in the external

cylindrical grinding of hardened SCM440 steel using the response surface

method. The grinding power was obtained by multiplying the supplied

voltage of the motor and the current measured flowing into the spindle motor

cable using a Hall effect sensor. It concluded that increasing the depth of cut

affected the grinding power more than increasing the traverse speed.

2.8 OPTIMIZATION OF MACHINING PARAMETERS

Grinding is an important finishing process for many engineering

components. An efficient application of the process and the resultant cost and

product quality depends on the quality of the process parameters used.

Although a significant amount of research has been done in the grinding, it is

difficult to make use of the results and published data in practical grinding

situations. This is because of the fact that the knowledge resource is not

consolidated, not easily available to the industrial user and often not user

friendly. As a result of this, the selection of grinding parameters still relies

very much on the input by human expert based on his personal knowledge

and experience (Midha et al 1991).

The existing analytical and experimental models have typically

been developed under a specific set of conditions in terms of workpiece

material, grinding wheel type and operation conditions, and hence are not

readily applicable to a process where operation conditions or the setup is

different than those used for the model development. Further, the difficulties

in optimization are associated with the fact that no comprehensive model

39

exist, which relate all the input variables to output process conditions

(Lee et al 2003)

Over the years, different strategies have been adopted for

optimization of grinding processes. The most common approaches use

grinding model to generate simulated outputs for the optimization, which can

be regarded as a model based optimization. Development of technologies

emerged as promising alternatives when traditional methods failed due to lack

of comprehensive mathematical models. These new technologies applied to

grinding processes, can be categorized into knowledge based expert systems,

genetic algorithm, fuzzy logic, Neural network , ant colony method, and

enumeration method.

Process parameter optimization (Mukherjee and Ray 2006) in any

machining operation can be undertaken in two stages. (i) modelling of input-

output and in-process parameter relationship, and (ii) determination of

optimal or near-optimal cutting conditions. Modelling of input-output and

in-process parameter relationship is considered as an abstract representation

of a process linking causes and effects or transforming process inputs into

outputs. The functional relationship between input-output and in-process

parameters as determined analytically for a cutting process is called

mechanistic model. However, as there is a lack of adequate and acceptable

mechanistic models for metal cutting processes, the empirical models are

generally used in metal cutting processes. Statistical regression, artificial

neural network and fuzzy set theory techniques are normally employed in the

application of modelling.

40

Figure 2.6 Classification of optimization techniques in metal cutting process (Mukherjee and Ray 2006)

Optimal or near-optimal solutions are determined by a suitable

optimization technique based on input-output and in-process parameter

relationship or objective function formulated from models with or without

constraints. The available optimization method is classified as conventional

and non-conventional optimization techniques. Classification of optimization

techniques in metal cutting process is shown in Figure 2.6.

Linear Programming (LP) based Algorithms

Conventional Techniques

Optimization Tools and Techniques

Non-conventional Techniques

Design of Experiment

Mathematical Iterative Search

Problem specific Heuristic search Meta-Heuristic

search

Dynamic Programming (DP) based Algorithms

Non-linear Programming (NLP) based Algorithms

Taguchi method based

Factorial Design based

Response Surface Design Methodology (RSM) based

Tabu Search

Genetic Algorithm Simulated Annealing

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2.8.1 Conventional Techniques

2.8.1.1 Taguchi method

Taguchi parameter design can optimize the performance

characteristics through the setting of design parameters and reduce the

sensitivity of the system performance to sources of variation. This method is

usually appreciated for its distribution-freed and orthogonal array design, and

it provides a considerable reduction of time and resource needed to determine

important factors affecting the operations with simultaneous improvement of

quality and cost of manufacturing. Youssef et al (1994) discussed and

compared the economical benefits of Taguchi method and fractional factorial

experiments with full factorial design technique in lathe turning operations.

Yang and Tarng (1998) used the Taguchi method to optimize the cutting

parameters for turning operations. They found that cutting speed and feed rate

are the significant cutting parameters for affecting tool life and cutting speed,

feed rate and depth of cut are the significant cutting parameters for affecting

surface roughness.

Lin (2002) presented an application of Taguchi method for multi-

response optimization in face milling operation, and shows the effectiveness

of Taguchi method for simultaneous optimization and improvement of

milling performance characteristics. Shaji and Radhakrishnan (2003)

investigated the possibility of using graphite as a lubricating medium to

reduce the heat generated at the grinding zone on surface grinding using

Taguchi method and found that the process parameters such as speed, feed,

infeed and mode of dressing as influential factors on the force components

and surface finish. Also, they informed that the use of graphite produced the

lowest surface roughness at higher infeeds.

42

Manna and Bhattacharyya (2004) used Taguchi method for

determining significant cutting parameters setting to achieve better surface

finish during turning operation of aluminium and silicon carbide–based metal

matrix composite. Ghani et al (2004) applied Taguchi method in the

optimization of end milling parameters and found that the optimum

combination of low resultant cutting force and good surface finish in milling

parameters are high cutting speed, low feed rate and low depth of cut.

2.8.1.2 Response surface design methodology

Many researchers used response surface design methodology

(RSM) in metal cutting process parameter optimization problems. Lee et al

(1996) provided an interactive algorithm using both RSM and mathematical

modelling to solve a parameter optimization problem in turning operation.

The optimal machining condition for good surface finish and dimensional

accuracy as predicted using Genetic algorithm by Suresh et al (2002). A

surface roughness prediction model for turning mild steel was developed

using Response Surface Methodology (RSM). The combination of high

speed, low feed, with moderate depth of cut and nose radius produced better

surface roughness and metal removal rate and minimizes the machining time.

Kwak (2005) effectively applied Taguchi method for evaluating the

grinding parameters’ effect on a geometric error in surface grinding process

and developed a mathematical model by Response Surface Method (RSM) for

predicting the geometrical error. It was found that low levels of depth of cut

and of grain size combined with the middle levels of wheel speed and table

speed gives geometric error.

Aggarwal et al (2008) analysed the effect of cutting speed, feed

rate, depth of cut, nose radius and cutting environment on power consumption

in CNC turning of AISI p-20 tool steel. In their study, significant parameters

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in achieving minimum power consumption were analyzed using Response

Surface Methodology (RSM) and Taguchi’s technique.

2.8.2 Non-Conventional Techniques

2.8.2.1 Genetic Algorithm

Genetic Algorithm (GA) is generally preferred for large and

complex cutting process parameter optimization problems, is based on three

basic operators, viz., reproduction, crossover, and mutation, in order to offer a

population of solutions. The algorithm creates new population from an initial

random population (obtained from different feasible combination of process

decision variables) by reproduction, crossover, and mutation in an iterative

process. The selection, crossover and mutation on initial population create a

new generation, which is evaluated with pre-defined termination criteria. The

procedure continues by considering current population as initial population

till the termination criteria are reached.

Wang et al (2002) applied GA based technique for near optimal

cutting conditions for a two and three pass turning operation having multiple

objective. Saravanan et al (2001) and (2002) developed a multi-objective

algorithm approach of the optimization of surface grinding operations using

GA. This optimization procedure was developed to optimize the grinding

conditions, viz., the wheel speed, work speed, depth of dressing and lead of

dressing, using multi-objective function model with a weighted approach for

the surface grinding process. The optimized parameter obtained using the GA

method was compared with the parameter obtained using an Ant colony

algorithm approach by Baskar et al (2004). Asokan et al (2005) used particle

swarm optimization technique.

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Gupta et al (2001) used an enumeration method to optimize the

grinding process parameters. In this procedure, corresponding to a set of

values of input variables, a set of output parameters and a corresponding

value of objective function were obtained. From all the solution sets, the user

can select conditions which would result in better performance with respect to

the desired objective. Nandi and Banerjee (2005) proposed an intelligent

approach based on fuzzy basis functional neural network to model the

cylindrical plunge grinding process.

Gopal and Rao (2003) studied the selection of optimum conditions

for the maximum material removal rate (MRR) with surface finish and

damage as constraints in SiC grinding using Genetic Algorithm (GA). The

investigation states that the parameters, the feed rate, depth of cut and grit

size are the primary influencing factors, which affect the surface integrity of

SiC grinding.

Although GA based optimization technique works well in many

situations, a few shortcomings of this method are

i) convergence of the GA is not always assured

ii) no universal rule exists for appropriate choice of algorithm

parameter, and string length

iii) GA may require a significant execution time to attain near

optimal solutions and convergence speed of the algorithm may

be slow.

2.8.2.2 Tabu search

A local search algorithm based technique, called ‘Tabu search’

(TS), developed by Glover derives its attractiveness due to its greater

flexibility and ease of implementation in combinatorial optimization

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problems. Kolahan and Liang (1996) explored the potentials of TS technique

for simultaneous decision making, attempt to minimize drilling cost by setting

a number of machining parameters, such as machine cutting speed, tool

travel, and tool selection for a drilling operation in a plastic mould, there is

hardly any report indicating an application of this technique for metal cutting

process parameter optimization.

2.8.2.3 Simulated Annealing

Simulated annealing (SA) technique, based on the concept of

modelling and simulation of a thermodynamic system, may be used to solve

many combinatorial process optimization problems. This technique starts

with selection of an initial random process decision vector and moves to new

neighbourhood decision vector that improves objective function value. SA

technique may accept inferior decision vector based on certain probabilistic

measure to avoid local optimal in multimodal response function. The

probability that there is a move to an inferior decision vector decreases as the

value of a ‘temperature parameter’ defined in the algorithm, decreases, which

is analogous with slow cooling in an annealing process to attain perfect

crystalline state. SA procedure of stochastic search algorithm gradually

changes to a traditional gradient descent search method as the temperature

parameter value drops. Chen and Su (1998) determined near optimal

machining conditions for a continuous profile turning operation in CNC by

using SA algorithm, and claimed that the algorithm deliver high quality

heuristic solution with reasonable computational requirements. SA is

appreciated for its simplicity and effectiveness, the convergence of the

algorithm may be strongly affected by the parameters of cooling schedule,

and no universally acceptable levels of control parameters in cooling schedule

exist for different types of cutting process parameter optimization problem.

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Moreover, the repeatability of the near optimal solution obtained by SA with

same initial cutting conditions is not guaranteed.

2.9 MEASUREMENT OF SURFACE ROUGHNESS

Surface roughness plays an important role in many areas, and is a

factor of great importance in the evaluation of machining accuracy. Although

many factors affect the surface condition of a machined part, the machining

parameters have a significant influence on the surface roughness for a given

machine tool and workpiece set-up.

Grinding processes are often selected for the final finishing of

components because of their ability to satisfy stringent requirements of

surface roughness and tolerance. Surface roughness and tolerance are closely

interrelated, as it is generally necessary to specify a smoother finish in order

to maintain a finer tolerance in production. A close-tolerance dimension

requires a very fine finish. The finishing of a component to a very low

roughness value may require multiple machining operations. A very fine

surface finish would require grinding or additional subsequent operations,

such as honing, super finishing and buffing.

The reliability of the mechanical components, especially for high

strength applications, often critically depends upon the quality of the surface

produced by machining. This quality consists of two aspects: surface integrity

and surface topography. The surface integrity of a workpiece is associated

with the mechanical and metallurgical alterations of the surface layer induced

by machining. Surface topography refers to the geometry of machined

surfaces, which is usually characterized by surface roughness. The surface

texture refers to the micro geometry or the topography of the machined

surface.

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Ali and Zhang (1999) presented the surface roughness prediction of

ground surface produced by surface grinding operations, using Fuzzy logic

approach. In this study they revealed that though surface roughness is one of

the most important factors in assessing the quality of a ground component,

there is no comprehensive model that can predict the surface roughness over a

wide range of operating conditions. The difficulty stems from the fact that

many variables affect the process. These include: the work material

properties, grinding wheel composition, dressing conditions, operating

parameters, coolant properties and machine vibration.

The surface roughness can be measured by a variety of instruments,

including both surface contact and non-contact types. So far, the most

universal technique is to measure the surface roughness with a contact type

stylus instrument, which provides a numerical value for surface roughness.

Such instrument provides an indication of the surface roughness in terms of

the arithmetic average, Ra.

2.10 MEASUREMENT OF SURFACE HARDNESS

Grinding is a manufacturing method imparting close dimensional

tolerance and good surface roughness. Due to very high strain rate in the

metal removal lot of heat is generated leading to increase in temperature at

the grinding zone and temperature gradient in the work material. This in turn

causes structural changes, changes in hardness distribution and generation of

tensile residual stresses in the surface layer of the workpiece. All these

changes of the surface integrity are highly disadvantageous because they lead

to a significant deterioration of tribological and fatigue properties.

Murthy et al (2000) reported that the hardness of the ground steels

are likely to drop sharply with rise in temperature beyond 4000-5000C due to

over tempering. On the other hand the austenite manganese steel gets work

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hardened and the hardness rise sharply due to the transformation of austenite

into martensite.

Algumurthy et al (2007) analyzed the total amount of heat

generated, amount of heat entering into the workpiece, energy partition ratio,

grinding temperature, grain depth of cut, grain contact length, grit contact

time with the chip, optimization of heat generation and wheel-work contact

zone temperature for steels of different carbon composition (AISI 3310, 6150,

52100) in cylindrical grinding. Taguchi method was applied in optimizing

the heat generation and temperature developed in the grain contact zone by

considering surface roughness, surface hardness and temperature as an output

parameters. The optimization output stated that the depth of cut is the most

influencing parameter followed by the work speed, number of passes and the

wheel speed.

Hardness of the materials are measured using various test such as

abrasion test, scratch test, impact test, cutting test, rebound test etc., in which

static indentation test (Brinell, Knoop, Vickers and Rockwell) is the most

preferred test in the area of material testing. In grinding process the surface

and subsurface hardness are measured using Vickers test (Brinksmeier and

Minke 1993, Tonshoff et al 2002).

2.11 THE RESEARCH GAP

Generally, the strain gauge or piezoelectric sensor based

dynamometer are employed in the measurement of cutting forces in all

conventional metal cutting operations, including surface grinding. These

dynamometers cannot be used in cylindrical grinding, because the workpiece

and the grinding wheel are in motion. Meanwhile, some researchers have

proposed various force measurement methods for cylindrical grinding. In all

these methods, the modification has to be done in the workpiece or in the

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machine setup. These modifications cannot be implemented in the industries

and are only suitable for research laboratory. The main objective of this

research work is to develop a measurement setup that can be implemented in

the industrial work without any major difficulty.

2.12 NEED FOR THIS STUDY

Grinding force has a direct influence on grinding wheel wear,

grinding temperature and surface quality of the workpiece. The specific

energy and the temperature generated during grinding are directly related to

the grinding forces in the form of mechanical energy. The wear mechanism of

a wheel depends on the force on an individual grain. Depending on the

magnitude of this force, wear may occur by attrition or by fracture. The

cutting force is proportional to the specific energy in grinding and this

influences the performance and surface integrity of the workpiece. Hence, the

measurement of force in grinding is very significant.

Grinding is a complicated process which involves many

parameters, such as wheel velocity, workpiece velocity, depth of cut, feed

rate, grit size, type of abrasive, wheel structure, etc. It is an important task to

select grinding parameters for achieving high grinding performance. Usually,

the desired grinding parameters and the cutting fluid are determined based on

experience or by the use of a handbook. However, this does not ensure that

the selected grinding parameters have optimal or near optimal grinding

performance for a particular machine. Hence, the quality performance in any

machine can be obtained through optimization.

2.13 SUMMARY OF LITERATURE

The summary of the literature indicates that most of the research

work is concentrated in the area of force measurement, product quality

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analysis, tool condition monitoring, and the development of mathematical

model and optimization of machining parameters in the metal cutting

operations. Extensive studies can be found in the literature on the design and

development of the dynamometer. Most of these developments are focused on

turning, milling, drilling and surface grinding due to the inherent facility

available in the machine to accommodate the dynamometer. Due to the

difficulties in locating the dynamometer in cylindrical grinding, not much

research work has been done.

The understanding of the grinding parameters and their relation to

the responses in cylindrical grinding, are still limited and yet to be studied.

The factors that have been outlined in the literature have considered, to

explore the changes on the selected responses in cylindrical grinding.