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CHAPTER 2

LITERATURE SURVEY

2.1 INTRODUCTION

The end milling input parameters play a very significant role in

determining the quality of machining. Generally all end milling processes are

used with the aim of obtaining a product with the desired machining quality,

minimum surface roughness, cutting force, vibration amplitude, temperature

and tool wear. Nowadays the application of DOE, evolutionary algorithms

and computational networks are widely used to develop a mathematical

relationship between the end milling process input parameters and output

variables of the end mill in order to determine the optimum process

parameters that results in the desired machining quality.

The present study focuses on surface roughness, cutting force,

vibration amplitude, temperature rise, tool wear, surface topography study in

CNC end milling of Al7075-T6 aluminum alloy and a Finite Element

simulation of cutting processes using the Deform Software for Al7075-T6. In

this work, predictive models were developed based on response surface

methodology and artificial neural networks to study the main and interaction

effects of process parameters on the above responses. Various nontraditional

optimization techniques like PSO, SA and GA were used to optimize the

process parameters to get the desired machining quality. A detailed, surface

topography study has been made to understand the effect of the process

parameters on metallography of this Al7075-T6 aluminum alloy.

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A comprehensive literature review of the application of these

methods in the area of machining has been presented here. The focus of the

literature survey collected pertains to surface roughness, cutting force,

vibration amplitude, temperature, tool wear, surface topography study with

respect to various materials in different machining processes. The literature

survey also gives collective information about various methodologies

employed for conducting experiments, application of artificial neural

networks for developing predictive models and optimization of process

parameters using nontraditional optimization techniques with respect to

various machining processes.

The review was classified according to the output features of the

end mill as presented below:

1. Surface roughness

2. Cutting force

3. Vibration amplitude

4. Temperature rise

5. Tool wear

6. Surface topography study

7. Finite element analysis

2.2 SURFACE ROUGHNESS

Surface roughness is a measurable characteristic based on the

roughness deviations as defined in the preceding. Surface finish is a more

subjective term denoting smoothness and general quality of a surface. In

popular usage, surface finish is often used as a synonym for surface

roughness. The most commonly used measure of surface texture is surface

roughness. With respect to Figure 2.1, the surface roughness can be defined as

the average of the vertical deviations from the nominal surface over a

specified surface length.

24

Figure 2.1 Deviations from the nominal surface used in the two definitions of surface roughness

An Arithmetic Average (AA) is generally used, based on the

absolute values of the deviations, and this roughness value is referred by the

name average roughness. In Equation form (2.1)

0

mL

am

yR dx

L(2.1)

where Ra = arithmetic mean value of roughness, m (µm); y = the vertical

deviation from nominal surface (converted to absolute value),m(µm); and

Lm=the specified distance over which the surface deviations are measured.

The AA method is the most widely used averaging method for

surface roughness today. An alternative, sometimes used in the United States,

is the Root-Mean-Square (RMS) average, which is the square root of the

mean of the squared deviations over the measuring length. RMS surface

roughness values will almost always be greater than the AA values because

the larger deviations will figure more prominently in the calculation of the

RMS value.

Surface roughness suffers the similar deficiencies of any single

measure used to assess a complex physical attribute. For example, it fails to

account for the lay of the surface pattern; thus, surface roughness may vary

significantly, depending on the direction in which it is measured.

25

Another deficiency is that waviness can be included in the Ra

computation. To deal with this problem, a parameter called the cutoff length

is used as a filter that separates the waviness in a measured surface from the

roughness deviations. In effect, the cutoff length is a sampling distance along

the surface. A sampling distance shorter than the waviness width will

eliminate the vertical deviations associated with waviness and only include

those associated with roughness. The most common cutoff length used in

practice is 0.8mm. The measuring length Lm is normally set at about five

times the cutoff length.

The actual surfaces of a manufactured part are determined by the

processes used to make it. The variety of processes available in manufacturing

result in wide variations in surface characteristics, and it is important for

engineers to understand the technology of surfaces. Surface technology is

concerned with (1) defining the characteristics of a surface, (2) surface

texture, (3) surface integrity.

A microscopic view of a part’s surface reveals its irregularities and

imperfections. The features of a typical surface are illustrated in the highly

magnified cross section of the surface of a metal part in Figure 2.2. Although

the discussion here is focused on metallic surfaces, these comments apply to

ceramics and polymers, with modifications owing to differences in the

structure of these materials. The bulk of the part, referred to as the substrate,

has a grain structure that depends on previous processing of the metal; for

example, the metal’s substrate structure is affected by its chemical

composition, the casting process originally used on the metal, and any

deformation operations and heat treatments performed on the casting

(MikellGroover 2010).

26

Figure 2.2 A magnified cross section of a typical metallic part surface

The exterior of the part is a surface whose topography is anything

but straight and smooth. In this highly magnified cross section, the surface has

roughness, waviness, and flaws. Although not shown here, it also possesses a

pattern and/or direction resulting from the mechanical process that produced

it. All of these geometric features are included in the term surface texture.

Just below the surface is a layer of metal whose structure differs

from that of the substrate. This is called the altered layer, and it is a

manifestation of the actions that have been visited upon the surface during its

creation and afterward. Manufacturing processes involve energy, usually in

large amounts, which operates on the part against its surface. The altered layer

may result from work hardening (mechanical energy), heat (thermal energy),

chemical treatment, or even electrical energy. The metal in this layer is

affected by the application of energy, and its microstructure is altered

accordingly. This altered layer falls within the scope of surface integrity,

which is concerned with the definition, specification,and control of the

surface layers of a material (most commonlymetals) in manufacturing and

subsequent performance in service. The scope of surface integrity is usually

interpreted to include surface texture as well as the altered layer beneath.

Both surface roughness and waviness can be measured by a variety

of instruments, including both surface contact and non-contact types. By far

the most universal technique is to measure surface roughness with a stylus

contact-type instrument that provides a numerical value for surface roughness.

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The limitations of surface roughness have motivated the development of

additional measures that more completely describe the topography of a given

surface. The literature survey also gives collective information about various

methodologies employed for conducting experiments based on the input and

output parameters of the end mill. The literature survey pertaining to the work

of other researchers is indicated below.

Alauddin et al (1995) developed a surface-roughness model for the

end milling of 190 BHN steel. They identified that feed rate is a very

dominant factor in both first and second order model and an increase in either

the feed rate or axial depth of cut increases the surface roughness, whilst an

increase in cutting speed decreases the surface roughness.

Mike et al (1999) the author investigated a new approach for finish

surface prediction in end-milling operations. Through experimentation, the

system proved capable of predicting the surface roughness (Ra) with about

90% accuracy. The author concluded feed rate was the most significant

machining parameter used to predict the surface roughness in the multiple

regression models.

Yu Hsuan et al (1999) developed an in-process based surface

recognition system to predict the surface roughness in the end milling process.

A back propagation artificial neural network model was developed by using

spindle speed, feed rate, depth of cut, and the vibration average per revolution

as four input neurons to predict surface roughness.

Benardos&Vosniakos (2002) presented a neural network modeling

approach for the prediction of surface roughness (Ra) in CNC face milling.

The data used for the training and checking of the networks’ performance was

derived from the experiments conducted on a CNC milling machine according

to the principles of Taguchi’s Design of Experiments (DoE) method. The

factors considered in the experiment were the depth of cut, the feed rate per

28

tooth, the cutting speed, the engagement and wear of the cutting tool, the use

of cutting fluid and the three components of the cutting force. Using feed-

forward Artificial Neural Networks (ANNs) trained with the Levenberg–

Marquardt algorithm, the most influential of the factors were determined,

again using DoE principles, and an ANN based on them was able to predict

the surface roughness with a mean squared error equal to 1.86%.

Ming & Hung (2004) analyzed the influence of machining

parameters such as the cutting speed, feed, depth of cut, concavity and axial

relief angles of the cutting edge of the end mill on surface roughness in the

slot end milling of aluminum alloy. Predictive surface roughness models were

built by applying response surface methodology for both dry and coolant

cutting conditions. They concluded that the significant factors affecting the

dry-cut model were the cutting speed, feed, concavity and axial relief angles

and for the coolant model, the feed and concavity angle.

Ghani et al (2004) applied the Taguchi optimization method to

optimize cutting parameters in end milling when machining hardened steel

AISI H13 with TiN Coated P10 carbide insert tool under semi-finishing and

finishing conditions of high-speed cutting. The milling parameters evaluated

were cutting speed, feed rate and depth of cut. An orthogonal array, signal-to-

noise ratio and Pareto analysis of variance were employed to analyze the

effect of these milling parameters. The analysis of the result showed that the

optimal combination for low resultant cutting force and good surface finish

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

Brezocnik et al (2004) proposed genetic programming approach to

predict the surface roughness in end milling. Cutting parameters such as

spindle speed, feed, and depth of cut and also vibration between tool and

work piece, were used to predict the surface roughness. The authors found

29

that the proposed model that involves all these variables predict the surface

roughness accurately.

Oktem et al (2005) focused on the development of an effective

methodology to determine the optimum cutting conditions leading to

minimum surface roughness in the milling of mold surfaces by coupling

Response Surface Methodology (RSM) with a developed Genetic Algorithm

(GA). RSM is utilized to create an efficient analytical model for surface

roughness in terms of cutting parameters: feed, cutting speed, axial depth of

cut, radial depth of cut and machining tolerance. For this purpose, a number

of machining experiments based on statistical three-level full factorial design

of experiment method are carried out in order to collect surface roughness

values. An effective fourth order Response Surface (RS) model is developed

utilizing experimental measurements in the mold cavity. RS model is further

interfaced with the GA to optimize the cutting conditions for desired surface

roughness.

Babur et al (2005) employed feed forward artificial neural network

to predict surface roughness in terms of cutting parameters such as cutting

speed, feed rate, depth of cut and material removal rate and further optimized

to obtain minimum surface roughness by genetic algorithm. The input and

output data for the developed neural model was obtained from the

experimental values, conducted based on a three-level full factorial

experimental design technique.

Babur et al (2006) established a first and second order statistical

model to predict surface roughness for high-speed flat end milling process

under wet cutting conditions by using rotatable central composite design.

Eyup&Seref (2006) used Taguchi method for investigating the

effects of cutting parameters on the surface roughness value in the face

30

milling of stellite 6 materials. The milling parameters evaluated was feed rate,

cutting speed and depth of cut.

Oktem et al (2006) focused on the development of an effective

methodology to determine the optimum cutting conditions leading to

minimum surface roughness in the milling of mold surfaces by coupling

response surface methodology (RSM) with a developed genetic algorithm.

GA optimization technique was used by Palanisamy, Rajendran,

&Shanmugasundaram (2007) to find the most optimal process parameters of

end milling machining such as cutting speed, depth of cut and feed rate. The

objective function considered in this study was machining time.The authors

concluded that the optimized process parameters are capable of machining the

work piece more efficiently with better surface finish.

Julie et al (2007) applied the Taguchi method to optimize the

machining parameters such as spindle speed, feed rate and depth of cut for

surface roughness. They concluded that the effects of spindle speed and feed

rate on the surface were larger than the depth of cut for milling operation.

Mohammed et al (2007) developed a multiple regression model to

predict the surface roughness in end milling process by relating with spindle

speed, cutting feed rate and depth of cut. The effects of spindle speed, feed

rate and depth of cut on surface roughness were analyzed.

Kadirgama et al (2007) developed the surface roughness prediction

models, with the aid of statistical methods, for Hastelloy C-22HS when

machined by physical vapor deposition and chemical vapor deposition coated

carbide cutting tools under various cutting conditions. These prediction

models were then compared with the results obtained experimentally. By

using RSM, first order models were developed with 95% confidence level.

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The surface roughness models were developed in terms of cutting speed, feed

rate and axial depth using RSM as a tool of DoE. In general, the results

obtained from the mathematical models were in good agreement with those

obtained from the machining experiments.

Palanikumar (2008) employed both Taguchi and response surface

methodologies for minimizing the surface roughness in machining glass fiber

reinforced plastics with a polycrystalline diamond tool. The cutting

parameters used were cutting speed, feed and depth of cut. The effect of

cutting parameters on surface roughness was evaluated and the optimum

cutting condition for minimizing the surface roughness was determined.

In the research by Prakasvudhisarn et al (2009), process parameters

of CNC end milling were selected such as feed rate, spindle speed, and depth

of cut to find the minimum surface roughness. Support vector machine was

proposed to capture characteristics of roughness and its factors. PSO

technique is then employed to find the combination of optimal process

parameters. The results showed that cooperation between both techniques can

achieve the desired surface roughness and also maximize productivity

simultaneously.

Turnad et al (2009) employed central composite response surface

methodology to develop an analytical model for surface roughness in terms of

cutting parameters such as cutting speed, axial depth of cut, and feed per

tooth. Design of the expert software package was applied to establish the first

order and the second order mathematical model. The adequacy of the

predictive model was verified using analysis of variance.

Routara et al (2009) conducted experiments for three different work

piece materials to see the effect of work piece material variation in this

respect. Five parameters, viz., center line average roughness, root mean

32

square roughness, skewness, kurtosis and mean line peak spacing were

considered. The second-order mathematical models, in terms of the

machining parameters, were developed for each of these five roughness

parameters prediction using RSM on the basis of experimental results. The

roughness models as well as the significance of the machining parameters

were validated with ANOVA. It was found that the response surface models

for different roughness parameters were specific to work piece materials. An

attempt was also made to obtain optimum cutting conditions with respect to

each of the five roughness parameters using a response optimization

technique.

In the study by Zain et al (2010), the three parameters of end

milling were considered for minimizing surface roughness. From the

experiments, it was recommended that process parameters should be set at the

highest cutting speed, lowest feed and highest radial rake angle in order to

achieve the minimum surface roughness.

Del Prete et al (2010) developed a prediction model for surface

roughness in flat end mill operation using RSM. ANN was used to predict

surface roughness and GA was employed to optimize the surface roughness

model. By coupling developed an RS model with GA, the optimization

methodology is effective and can be effective if the developed RS model is

accurate.

In Alam et al (2010), machining process parameters of NC milling

such as speed, feed rate, and depth of cut was used to predict surface

roughness. In the paper, the quadratic prediction model was coupled with GA

to optimize the machining process parameters for the minimum surface

roughness.

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Chandrasekaran et al (2010) reviewed the application of soft

computing tools such as neural networks, fuzzy sets, genetic algorithms,

simulated annealing, ant colony optimization, and PSO to our machining

processes turning, milling, drilling, and grinding. The authors highlighted the

progress made in this area and discussed the issues that need to be addressed.

SuleymanNeseli et al (2011) carried out to develop the

mathematical model of the surface roughness to investigate the influences of

cutting tool geometry parameters and found the optimum value of geometry

parameters, the quadratic model of response surface methodology was

utilized. The author has indicated that the tool nose radius was the dominant

factor on the surface roughness.

Kadirgama et al (2012) developed a surface roughness model to

optimize machining conditions of aluminium alloys with carbide coated

inserts by design of experiments method and response surface methodology .

Patel (2012) explored the influence of various machining

parameters like tool speed, tool feed, depth of cut and tool diameter. In their

study, experiments were conducted on AL 6351 –T6 material with four

factors and five levels. This paper attempted to introduce how Taguchi

parameter design could be used to identify the significant processing

parameters and to optimize the surface roughness of end-milling .

Ahmet (2013) determined the effects of process parameter on

surface roughness and the factor levels with minimum surface roughness in

pocket machining. The author found that that surface roughness correlates

negatively with cutting speed and positively with feed rate and cutting depth.

34

ZahiaHessainia et al (2013) developed a model to predict surface

roughness in hard turning by using response surface methodology. They

author taken the input parameters were cutting speed, feed rate, depth of cut

and tool vibration in radial and in main cutting force directions. Their results

indicated that the feed rate was the dominant factor affecting the surface

roughness, whereas vibrations on both predicted directions have a low effect

on it.

2.3 CUTTING FORCE

Cutting force is one of the important characteristic variables to be

monitored during the machining process. Tool breakage, tool wear, and work

piece deflection are mainly due to abnormal cutting force developed during

the machining process (Lei & Li 2004). Cutting forces of the tool point are

measured by specially designed dynamometers. Early researchers used a

variety of hydraulic, pneumatic and strain gage instruments, however

piezoelectric dynamometers employing quartz load measuring elements are

most commonly used for cutting force measurement. The dynamometers are

mounted between the tool or workpiece and non rotating part of the machine

tool structure. A coordinate system can be used to resolve the cutting forces

into directional components. In the milling process, force components are

related to the axes of motion of the machine tool. Three resolved component

of the force are infeed force, crossfeed force and thrust force. The infeed force

acts tangent to the rotating tool and acts in the x direction of the machine tool,

crossfeed force acts normal to the rotating tool and acts in the y direction of

the machine tool and thrust force acts parallel to the axis of the tool and acts

in the z direction of the machine tool is shown in Figure 2.3. The literature

survey pertaining to the work of other researchers is indicated below.

35

Figure 2.3 Cutting forces in end milling

Li Zheng et al (1997) presented a generalized cutting force model

in terms of material properties, tool geometry, cutting parameters and process

configuration. The model specifies the interaction between workpiece and

multiple cutters, flutes by the convolution of cutting-edge geometry function

with a train of impulses having the period equivalent to tooth spacing. The

authors conducted experiments over various cutting conditions and obtained

the results to verify the model fidelity.

Wen (2000) explained the influence of dynamic radii, feed rate,

radial and axial depths of cut on cutting forces. The author concluded that

when radial and axial depths of cut increase, the cutting forces also increase

since the engaged flute lengths are increased.

Tandon& El-Mounayri (2001) employed neural network to predict

cutting force in terms of machining parameters such as tool diameter, spindle

speed, feed rate, number of flutes, rake angle, clearance angle, axial and radial

depth of cut. The authors concluded that this model can predict accurately the

cutting forces in three directions.

Lin et al (2003) used radial basis function neural network and

multiple regression analysis to predict machining forces–tool wear

36

relationship in the machining of aluminum metal matrix composites. Besides

process parameters, feed and cutting forces were used to estimate tool wear.

The authors obtain better correlation of tool wear with feed force data than

with cutting force.

Kovacic et al (2004) proposed modeling of cutting forces with

genetic programming, which imitates the principles of living beings.

Measurements were made from two materials (aluminum alloy and steel) and

two different types of milling (conventional milling and STEP milling). For

each material and type of milling parameters, tensile strength and hardness of

work piece, tool diameter, cutting depth, spindle speed, feeding and type of

milling were monitored, and cutting forces were measured for each

combination of milling parameters. On the basis of the experimental data,

different models for cutting forces prediction were obtained by genetic

programming.

Radhakrishnan&Nandan (2005) predicted cutting force model

using regression and neural networks. A regression model was used to filter

out abnormal data points and the filtered data were used in the neural network

for better prediction.

Palanisamy et al (2006b) developed a cutting force model to predict

the tangential and thrust cutting force in end milling of AISI 1020 steel. The

model prediction was validated with the experimental cutting forces during

the machining of AISI 1020 steel using a three-axis milling tool

dynamometer.

Li et al (2006) presented an experimental study on cutting force

variations in the end milling of Inconel 718 with coated carbide inserts. The

cutting force variation along with the tool wear propagation was analyzed.

37

The authors concluded that the tool wear propagation was responsible for the

increase of the mean peak force.

Haci et al (2006) developed a model to calculate the various

components of cutting forces and analyzed the effect of cutting parameters

and tool geometry on cutting force.

Omar et al (2007) introduced a method to simultaneously predict

the conventional cutting forces along with 3D surface topography during side

milling operation. The model incorporates the effects of tool runout, tool

deflection, system dynamics, flank face wear, and the tool tilting on the

surface roughness.

Viktor &Astakhov (2007) discussed the effects of the cutting feed,

depth of cut, and workpiece diameter on the tool wear rate. They result

showed that the influence of the cutting feed on the tool wear rate was

different at different cutting speeds and the depth of cut on the tool wear rate

was negligibly small if the machining was carried out at the optimum cutting

regime.

Ganesh et al (2008) employed response surface methodology to

develop a mathematical model to predict cutting forces in terms of depth of

cut, feed, cutting speed and immersion angle by using response surface

methodology in end milling of composite material. The authors analyzed

direct and interaction effects of the machining parameter with cutting forces.

Soo Kang et al (2008) studied an analytical cutting force model for

micro end milling was proposed for predicting the cutting forces. The cutting

force model, which considered the edge radius of the micro end mill, was

simulated. They found that the increasing of thrust force affects the feed

direction cutting force in micro end milling with a very small feed per tooth.

38

Anayet et al (2009) discussed the development of the first and

second order models for predicting the tangential cutting force produced in

end-milling operation of medium carbon steel. The author found that an

increase in either the feed or the axial depth of cut increases the cutting force,

whilst an increase in the cutting speed decreases the cutting force.

Budak et al (2009) presented models for 5-axis milling process

geometry, cutting force and stability. The application of the models in

selection of important parameters was also demonstrated. A practical method,

developed for the extraction of cutting geometry, was used in simulation of a

complete 5-axis cycle.

Tongchao Ding et al (2010) experimentally investigated the effects

of cutting parameters on cutting forces and surface roughness in hard milling

of AISI H13 steel with coated carbide tools. Based on Taguchi’s method,

four-factor (cutting speed, feed, radial depth of cut, and axial depth of cut)

four-level orthogonal experiments were employed. Three cutting force

components and roughness of machined surface were measured, and then

range analysis and ANOVA are performed. It is found that the axial depth of

cut and the feed are the two dominant factors affecting the cutting forces. The

validity of the model was proved through cutting experiment, and model was

used, predict the machined surface roughness from the information on the

cutting parameter.

Zhang et al (2010) analyzed the tool wear and the cutting force

variation during high-speed end-milling Ti-6Al-4V alloy. The experimental

results showed that the major tool wear mechanisms in high-speed end-

milling Ti-6Al-4V alloy with uncoated cemented tungsten carbide tools are

adhesion and diffusion at the crater wear along with adhesion and abrasion at

the flank wear.

39

Peng et al (2011) investigated the characteristics of high speed

machining dynamic milling forces of Titanium alloy by use of polycrystalline

diamond tools. They found that amplitudes increase with the increase of

cutting speed and tool wear level, which could be applied to the monitoring of

the cutting process.

2.4 VIBRATION AMPLITUDE

During milling, the cutter and workpiece move with each other with

a frequency determined by the natural frequency of the machine tool. Chatter

is a resonant vibration that occurs when the forces acting on the cutting tool

cause it to vibrate at a natural frequency of the machine in which a minimum

excitation produces maximum amplitude. Once this condition is established

the interaction between the cutter and work piece will sustain vibration and

constant pounding will reduce tool life, impair surface finish and also high

amplitude may cause damage to machine tool. There are many parameters,

almost all the components of machine tool and tool system are involved in the

generation of chatter vibration. In general chatter vibrations occur due to lack

of rigidity in the machine tool and cutting conditions. The altering machine

tool is not possible but predicting the right cutting condition to reduce the

chatter is possible by controlling the process parameter of the end milling.

The action of the milling cutter is totally different from that of a

drill or a turning tool. In turning and drilling, the tools is kept continuously in

contact with the material to be cut, whereas milling is an intermittent process,

as each tooth produces a chip of variable thickness. It is possible for periodic

force variations in the cutting process to interact with the dynamic stiffness

characteristics of the machine tool to create vibrations during processing that

are known as chatter.

40

Monitoring of cutting tools and cutting process is of considerable

economic importance in the manufacturing industry. Continuity of production

with improved quality and reliability, preservation of investing capital,

optimization of manufacturing process efficiency and economic operation can

only be assured by an efficient monitoring of cutting tools, to predict damage

and to avoid any disturbances which affect the quality of machined

components. Prediction of the vibration of the machine tool is great concern

as it helps to increase quality of machining.

In milling operations, cutting edge impacts excite vibrations due to

the interaction between the cutter and the workpiece, and because of the

system’s lack of dynamic stiffness. It is possible to distinguish between free,

forced and self-excited vibrations. During a milling operation these three

different types of mechanical vibrations propagate through the air and

generate a sound that intrinsically contains information about the process. In

machine shops and on production floors, chatter is easily recognized from the

loud noise that accompanies it and the visible chatter marks that the cutting

tool leaves on the workpiece surface.

Milling is a metal cutting process in which the cutting tool

intermittently enters and leaves the workpiece, unlike turning, in which the

tool is always in contact. In the milling process, material is removed from a

workpiece by a rotating cutting tool. While the tool rotates, it translates in the

feed direction at a certain speed. A basic dynamic model of 2-DOF end-

milling with a flexible tool is illustrated in Figure 2.4.

41

Figure 2.4 Dynamic model of end-milling (2-DOF)

Metal cutting processes can entail three different types of

mechanical vibrations that arise due to the lack of dynamic stiffness of one or

several elements of the system composed by the machine tool, the tool holder,

the cutting tool and the workpiece material. These three types of vibrations

are known as free vibrations, forced vibrations and self-excited vibrations

(David & John 2006)

Free vibrations occur when the mechanical system is displaced

from its equilibrium and is allowed to vibrate freely. In a metal removal

operation, free vibrations appear, for example, as a result of an incorrect tool

path definition that leads to a collision between the cutting tool and the

workpiece. Forced vibrations appear due to external harmonic excitations.

The principal source of forced vibrations in milling processes is when the

cutting edge enters and exits the workpiece. Free and forced vibrations can be

avoided, reduced or eliminated when the cause of the vibration is identified.

Engineers have developed several widely known methods to mitigate and

reduce their occurrence. Self-excited vibrations extract energy to start and

grow from the interaction between the cutting tool and the workpiece during

the machining process. This type of vibration brings the system to instability

42

and is the most undesirable and the least controllable. For this reason, chatter

has been a popular topic for academic and industrial research. Regenerative

chatter is the most common form of self-excited vibration. It can occur often

because most metal cutting operations involve overlapping cuts which can be

a source of vibration amplification. The cutter vibrations leave a wavy surface

(see Figure 2.4). When milling the next tooth in cut attacks this wavy surface

and generates a new wavy surface. The literature survey pertaining to the

work of other researchers is indicated below.

Lacerda& Lima (2004) applied analytical method to develop an

algorithm to predict cutting forces. The forces in the contact were evaluated

by an algorithm using a mathematical model derived from experimental tests.

A stability lobes diagram has been plotted to this dynamic system. These

curves relate the spindle speed with axial depth of cut, separating stable and

unstable areas, allowing the selection of cutting parameters resulting

maximum productivity, with acceptable surface roughness and absence of

chatter vibrations.

Kivanc&Budak (2004) presented Structural modeling for predicting

deflections and vibrations in milling processes. Static and dynamic analysis of

tools with different geometry and material are carried out by finite element

analysis. In this analysis, experimental or analytic frequency response

functions for the individual components are used to predict the final

assembly’s dynamic response.

Liu & Cheng (2005) presented a new approach modeling and

predicting the machining dynamics for peripheral milling. First, a machining

dynamics model was developed based on the regenerative vibrations of the

cutter and workpiece excited by the dynamic cutting forces, which were

mathematically modeled and experimentally verified by the authors. Then, the

mechanism of surface generation was analyzed and formulated based on the

43

geometry and kinematics of the cutter. Thereafter a simulation model of the

machining dynamics is implemented using Simulink. In order to verify the

effectiveness of the approach, the transfer functions of a typical cutting

system in a vertical CNC machining center were measured in both normal and

feed directions by an instrumented hammer and accelerometers. Then a set of

well-designed cutting trials was carried out to record and analyze the dynamic

cutting forces, the vibrations of the spindle head and workpiece, and the

surface roughness and waviness. Corresponding simulations of the machining

processes of these cutting trials based on the machining dynamics model were

investigated and the simulation results are analyzed and compared to the

measurements. It was shown that the proposed machining dynamics model

could well predict the dynamic cutting forces, the vibrations of the cutter and

workpiece.

Budak&Tekeli (2005) had shown that, for the maximization of

chatter free MRR, radial depth of cut is of equal importance. The authors had

proposed a method to determine the optimal combination of depths of cut, so

that chatter free MRR is maximized. The application of the method was

demonstrated on a pocketing example where a significant reduction in the

machining time was obtained using the optimal depths. The procedure can

easily be integrated into a CAD/CAM system or a virtual machining

environment in order to identify the optimal milling conditions.

JanezGradisek et al (2005) experimentally investigated the Stability

of a 2-dof milling process. They found that the frequency and compliance of

the dominant vibration mode (first bending mode of the tool) were decreased

as spindle speed was increased.

Neil et al (2005) demonstrated good agreement with traditional

stability test procedures which rely on a modal hammer, and the new method

is particularly well suited to use on small milling tools which can be difficult

44

to test with a modal hammer. It is suggested that the proposed technique could

be used for automated chatter testing using intelligent tools with embedded

piezoelectric transducers. Finally, the proposed technique may prove useful to

other applications of vibration testing where a frequency response function is

to be predicted at a sensor location, when the system is excited by surface-

mounted piezoelectric actuators.

Palanisamyet al (2006a) developed an Artificial Neural Network

has been used to predict the chatter-free vibrations Stability Lobe Diagram for

milling of AISI 1020 Steel. They stated that the polar plot helps the designer

to design the machine tool system having a minimum stiffness in order to

maintain the stability.

Isa Yesilyurt (2006) presented the use of the mean frequency of a

scalogram to end mill defect detection under varying feed rates. The author

found that the feed rate is significantly influential upon the mean frequency of

a scalogram, and mean frequency variation is quite responsive to the presence

of guilt even when the severity of the fault is considerably small.

Palanisamy et al (2007) developed a mathematical model based on

both the material behavior and the machine dynamics to determine cutting

force for milling operations. The system used for optimization was based

genetic algorithms. The machining time was considered as the objective

function and constraints were tool life, limits of feed rate, depth of cut, cutting

speed, surface roughness, cutting force and amplitude of vibrations while

maintaining a constant MRR. Experimental end-milling tests were performed

on mild steel to measure surface roughness, cutting force using milling tool

dynamometer and vibration using a fast Fourier transform (FFT) analyzer for

the optimized cutting parameters in a universal milling machine using an HSS

cutter.

45

Sadettin et al (2007) revealed that increase in the vibration

amplitude results in increase in tool wear. The vibration was measured only in

the machining direction, which has more dominant signals than in the other

two directions. The measurements were taken by using an acceleration sensor

assembled on a machinery analyzer. Tool wear was measured by a

toolmaker’s microscope. It was observed that there was an increase in

vibration amplitude with increasing tool wears.

Henri &Gregoire (2007) described the influence of defects in the

position of cutting edges of long tool holder on the stability of the machining

and the raising of chatter phenomenon. It was achieved using a proper time

domain simulator of the milling process, based on an accurate geometrical

modelling of the tool, the machined surface and the material removal. This

simulator computes accurately the machined surface geometry and roughness

as well as the cutting forces and the amplitude of the cutting tool vibrations.

Julie & Joseph (2008) demonstrated a tool condition monitoring in

end milling based on the vibration signal collected through microcontroller-

based data acquisition. A data acquisition system has been built through

interfacing a microcontroller with a signal transducer for collecting cutting

vibration. The examination tests of this developed system had been carried

out on a CNC milling machine. Experimental studies and data analysis was

performed to validate the proposed system.

Kourosh&Per (2008) use of laser vibrometry for milling tool

vibration measurements during cutting is demonstrated. They outlined that

vibration velocities or displacements of the tool can be obtained with high

temporal resolution during cutting load and therefore the approach was

proven to be feasible for analyzing high-frequency milling tool vibrations.

46

Alonso et al (2008) developed a reliable tool condition monitoring

system for industrial application. They described the use of singular spectrum

analysis and cluster analysis provides an efficient automatic signal processing

method. The author proposed tool condition monitoring system based on this

procedure, was fast and reliable for tool wear monitoring.

Guillem Quintana et al (2009) dealed with milling sound

information. A sound map is a graphical sound-level representation of a

certain zone or region that is divided into points by means of a mesh. Sound

maps have typically been used with social considerations in mind: to

determine, for instance, noise levels in cities. The author determined the

stability lobe diagram of a milling process by applying sound mapping

methodologies.

Mikel et al (2010) described the concept of directional factor for

chatter stability analysis. The multi frequency analysis gives acceptable

results at stable cutting directions corresponding to very low directional

factors for end milling process and helix angle has a very important effect on

the stability.

Ahmad et al (2010) experimentally investigated to evaluate the

performance of process damped milling considering different tool geometries

(edge radius, rake and relief angles and variable helix/pitch). Their results

clearly indicated that variable helix/pitch angles most significantly increase

process damping performance.

Armando et al (2010) described a cutting-force-based vibration

analysis to ascertain the effect of the tool entering angle on tool vibration and

thus on tool life in a titanium alloy milling operation. They stated that lower

entering angles may provide stabler cutting, as indicated by the regular tool

wear instead of the microchipping resulting from the use of a higher value of

47

this angle. Although cutting forces are larger at lower entering angles, the tool

life is much longer, since most of this load is associated with low frequencies,

at which the tool behaves like a rigid body.

Jose Vicente et al (2010) reviewed six key issues involved in the

development of intelligent machining systems: (i) the different sensor systems

applied to monitor machining processes, (ii) the most effective signal

processing techniques, (iii) the most frequent sensory features applied in

modelling machining processes, (iv) the sensory feature selection and

extraction methods for using relevant sensory information, (v) the design of

experiments required to model a machining operation with the minimum

amount of experimental data and (vi) the main characteristics of several

artificial intelligence techniques to facilitate their application selection.

Ning Fang et al (2011) designed set of experiments in high-speed

finish turning of Inconel 718 has been carried out using four tool inserts with

different tool edge radii . They concluded that in high-speed turning, the tool

edge profile dynamically changes across each point on the tool cutting edge,

Tool edge wears increases as the tool edge radius increases and as the initial

tool edge radius increases, all the three components of the cutting forces

increase.

BovicKilundu et al (2011) explored the use of data mining

techniques for tool condition monitoring in metal cutting. Pseudo-local

singular spectrum analysis was performed on vibration signals measured on

the tool holder. They highlighted the two important aspects: strong relevance

of information in ‘‘high frequency’’ vibration components and robustness of

constructing features which prove the benefits of the combination of singular

spectrum analysis and band-pass filtering to get rid of useless components

(noise).

48

Banihasan et al (2011) introduced a detailed two-degree-of-freedom

mechanics based model for the study of chaotic vibrations in milling. They

pointed out the global dynamics of high speed milling in the post stability

region demonstrate the appearance of chaos for high speed machining as a

real operating condition and this behavior is mainly related to the tool loss of

contact and the multiple regenerative effect that results in a complex surface

profile.

Yasuhiro Kakinuma et al (2011) described a novel in-process

method to detect chatter vibrations in end milling is developed on the basis of

a disturbance observer theory. The author concluded that does not require any

external sensors because it uses only the servo information of the spindle

control system.

ErolTurkes et al (2012) presented a reverse running analytical

calculation procedure of traditional Stability Lobe Diagram to predict the

Variation and quantity of process damping ratio and studied how the process

damping ratio results with different materials such as AISI-1050 and Al-7075

were examined .They author discussed how much of the total process

damping ratios of cutting system was caused by the tool penetration and how

much was caused by the shear angle oscillation. Finally, verified of process

damping ratio values and the process damping model was performed by

energy equations.

Panling Huang et al (2012) introduced a signal analysis method for

milling force and acceleration was adopted to identify chatter. The

experimental results showed that when the chatter occurs, milling forces were

found to increase dramatically by 61.9– 66.8% compared with that of at stable

cutting; machining surface quality became poor and machined surface

roughness increases by 34.2–40.5% compared with that of at stable cutting.

49

2.5 TEMPERATURE RISE

Cutting temperature is an important factor that influences tool wear

and surface finish in the machining performance. The temperature at the tool

cutting edge is affected by the properties of the workpiece material, cutting

condition of the machine tool, tool geometry and many other variables

(Liu Yijianet al 2005). Heat is removed from the primary, secondary and

tertiary zones by the chip, the tool and the workpiece. Figure 2.5

schematically shows this dissipation of heat. The temperature rise in the

cutting tool is mainly due to the secondary heat source, but the primary heat

source also contributes towards the temperature rise of the cutting tool and

indirectly affects the temperature distribution on the tool rake face. During the

process, part of the heat generated at the shear plane flows by convection into

the chip and then through the interface zone into the cutting tool (Abukhshim

et al 2006). Therefore, the heat generated at the shear zone affects the

temperature distributions of both the tool and the chip sides of the toolchip

interface, and the temperature rise on the tool rake face is due to the combined

effect of the heat generated in the primary and secondary zones.

Figure 2.5 Schematic representation of a heat transfer model in metal cutting considering the combined effect of the three heat sources

50

The measurements of cutting temperatures are more difficult as the

temperature is a scalar field which varies throughout the system and cannot be

uniquely described by values at a point. The most widely used method to

measure cutting temperatures is tool-work thermocouple, which measures

average interfacial temperature at tool work piece interface (Leshock&Shin

1997). The thermocouple can be embedded in the tool or work piece to

measure the temperature accurately with less effort. Measured the cutting

temperature by inserting thermocouple in the hole drilled in the work

piece.Thermocouples are conductive, operate over a wide temperature range,

rugged and inexpensive (Shaw 1984). This measurement by thermocouple is

very useful to study the effects of the cutting parameters on the temperature.

A cost effective application is required for end milling operation to

understand the relationship between temperature rise and performance

measures (tool wear and surface finish). An effective model is essential to

predict the cutting temperature becomes necessary. The current study takes

into account the temperature rise for analysis, to understand its effect on

performance measures and to determine its predictive model from machining

parameters by using response surface methodology and artificial neural

network. The literature survey pertaining to the work of other researchers is

indicated below.

Mathew (1989) presented a method to describe the relationship

between the log of the tool wear rates and the reciprocal of the absolute

temperatures achieved at the tool/chip interface.

Kitagawa (1997) investigated to predict Temperature and wear of

cutting tools by using cutting experiments and numerical analysis in high-

speed machining of Inconel 718.

DimlaSnr (2000) reviewed of some of the methods that have been

employed in tool condition monitoring (Tool wear, Cutting forces, Vibration

51

signals, Acoustic emission, Tool temperature) and also pointed out which

sensor signals from the cutting process have been harnessed and used in the

development of tool condition monitoring systems.

Ismail Lazoglu& Yusuf Altintas (2002) developed a finite element

model to predict tool and chip temperature area in continuous machining and

time variant milling processes. They proposed model combined the steady-

state temperature prediction in continuous machining with transient

temperature evaluation in interrupted cutting operations where the chip and

the process change in a discontinuous manner. They author suggested

proposed algorithm could be used for choosing cutting speed, cutting feed

rate, tool rake and clearance angles in order to avoid excessive thermal

loading of the tool, hence reducing the edge chipping and accelerated wear of

the cutting tools.

Grzesik et al (2004) has used a special variant of the finite

difference method to predict the tool temperature fields in continuous

(orthogonal) machining of AISI 1045 steel with uncoated and coated carbide

tools. They author provides a detailed view of the turn in the two-

dimensional thermal field inside the tool body as a function of cutting speed

for the defined friction conditions and values of the heat partition coefficient.

Vincent et al (2004) developed a thermal model to predict the

distribution of temperatures generated in the cutting tool in a self-propelled

rotary tool machining and also experimentally verified through measurements

of the cutting tool temperature distribution using an infrared camera under

different cutting conditions.

Research carried out by Toh (2005) on Comparison of chip surface

temperature between up and down milling orientations in high speed rough

milling of hardened steel by using infrared red technique. The experimental

52

results showed that the chip surface temperature procreated when up milling

were in common lower as analogized to down milling at all cutter conditions

and axial depths of cut employed.

HaciSaglam et al (2007) Investigated the influence of rake angle

and approaching angle on main cutting force and tool tip temperature and

compared the measured and calculated results of cutting force components

and temperature variation generated at the tool tip on turning for different

cutting parameters and different tools having various tool geometries while

machining AISI 1040 steel hardened at HRc 40.

Abukhshim et al (2006) reviewed on heat generation and heat

dissipation in the orthogonal machining process and also provides the various

temperature measurement technique tool-work (dynamic) thermocouple,

Embedded thermocouple technique, radiation techniques, non-contact

thermographic methods, infrared pyrometer and fine powder method for metal

cutting. Finally the author proposed some modelling requirements for

computer simulation of high speed machining processes.

Richardson et al (2006) developed a thermal model to determine

the magnitude and distribution of workpiece temperatures for dry milling of

aerospace aluminium alloys. The author suggested that dry machining of

aluminium should be carried out at high cutting speeds and feeds to minimize

temperature rise in the workpiece.

Davies et al (2007) reviewed several widely used temperature

measurement methods and show how they can be applied to temperature

monitoring during material removal. Finally, pointed out the criteria critical in

measuring material removal, methods were compared, and the results

presented.

53

Masahiko et al (2007) experimentally investigated the tool–chip

interface temperature in end milling by using infrared radiation pyrometer

with two optical fibers. Their method was very practical in end milling for

measuring the temperature history at the tool – chip interface during chip

formation. They found that the maximum tool temperature in down milling is

about 50 °C higher than that in up milling and the temperature difference in

rake face is about 90 °C when the cutting speed is doubled.

Liao et al (2008) carried out the behaviors of end milling Inconel

718 super alloy by cemented carbide tools. They author pointed out the rise of

cutting temperature and strain hardenings were responsible for the difficulty

at low speed cutting. They found that high cutting temperature and difficult

chip disposal were two main problems encountered in the high speed end

milling of Inconel 718.

Palanisamy et al (2006) Investigated the tool–chip interface

temperatures for different machining conditions by using Oxley’s energy

partition function and also studied the thermal effect on the cutting force

using Rapier’s equation. They pointed out that the maximum temperature in

the tool raises with the increase in the cutting speed.

Shijun&Zhanqiang (2008) presented one-dimensional transient

temperature distributions in monolayer coated tools. They provided some data

for selecting appropriate coating materials to reduce the temperature within

coated tools.

ImedZaghbani& Victor Songmene (2009) did the analytical cutting

force model was developed for the dry high Speed Milling of Al6061-T6 and

Al7075-T6 aluminium. According to their proposed model required only

work-material properties and cutting conditions to estimate the cutting forces

and the temperature during end milling processes.

54

Kadirgama et al (2009) was employed Response Surface Method to

determine the temperature distribution on cutting tool in the machining of

HASTELLOY C-22HS with carbide coated cutting tool and also Finite

element analysis model used for verification of the temperature distribution

on cutting tool. They author pointed out the feed rate has the most dominant

parameter on the temperature, followed by the axial depth and cutting speed.

Balkrishna et al (2011) carried out experimentally and numerically

to predict of the tool–chip interface temperature, contact stress and chip

velocity. Tool wear patterns were described in terms of various cutting

conditions and the influence of tool wear on surface integrity was

investigated.

2.6 TOOL WEAR

Machining operations are accomplished using cutting tools. The

higher forces and temperatures during machining create a very harsh

environment for the tool. If cutting force becomes too high, the tool fractures.

If the cutting temperature becomes too high, the tool material softens and

fails. If neither of these conditions causes the tool to fail, continual wear of

the cutting edge ultimately leads to failure.

Cutting tool technology has two principal aspects: tool material and

tool geometry. The first is concerned with developing materials that can

withstand the forces, temperatures, and wearing action in the machining

process.The second deals with optimizing the geometry of the cutting tool for

the tool material and for a given operation.

There are three possible modes by which a cutting tool can fail in

machining:

55

1. Fracture failure. This mode of failure occurs when the cutting

force at the tool point becomes excessive, causing it to fail

suddenly by brittle fracture.

2. Temperature failure. This failure occurs when the cutting

temperature is too high for the tool material, causing the

material at the tool point to soften, which leads to plastic

deformation and loss of the sharp edge.

3. Gradual wear. Gradual wearing of the cutting edge causes loss

of tool shape, reduction in cutting efficiency, an acceleration

of wearing as the tool becomes heavily worn, and finally tool

failure in a manner similar to a temperature failure

(MikellGroover 2010).

As cutting proceeds, the various wear mechanisms result in

increasing levels of wear on the cutting tool. The general relationship of tool

wear versus cutting time is shown in Figure 2.6 Although the relationship

shown is for flank wear, a similar relationship occurs for crater wear.Three

regions can usually be identified in the typical wear growth curve.The first is

the break-in period, in which the sharp cutting edge wears rapidly at the

beginning of its use. This first region occurs within the first few minutes of

cutting. The break-in period is followed by wear that occurs at a fairly

uniform rate. This is called the steady-state wear region. In our figure, this

region is pictured as a linear function of time, although there are deviations

from the straight line in actual machining. Finally, wear reaches a level at

which the wear rate begins to accelerate. This marks the beginning of the

failure region, in which cutting temperatures are higher, and the general

efficiency of the machining process is reduced. If allowed to continue, the

tool finally fails by temperature failure.

56

Figure 2.6 Tool wear as a function of cutting time

Flank Wear (FW) is used here as the measure of tool wear. Crater

wear follows a similar growth curve. Figure 2.7 shows the characteristic wear

surfaces on a turning tool insert, end mill, form tool, and drill (ASM

Handbook 1999). The Wear surfaces on common tools due to the tool motion

shown in Figure 2.7.

Figure2.7 Wear surfaces on common tools due to the tool motion

Tool wear becomes an important factor in the machining process.

To measure tool wear, an understanding of the tool wear mechanisms at the

tool tip is necessary. Predicting and measuring wear had been the object of

research for a long time. Therefore, research into the tool wear of the cutting

57

tools is still of great interest. The literature survey pertaining to the work of

other researchers is indicated below.

Ezugwu et al (1995) proposed a model to predict tool life and

failure mode based on average and maximum flank wear, crater depth, notch

depth, surface roughness, and catastrophic failure of the tool in the machining

of gray cast iron with ceramic cutting tools. The experimental data had been

used to train the MLP neural network using back propagation algorithm.

Tarng et al (1996) used neural network is applied to the detection

of tool failure in end-milling operations. They indicated that tool failure can

be detected when unrecognized patterns start to appear in the resultant-force

spectrum.

KurapatiVenkatesh et al (1997) proposed an on-line scheme using

artificial neural networks for monitoring tool wear in a machining process.

The author findings and experience obtained should facilitate the design and

implementation of reliable and economical real-time systems for tool wear

monitoring and identification in intelligent manufacturing.

Alauddin&Baradie(1997) developed a tool-life model for the end

milling of steel using high speed steel slot drills under dry conditions by using

design of experiments and utilization of response surface methodology. The

model was developed in terms of cutting speed, feed per tooth and axial depth

of cut. The author found that the speed was the dominant factor in both the

first- and the second-order models, followed by the field and the axial depth

of cut.

Jieet al (1999) tool life test was conducted using uncoated C5

inserts and C5 inserts coated with TiN, TiAlN or ZrN in Milling. Their author

was identified the wear mechanisms of attrition, abrasion, mechanical fatigue,

58

and thermal fracture by wear maps . The author has indicated that the tool life

was found to depend more on cutting speed than on feed rate and highest at a

moderate cutting speed of 120 m/min. At lower cutting speeds wear rate

increased due to the development of BUE edge and at higher cutting speed

wear rate increased due to increased temperature in the cutting zone.

Choudhury&Subhashree (2000) measured the tool flank wear

indirectly by correlating it to the cutting parameters and the tangential cutting

force coefficient had been established to provide a reliable and sensitive

technique for on-line monitoring of tool wear in milling. The author indicates

that the effects of feed per tooth and the depth of cut on the tangential cutting

force coefficient are significant, while the effect of cutting speed on the same

is relatively insignificant due to change in tool-work material properties.

Chung Choo&Saini (2002) proposed an online fuzzy neural

network to predict tool wear. Cutting forces, acoustic emission signals, skew

and kurtosis of force bands, and total energy of forces were taken as input

parameters of the neural network.

Tsai et al (2005) presented an abductive network method for

predicting tool life in high-speed milling operations. The developed network

was capable of predicting tool life in terms of machining parameters such as

cutting speed, feed per tooth and axial depth of cut. The authors showed that

the developed model can able to predict the tool life under varying conditions

with less than 10% error.

Tansel et al (2005) proposed tool monitoring system using genetic

algorithm to monitor micro-end milling operations, which is able to estimate

wear and local damages of the cutting edges of a tool.

59

Dutta et al (2006) predicted the wear of the tungsten carbide inserts

using neural network during face milling of steel. They proposed a new

approach called modified back-propagation neural network with delta bar

delta learning to enhance the convergence speed and prediction accuracy of

the network. The authors found that MBPNND was efficient compared to

three other approaches, viz., back-propagation neural network, fuzzy back-

propagation neural network, and modified back-propagation neural network.

Natarajan et al (2006) employed a neural network model For tool

life estimation that was optimized by PSO. The use of PSO resulted in

reduction of training time by 50%.

Krain et al (2007) conducted in two phases works experimentally,

the first phase used a fixed tool material and geometry to examine the effects

of various feed rates and radial depth’s of cut and the second phase, a

reduced number of parameters were examined but various different tool

materials and geometries were utilized. Their results showed that no single

tool material or geometry gave the best overall performance. However, areas

were identified in which a specific combination of tool material and geometry

was superior.

In the research of Parent et al (2007), GA optimization technique

was proposed to find the optimal process parameters of end milling operation.

The authors presented a generalized mathematical programming model to

optimize the process parameters of end milling. Then GA was employed to

find the optimal process parameters.

Onwubolu et al (2008) presented an enhanced approach to

predictive modeling for determining tool-wear in the end-milling operations

based on the enhanced - group method of data handling . Using milling input

parameters (speed, feed, and depth of cut) and response (tool wear), the data

60

for the model was partitioned into training and testing datasets, and the

training dataset was used to realize a predictive model that was a function of

the input parameters and the coefficients determined. The results realized

using e-GMDH method was promising, and the comparative study presented

showed that the e-GMDH outperforms polynomial neural network. The

extended PSO technique was applied to obtain the optimal parameters.

Palanisamy et al (2008) developed a regression mathematical

model and artificial neural network model to predict tool flank wear in terms

of machining parameters such as cutting speed, feed and depth of cut. The

authors concluded that the predictive neural model was found to be capable of

better predictions of tool flank wear.

Quiza et al (2008) presented an investigation on tool wear

prediction on ceramic cutting tools used for turning hardened cold rolled tool

steel. The authors predicted tool wear with the help of neural network and

regression models. The prediction of neural network model was found better

and faster than the regression model.

Pinaki et al (2008) focused on end-milling of AISI 4340 steel with

multi-layer physical vapor deposition coated carbide inserts under semi-dry

and dry cutting conditions and proposes a mixed effects model for the

analysis of the longitudinal data obtained from a designed experiment. The

author found the depth of cut on the other hand did not show any significant

effect on tool wear when compared to cutting speed, feed and cutting

conditions.

Song Zhang et al (2010) determined the relationship between tool

wear, surface topography and surface roughness during the high-speed end

milling of Ti-6Al-4V alloy with uncoated carbide inserts.

61

Bharathi&Baskar (2010) used three evolutionary optimization

techniques such as SA, GA and PSO explore the optimal machining process

parameters for single pass turning operation, multi-pass turning operation and

surface grinding operation. The most affecting machining parameters are

considered such as the number of passes, cutting speed, feed, and depth of

cut. The machining performances considered in this study are the production

cost and the metal removal rate in turning operation. From the experiments, it

was found that GA gave better results compared to SA.

Rao&Pawar (2010) optimized the process parameters of multi-pass

milling operation such as the number of passes, depth of cut, cutting speed

and feed to minimize the production time (i.e., maximization of production

rate). SA was employed to find the optimal process parameters. The results of

the SA were compared with the previously published results obtained by

using other optimization techniques, ABC and PSO optimization.

Jilin Zhang et al (2012) suggested a novel technique for the tool

wear measurement based on machine vision. The author proposed scheme

was shown to be reliable and effective for the automated tool wear

measurement. It provided a basis for industrial application to on-line tool

wear monitoring.

Li et al (2013) analyzed tool wear phenomena in the milling of tool

steel AISI H13 and superalloy Inconel 718. The author developed an optical

tool inspection system its provides an online evaluation of tool flank wear

during the milling process. The author has indicated the nose wear may act as

a necessary supplement to the current tool failure criteria which mainly

consider exclusively flank wear.

Dahu et al (2013) reviewed over the research on tool wear

characteristics in the machining of nickel based superalloys and also analyzed

62

the experimental, theoretical and numerical investigations in the field of

cutting process. Finally, the existing research limitations and the latest ideas

were presented. They found that tool wear control was not only related to the

machining time or machining length, but also closely associated with the

geometrical parameters of the tool (rake angle, flank angle, edge angle, etc.),

the cutting parameters (cutting speed, feed rate, depth of cut) and the physical

and chemical properties of workpiece material.

2.7 SURFACE TOPOGRAPHY

The quality of a machined surface is becoming important to satisfy

the increasing demands of component performance and reliability. Machined

parts used in military, aerospace, and automotive industries are subjected to

high stresses, temperatures, and hostile environments. The dynamic loading

and design capabilities of machined components are limited by the fatigue

strength of the material, which is commonly linked to the fatigue fractures

that always nucleate on or near the surface of the machined components.

Stress corrosion resistance is another important material property that can be

directly linked to the machined surface characteristics. When machining any

component, it is necessary to satisfy the surface technological requirements in

terms of high product accuracy, good surface finish, and a minimum of

drawbacks that may arise as a result of possible surface alterations by the

machining process. The nature of the surface layer has a strong influence on

the mechanical properties of the part. Any machined surface has two main

aspects—the first aspect is concerned with the surface texture or the

geometric irregularities of the surface, and the second one is concerned with

the surface integrity, which includes the metallurgical alterations of the

surface and surface layer, as shown in Figure 2.8. Surface texture and surface

integrity must be defined, measured, and controlled within specific limits

during any machining operation.

63

Figure 2.8 Surface technology by machining

The surface layer is the layer from the geometrical surface inward

that shows changed physical and sometimes chemical properties, as compared

with those with the material before machining. As shown in Figure 2.9, the

main parts of such a layer are defined as:

1. Adsorbed and amorphous zone of adsorbed gas, solid, or

liquid particles

2. Fibrous zone that occurs by the frictional forces between the

tool and WP

3. Compressed layer that occur due to grain size changes

Surface layer alterations occur when abusive (severe) cutting

conditions are used. Under such conditions, high temperature and excessive

plastic deformation are promoted.

Figure 2.9 Surface layer after machining, section perpendicular to the tool

Surface integrity is more difficult to assess than surface roughness.

Some of the techniques to inspect for subsurface changes are destructive to

64

the material specimen. Evaluation techniques for surface integrity include the

following:

Surface texture: Surface roughness, designation of lay, and other measures

provides superficial data on surface integrity. This type of testing is relatively

simple to perform and is always included in the evaluation of surface

integrity. Visual examination. Visual examination can reveal various surface

flaws such as cracks, craters, laps, and seams. This type of assessment is often

augmented by fluorescent and photographic techniques.

Microstructural examination: This involves standard metallographic

techniques for preparing cross sections and obtaining photomicrographs for

examination of microstructure in the surface layers compared with the

substrate.

Microhardness profile: Hardness differences near the surface can be

detected using microhardness measurement techniques such as Knoop and

Vickers . The part is sectioned, and hardness is plotted against distance below

the surface to obtain a hardness profile of the cross section.

Residual stress profile: X-ray diffraction techniques can be employed to

measure residual stresses in the surface layers of a part (Helmin&Youssef

2008). The literature survey pertaining to the work of other researchers is

indicated below.

Machined surface characteristics such as surface roughness and

form as well as the sub-surface characteristics such as residual stress, granular

plastic flow orientation and surface defects (porosity, micro-cracks, etc.) are

important in determining the functional performance of machined

components. The quality of surfaces of machined components is determined

by the surface finish and integrity obtained after machining. Surface integrity

65

is defined as the inherent or enhanced condition of a surface produced in

machining or other surface operations (Field & Kahles1971).

Surface defects also act as weak spots for crack propagation,

thereby accelerating the fatigue failure of the component in service. It is

therefore, clear that control of the machining process to produce components

of acceptable integrity is essential. Machined components for aerospace

applications are subjected to rigorous surface analysis to detect surface

damages that will be detrimental to the highly expensive machined

components (Ezugwu&Bonney 2003).

Deformation of feed marks occurs as a result of plastic flow of

material during the cutting process. Plastic flow of material on machined

surfaces results in higher surface roughness values and higher residual stress

levels (Zhou et al 2003).

Metal removal operations lead to the generation of surfaces that

contain geometric deviation (deviation from ideal geometry) and

metallurgical damage different from the bulk material. The geometrical

deviation refers to the various forms of deviations such as roundness,

straightness etc. Typical metallurgical surface damage produced during

machining include micro-cracks, micro-pits, tearing (pickup), plastic

deformation of feed marks, re-deposited materials, etc. High surface

roughness values, hence poor surface finish, decrease the fatigue life of

machined components (Novovic et al 2004).

Toh (2004) provided an in-depth understanding on the surface

texture produced by various cutter path orientations when high speed finish

inclined milling hardened steel at a workpiece inclination angle of 75ºusing

surface topography analysis and determine the best cutter path orientation

with respect to the best surface texture achieved.

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Emmanuel et al (2007) studied the Surface integrity of finished

turned Ti–6Al–4V alloy with PCD tools using conventional and high pressure

coolant supplies.

Surface integrity involves the study and control of both surface

roughness or surface topography, and surface metallurgy. Both of these

factors influences the quality of the machined surface and subsurface, and

they become extremely significant when manufacturing structural

components that have to withstand high static and dynamic stresses. For

example, when dynamic loading is a principal factor in a design, useful

strength is frequently limited by the fatigue characteristics of materials.

Fatigue failures almost always nucleate at or near the surface of a component;

similarly, stress corrosion is also a surface phenomena. Therefore, the nature

of the surface from both a topographical and a metallurgical point of view is

important in the design and manufacture of critical hardware (Helmi&

Youssef Hassan 2008).

Sun &Guo (2009) studied on milling induced surface integrity

including anisotropic surface roughness, residual stress, microstructure

alterations, microhardness and the relationship between these factors is

significantly lacking. In addition, there are many inconsistent and even

contradictory results in surface integrity. The objective of this study is to fill

in the knowledge gap and solve the impressing issues in milling Ti–6Al–4V.

2.8 FINTE ELEMENT ANALYSIS

Cutting is one of the most important and common manufacturing

processes in the industry. Simplified analytical methods have been developed

and these successful models gave useful insight into the mechanics of cutting.

In recent years, however, finite element analysis has become the main tool for

simulating chip formation processes. Several researchers analyzed the

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modelling of chip segmentation by finite element analysis. However, most of

these models are two-dimensional, which can only be used for the analysis of

orthogonal cutting. Three-dimensional analysis offers a more realistic

modelling of the process by avoiding the assumptions accompanied with 2D

modelling. It can also be used for a wider range of machining operations, such

as oblique cutting. A 3D thermo-mechanically coupled finite element model

of dry 3D machining operations has been developed by using the commercial

FEA software Deform-3D™ (Aurich& Bill 2006) .

The design of tool edge geometry influences process parameters

such as the shape of deformation zones, distributions of temperature and

stresses on the tool face, and cutting forces. These effects in turn affect the

changes in chip flow, machined surface integrity (e.g. residual stress), tool

wear resistance, and tool life (or machinability). This analyzes the cutting

process by considering the effect of machining parameter using the Finite

Element Method (FEM) simulation. It is aimed to provide a fundamental

understanding of the process variables and mechanics, necessary for the

optimization of tool edge design(Yung et al2004). The continuous

development of more and more powerful computers and numerical methods

and their ever-widening application in manufacturing, phenomena in metal

machining, such as cutting force, temperature, and even progressive tool wear

are gradually studied using numerical methods mainly including Finite

Differential Method (FDM) and Finite Element Method (FEM).

Ceretti et al (2000) set up two three-dimensional FEM reference

models to study three-dimensional cutting operations: one model for

orthogonal cutting, one for oblique cutting. This FEM code is based on an

implicit lagrangian computational routine, the finite element mesh is linked to

the workpiece and follows its deformation. To simulate the chip formation a

remeshing procedure is performed very frequently, so that the

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workpiecemeshis frequently updated and modified to follow the tool progress.

This technique makes possible to simulate the chip separation from the

workpiece without any arbitrary predefinition.

Yung-Chang et al (2004)analyzed the cutting process by

considering the effect of tool edge preparation (hone edge and chamfer edge)

using the FEM simulation. It is aimed to provide a fundamental understanding

of the process variables and mechanics, necessary for the optimization of tool

edge designs.

Meng Liu et al (2004) studied an experimental investigation was

conducted to clarify the effect of the nose radius of CBN tool on the residual

stress distribution in hard turning of bearing steel. And the effect of tool wear

on the residual stress distribution in the case of different tool nose radius was

also discussed.

Chou & Song (2004) investigated tool nose radius effects in finish

hardened AISI 52100 steels turning. Surface finish, tool wear, cutting forces,

and, particularly, white layer (phase transformation structures) were evaluated

at different machining conditions. Results showed 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.

Numerical methods like the FEM are in principle able to the

instationary heat transfer problem which occurs in a dry machining process.

Nevertheless the heat source and its heat partition to the work-piece have to

be determined for such an approach, which is the central scientific task. In

recent approaches the heat source was calculated based on experimental

results. Pabst for example used a calorimetric approach by analyzing the

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temperature distribution inside the workpiece in dry milling(Aurich et al

2006).

Jawahir& Wang (2007) presented a summary of developments in

modeling and optimization of machining processes, focusing on turning and

milling operations. With a brief analysis of past research on predictive

modeling, the authors presented the analytical, numerical and empirical

modeling efforts for 2D and 3D chip formation covering the development of a

universal slip-line model, a comprehensive finite element model, and

integrated hybrid models. This included a newly developed equivalent tool

face (ET) model and new tool-life relationships developed for machining with

complex grooved tools. A performance-based machining optimization method

developed for predicting optimum cutting conditions and cutting tool

selection was also presented.

Rai&Xirouchakis (2008) presented an overview of a

comprehensive FEMbased milling process plan verification model and

associated tools, which by considering the effects of fixturing, operation

sequence, tool path and cutting parameters simulates the milling process in a

transient 3D virtual environment and predicts the part thin wall deflections

and elastic–plastic deformations during machining. The prediction accuracy

of the model was validated experimentally and the obtained numerical and

experimental results were found in good agreement.

Attanasio et al (2008) focused on the 3D numerical prediction of

tool wear in metal cutting operations. In particular, an analytical model, able

to take into account the diffusive wear mechanism, was implemented through

a specific subroutine. Furthermore, an advanced approach to model heat

transfer phenomena at the tool–chip interface was included in the numerical

simulation. The adopted simulation strategy gave the possibility to properly

evaluate the tool wear. The 3D FEM results were compared with some

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experimental data obtained turning AISI 1045 steel using uncoated WCtool; a

good agreement was found out.

Arrazola et al (2008) remarked the importance of tool–chip

interface effects in the cutting process, and to present a new mechanistic

approach to model friction effects over the rake surface applying a variable

friction coefficient which takes into account cutting edge effects. The main

differences observed between FEM results and experimental ones are the feed

force and the tool–chip contact length. FEM of chip formation has proved

great sensitivity to tool/chip friction coefficient. This parameter cannot be

adequately identified through conventional tests, because thermal and

mechanical loadings during these tests are far from those encountered during

machining.

Ozel et al (2008) presented investigations on hard turning with

variable edge design PcBN inserts. Turning of hardened AISI 4340 steel with

uniform and variable edge design PcBN inserts is conducted, forces and tool

wear are measured. 3D finite element modelling was utilized to predict chip

formation, forces, temperatures and tool wear on uniform and variable edge

micro-geometry tools. Predicted forces and tool wear contours are compared

with experiments. The temperature distributions and tool wear contours

demonstrate the advantages of variable edge micro-geometry design.

Wan et al (2008) developed a systematic procedure to simulate the

peripheral milling process of thin-walled workpiece. The procedure integrates

the cutting force module consisting of calculating the instantaneous uncut

chip thickness, calibrating the instantaneous cutting force coefficients and the

cutting process module consisting of calculating the cutting configuration and

static form errors. It can be used to check the process reasonability and to

optimize the process parameters for high precision milling. The regeneration

mechanism in flexible static end milling was investigated both theoretically

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and numerically. Comparisons of the cutting forces and form errors obtained

numerically and experimentally confirmed the validity of the simulation

procedure.

Gonzalo et al (2009) presented a mechanistic model needed to

obtain specific cutting coefficients needed to predict the milling forces. The

experimental work was substituted for virtual experiments, carried out using a

finite element method model of the cutting process. This methodology had

been validated for end milling operations in AISI 4340 steel.

RajaKountanya et al (2009) addressed utilizing both cutting

experiments and FEM simulations.Experiments were conducted by varying

the tool edge geometryand process parameters. The resulting chip

morphology as wellas cutting forces was studied. FEM simulations, which

essentiallysimulate the shear instability and catastrophic slip in the chip

duringdeformation, if at all it occurs, were performed for a subset of

theexperimental conditions and the resulting chip morphologies

werecompared with the experiments.

Due to the difficulty of measuring the temperature in the cutting

zones,the finite element calculation is often presented as an alternative to the

experimental methods to find the temperature field in the chip and the tool.

However, the thermo-mechanical behavior of materials at high strain

rates,coupled with friction phenomena,complicates the description of physical

interactions in one model.Forexample,the material flow stress dependence on

temperature,strain and strain rate plays a significant role in the mechanism of

chip segmentation and adiabatic shearing (Childs 2006a,).For their part,the

frictional parameters affect drastically the cutting forces and temperature

(Childs 2006b, Arrazola et al 2010).

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In general, the mechanical process energy that is converted to heat

flows into the workpiece, tool, chip and the environment. Due to the complex

phenomena which are related to the chip formation process analytical

methods, which can be found for example in (Pabst et al 2010), mostly

exclude local effects of the chip formation process and are limited to the two

dimensional orthogonal cutting process in the majority of cases. The

temperature in the chip, workpiece, environment and tool and the

corresponding heat partition. The FE chip formation simulation is able to

calculate the local heat generation caused by plastic deformation and friction

during the chip formation. In addition it is able to account for complex tool

geometries which are used in machining practice.

2.9 SUMMARY OF THE LITERATURE REVIEW

The summary of the literature review about surface roughness (Ra),

cutting force (F), vibration amplitude (Am), Temperature rise (Tr), Tool wear

(Tw), surface topography (Stp) and finite element analysis (FEA) is listed

below :

1. The prediction of Ra,F,Am,Tr and Tw for various material

like EN-31 steel , EN24 grade steel , AISI H13 steel , AISI

52100 bearing steel ,Titanium Alloy Ti-6Al-4V , Inconel 718

,AL 6351 –T6,composite Al/SiCp MMC, and Aluminium

alloys (AA6061-T6) etc in various end milling processes have

been attempted by various researchers.

2. Empirical models and optimal cutting parameter have been

developed for the prediction of Ra, F, Am, Tr and Tw by

several researchers.

3. Statistical techniques like factorial design, Taguchi technique,

RSM has been employed by a number of researchers for

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developing mathematical models and to study the effect of

process parameters on Ra,F,Am,Tr and Tw for various

material.

4. The important end milling process parameters that influence

Ra,F,Am,Tr and Tw are cutting tool properties (Tool

material,Tool geometry), machining parameters (cutting

speed,cutting feed rate,depth of cut,cooling fluid,process

kinematics), workpiece material (workpiece dimension,

workpiece hardness) and cutting phenomena (cutting force

variation,chip formation,friction in the cutting zone and

accelerations) for CNC end milling process .

5. Artificial neural networks have been effectively used by

various researchers for developing neural network models for

prediction of Ra,F,Am,Tr and Tw. Optimization techniques like

genetic algorithm, simulated annealing and other intelligent

techniques have been applied by researchers for getting

optimized results of process parameters to get the desired

Ra,F,Am,Tr and Twin various end milling processes.

6. Most researchers have observed significant surface integrity

including anisotropic surface roughness, residual stress,

microstructure alterations and microhardness .

7. Machined surface characteristics such as surface roughness

and form as well as the sub-surface characteristics such as

residual stress, granular plastic flow orientation and surface

defects (porosity, micro-cracks, etc.) are important in

determining the functional performance of machined

components.

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8. The Finite Element Analysis for various material like alloy

steel, and aluminum alloy etc in various machining processes

have been attempted by numerous researchers.

9. FEM simulations will be helpful also for machines and tool

makers, they will help to select the right material, the best tool

geometry and the process parameters in order to optimize tool

design and working processes saving time and money.

From the literature it can be inferred that

1. Numerous works have been carried out on Ra,F,Am,Tr and

Tw in CNC end milling process for various Aluminum alloy

as it has wide applications but no work has been reported for

predicting Ra,F,Am,Tr and Tw in Al7075-T6 aluminum alloy.

This Aluminum alloy has been extensively used for highly

stressed structural parts. This Aluminum alloy has been

extensively used for in Aircraft fittings, gears and shafts, fuse

parts, meter shafts and gears, missile parts, regulating valve

parts, worm gears, keys, aircraft, aerospace and defense

applications. Hence prediction of Ra,F,Am,Tr and Tw for this

grade is also vital.

2. The empirical models and regression models were developed

for CNC end milling process. There were no models available

for predicting Ra,F,Am,Tr and Tw in CNC end milling of

Al7075-T6 aluminum alloy.

3. A strategy to devise the Ra,F,Am,Tr and Tw in AL7075-T6

aluminum alloys which has so far not attracted much

breakthrough in research. By formulating a mathematical

model, it becomes feasible to evaluate the effects of process

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parameters and it also enables the selection of process

parameters after scrutinizing the main and interaction effects

of the process parameters in order to accomplish the preferred

surface roughness.

4. The model for predicting Ra,F,Am,Tr and Tw has been

evolved by most researchers based on machining parameters.

But a holistic real model can be developed only by

considering both Tool geometry and machining parameters.

The present study focuses on the influence of the radial rake

angle, nose radius, cutting feed rate and axial depth of cut

during machining Ra,F,Am,Tr and Tw.

5. No mathematical models were available relating the process

parameters with Ra,F,Am,Tr and Tw for the above grade in

CNC end milling process.Most of the researchers have used

either CCD or Taguchi design for conducting experiments.

6. The works were focused on the development of regression

models for Ra,F,Am,Tr and Tw but very few literatures were

available on development of neural network model for

prediction of Ra,F,Am,Tr and Tw which is more accurate than

the regression models.

7. Applications of intelligent optimization techniques like GA,

SA and PSO to optimize process parameters to minimize

Ra,F,Am,Tr and Tw were little work is available .

8. Most researchers have used any one optimization technique

such as GA or SA etc. to find the optimum process parameters

but it is necessary to find out the algorithm which will yield

the accurate result.

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9. Most of the work focused on studying the surface integrity of

steel alloy, and aluminum alloy, but no literature was available

with respect to the study of surface topography in Al7075

aluminum alloy in CNC end milling process. Hence it

becomes necessary to understand the changes in surface

topography of this grade due to end milling before

recommending it for any application.

10. A strategy to devise the finite element method in AL7075-T6

aluminum alloys which has so far not attracted much

breakthrough in research. By formulating a mathematical

model, it becomes feasible to evaluate the effects of process

parameters and it also enables the selection of process

parameters after scrutinizing the main effects of the process

parameters in order to accomplish the preferred

stress,strain,temperature,velocity and cutting force by using

FEA.

Hence, in the present work both regression and ANN models were

developed to predict Ra,F,Am,Tr and Tw in CNC end milling of Al7075-T6

aluminum alloy. A comparison has been made between them to identify the

best model to predict Ra,F,Am,Tr and Tw. Using regression model,

optimization of process parameters was carried out by applying optimization

techniques like GA, SA and PSO. The results obtained from the three

techniques were compared and the best algorithm that can be used to get

accurate result was identified.

In the current work, surface topography studies were carried out on

the specimens to study the effects of process parameters like radial rake angle

, nose radius of cutting tool geometry, cutting speed,Cutting feed rate and

axial depth of cut when they are kept at the minimum and maximum levels.

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The study is concentrated on the Absorption , Alloy depletion ,Cracks ,

Craters, Heat affected zone, Inclusions, Intergranular attack, Laps, folds,

seams,Pits, Redeposited metal and remelted metal in the surface topography

of Al7075 aluminum alloy in CNC end milling process.

In the present work regression models were developed to predict

stress, strain, temperature, velocity and cutting force in Al7075-T6 aluminum

alloy using the regression model studied the main effects of the process

parameters on stress, strain, temperature, velocity and cutting force in

Al7075-T6 aluminum alloy by using FEA.