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CHAPTER 1

INTRODUCTION

1.1 GENERAL

In manufacturing, the process of removing unwanted segments of

metal workpieces in the form of chips is known as machining, so as to obtain

a finished product of the desired size, shape, and surface quality. The

machining cutting process can be divided into two major groups which are (i)

cutting process with traditional machining (e.g., turning, milling, boring and

grinding) and (ii) cutting process with modern machining (e.g., Electrical

Discharge Machining (EDM) and Abrasive Water-Jet (AWJ)). (Boothroyd

1989).

The basic element of the modern metal removal process consists of

a machine tool, a control system and the cutting tool. A tremendous

revolution in metal cutting practice takes place as machining is typically

carried out using dedicated, specially designed machining systems for mass

production. A flexible, agile, or reconfigurable machining system based on

Computerized Numeric Control machine tools, the development of open

architecture computer based controls and evolution of new tooling materials

have greatly impacted metal cutting practice. Milling is the most frequent

metal cutting operations in which the material is removed by advancing

workpiece against a rotating multiple point tool. End milling, a type of

peripheral milling operation, is used for profiling and slotting operation.

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The objectives of this research work is to provide a physical

understanding of CNC end milling operation by high speed steel end mill

cutter applied to machine aluminum (Al 7075) alloy. Tool geometries, cutting

speeds, feed rates and axial depth of cut must be selected to provide reliable

and efficient operation. Machinability test and process simulation can be used

to choose optimum conditions. The effect of machining parameters such as

such as radial rake angle ( ) , nose radius (R) of cutting tool ,cutting speed

(Vc), feed rate (fz), and axial depth of cut (ap) on the machining performance

are analyzed and investigated.

1.2 END MILLING

An end milling process is a multipoint, interrupted cutting, in which

the contact between cutting edge and the work piece is not continuous and the

uncut chip thickness varies with spindle rotation. Milling is widely used in the

industry and milled surfaces are largely used to mate with others in die,

aerospace, automobile, biomedical products and machinery design as well as

in manufacturing industries. End milling produces profiles, slots, engraves,

contours, and pockets in various components. Milling operations have

become more productive and efficient over time through the advent of

computer aided numerical control milling.

Milling is the removal of metal by feeding the work past a rotating

multitoothed cutter. During this operation the material removal rate is

enhanced as the cutter rotates at a high cutting speed. The surface quality is

also improved due to the multicutting edges of the milling cutter. 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.

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The high impact loads at entry as well as fluctuating cutting force

make the milling process subject to vibration and chatter. This aspect has

great influence on the design of milling cutters. This process is used to

generate flat surfaces or curved profile and many other intricate shapes with

great accuracy and delivers a very good surface finish. End milling is

extensively employed in molds, dies, automotive and aerospace industries. In

particular, this process is widely used in the aerospace industry due to the

accuracy and complexity involved in the finished dimensions. Competency

and productivity of the milling operations have improved due to the

introduction of computer aided numerical control milling.

1.2.1 End Milling Cutter

Several types of milling cutters are used for different operations.

End mill (profile relief) cutters are cutter with teeth on the circumferential

surface on one end. The shank may be straight or tapered. The teeth may be

helical or parallel to the axis of the rotation. A spiral end mill is an end mill

with moderate helix angle. The End mill cutters generate two workpiece

surfaces at the same time; cutting edges are located on both the end face and

the periphery of the cutter body. They are usually used in operations such as,

facing, profiling, slotting, shoulder, slabbing, plunging and are the most

versatile milling tools. They are produced in solid High-Speed Steel (HSS),

cobalt enriched HSS-Co, sintered tungsten carbide (WC), ceramic,

Polycrystalline Diamond (PCD)/ Polycrystalline Cubic Boron Nitride (PCBN)

brazed or vein construction, inserted blade, and indexable insert design.

Two major problems often encountered with the end mill cutters

which are related to rigidity are springback and chatter. The springback is

caused by insufficient stiffness and the results in the deflection or deformation

of the cutter due to cutting forces. Excessive springback (or elastic recovery)

of the end mill cutter results in a scratch marks during tool retraction. Chatter

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can occur either during the feeding or retracting motions. The resonant

frequencies and chatter resistance of a cutter strongly depends on the cutter

length to diameter (or overhang) ratio; overhang ratios greater than 4 or 5 are

especially susceptible to chatter.

In this present work the end mill cutters made of HSS with five

different radial rake angles (40, 80, 120, 160 and 200) and nose radius (0.4mm,

0.6mm, 0.8mm, 1mm, 1.2mm) have been used to conduct the experiments.

The effect of radial rake angles and nose radius on the machining

performance has been analyzed and investigated. The workpiece material

selected is aluminum alloy (Al 7075).

1.2.2 High Speed Steel

The HSS is a self –hardening steels have a high degree of red

hardness and high abrasion resistance along with a comparable degree of

shock resistance. Their primary application is used as a material for cutting

tools. The other applications are used as a material for extrusion dies and

blanking punches and dies. Their major alloying elements are tungsten,

molybdenum, chromium and vanadium, and in superior grade cobalt is added.

High speed steels are more difficult to machine and grind because of high

carbon and alloy content. The high speed steels are grouped into two divisions

as tungsten high speed steels (T-type steels) and molybdenum high speed

steels (M-type steels). The T-types are less tough than the M - type, but are

heat treated more easily.

1.3 AL7075 ALUMINUM ALLOY

Al7075 is a high strength material commonly used for highly

stressed structural components. It has been widely used in the missile parts,

bicycle frames ,all terrain vehicle sprockets, rock climbing equipment, bicycle

components, and hang glider airframes. It also used in transport applications,

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including marine, automotive and aviation, due to their high strength-to-

density ratio. More recently, Al7075 has been gaining popularity in the mold

making and rapid prototyping industry due to its favorable material properties

(Jamal Sheikh Ahmad &Twomey 2007). Al7075 was selected for this study

as it we can be used in a wide range of applications as well as its increased

usage in the mold making and rapid prototyping industry

The chemical composition of AL7075-T6 aluminum alloy is

shown in Table 1.1

Table 1.1Chemical composition of Al 7075 - T6

Element Al Zn Mg Cu Fe Cr Mn Ti Si

Composition%

87.1- 91.4

5.1- 6.1

2.1- 2.9

1.2- 2

Max0.5

0.18- 0.28

Max0.3

Max0.2

Max0.4

1.4 ECONOMIC CONSIDERATIONS

Economic considerations are obviously important in designing an

end milling operation. There is generally more than one approach for

machining a particular part; each approach will have an associated cost and

level of part quality. The initial method of producing a new part, including

machine tools, cutting tool materials and geometries, speeds and feeds, and

coolant, is generally determined from previous experience with similar parts,

hand book recommendations, catalog data, or rules of thumb. These sources

provide plausible starting starts, but rarely yield the most efficient approach.

In high-volume operations, changes are continually made and experience with

the specific part is accumulated. This can be a tedious process and often

results in comparatively inefficient practices being used much in the

production run. A more efficient methodology to predict and optimize

machining practices would be desirable to reduce the time required to identify

the best process more systematically (Hernandez et al 2006).

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1.5 PLAN OF RESEARCH

The research work presented in the subsequent chapters of this

thesis, was undertaken to investigate the effect of process characteristics of

CNC end milling process on surface finish, cutting force, vibration amplitude,

temperature rise, tool wear, surface topography of machined AL7075-T6 and

finite element analysis of machining for AL7075-T6 aluminum alloy .

Figure 1.1 Sequence of research work

Experiment No: 7 RSM- CCD (3factors, 5levels, and 20 runs)

Finite element analysis

Measuring Response for Expt No 7 (force, temperature, stress, strain etc)

Development of Regression Models for response

Main effect Analysis

Experiment no: 6 Surface topography

Material characteristics for CNC end milling process

Material best suited for product like Arerospace, defence, tool & die and RPT components

Al7075 –T6 Aluminium Alloy

Experiment No: 1 -5 RSM-CCD (5factors, 5levels, and 32 runs) CNC end milling Process Parameters

1.Radial angle of cutting tool ( )2.Nose radius (R) 3.Cutting speed (Vc)4.CuttingFeed rate (fz)5.Axial depth of cut (ap)

Measuring Response for Expt No 1-5 [surface roughness (Ra), cutting force (F), vibration amplitude (Am) temperature rise (Tr) and tool wear (Tw) for HSS

end milling]

Development of Regression Models for Ra, F, Am, Tr, Tw

Validations of Models-Comparison of Regression Models & ANN Models

Optimization of Process Parameters Using PSO,SA& GA

Machining characteristics for

Al7075-T6

Design of Experiments

Conclusions

Problem Identification &Material Selection

Literature Survey

Trial Runs for Finding Limits of Process Variables

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The various aspects of the research work are presented in

Figure 1.1. The process variables selected for investigation of surface finish,

cutting force, vibration amplitude, temperature rise , tool wear and surface

topography, are the radial rake angle( ), nose radius(R), cutting feed rate(fz)

and axial depth of cut(ap) for HSS end mill process. A final investigation of

finite element analysis of machining using Deform software for Al7075-T6

aluminum alloy is done.

Advanced statistical technique - Design of Experiments (DOE) was

used for deciding the experimental runs for this investigation. The central

composite rotatable design was used for conducting the experiments to

develop mathematical models for predicting the responses.

The optimization of process parameters to minimize surface finish,

cutting force, vibration amplitude, temperature rise and tool wear was done by

using intelligent optimization techniques like Genetic Algorithm (GA),

Simulated Annealing (SA) and Particle Swarm Optimization (PSO). A source

code was developed in MATLAB for optimization using GA, SA and PSO.

Before going into the details of investigations carried out in the

research work, an introduction to all aspects is described below under the

appropriate headings.

1.5.1 PredictionofSurface Roughness

The quality of the surface is significantly important for evaluating

the productivity of machine tools, and mechanical parts. A proper cutting

condition is extremely important because this factor determines the surface

quality of manufactured parts. The surface roughness value is a result of the

tool wear. When tool wear increase, the surface roughness also increases. The

determination of the sufficient cutting parameters is a very important process

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obtained by means of both minimum surface roughness values and long tool

life. The poor surface finish due to the chatter marks, excessive tool wear,

reduces dimensional accuracy, and tool damage. Machine-tool operators often

select conservative cutting conditions to avoid chatter, thus, decreasing

productivity.

Surface roughness is an important parameter in milling which

decides how the work piece components interact with its assembled parts.

Obviously rough surface will wear more and have a higher coefficient of

friction than smooth surface; hence surface roughness is a good predictor of

quality product (Benardos et al 2003). The demands for high quality of

product relay on surface roughness urge the industrial automation to focus its

attention on the surface finish of the product. Though surface roughness is a

prominent parameter, it is expensive to control it since the manufacturing cost

will increase exponentially with a decrease in surface roughness. An effective

model to predict the surface roughness becomes essential to ensure the

desired quality in end milling.

1.5.2 Prediction of Cutting Force

The prediction of cutting forces in milling processes is really

extremely important to effectively design the machining process, including

the choice of the optimal process parameters, the tooling and the fixture. A

correct estimation of such force could avoid quality problem related to the

tool deflection, chatter or fixture thereby improving also the productivity. The

excessive cutting forces are undesirable in milling, which results in a poor

surface finish, inaccurate dimensions and increases tool wear. Measured

cutting forces are used to compare the machinability of materials and for real

time control in monitoring a cutting process, tool wear and failure. Estimation

of cutting forces has been used to determine machine power requirements,

bearing loads and to design fixtures. The most actual techniques to improve

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the quality and efficiency of milling is arising the possibility to change

continuously the cutting parameters in order to avoid chatter and optimize the

production.

Cutting forces generated in metal cutting operations cause

deflections of the part, tool or machine structure and supply energy to the

machining system which results in excessive temperatures or unstable

vibrations. The excessive cutting forces are undesirable in milling, which

results in a poor surface finish, inaccurate dimensions and increases tool wear

(Kuljanic&Sortino 2005). Measured cutting forces are used to compare the

machinability of materials and for real time control in monitoring a cutting

process and tool wear and failure. Estimation of cutting forces has been used

to determine machine power requirements and bearing loads and to design

fixtures .An effective model to predict the cutting force becomes essential to

ensure the stability in end milling process.

1.5.3 Prediction of Vibration Amplitude

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

drill or a turning tool. In turning and drilling, the tool 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. The demand on high productivity leads to increased

material removal per unit time and higher spindle speeds, increased feed rate,

and greater depth of cut. However, at certain combinations of machining

parameters; process instabilities and vibrations can occur which result in

decreased accuracy, poor surface finish, reduced tool life time, a decrease of

the metal removal and in the worst case spindle failure and even a reduction

of life of a machine tool.

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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 of great

concern as it helps to increase quality of machining.

1.5.4 Prediction of Temperature Rise

The power consumed in metal cutting is largely converted into heat

near the cutting edge of the tool, and many of the economic and technical

problems of machining are caused directly or indirectly by this heating action.

The cost of machining is very strongly dependent on the rate of metal

removal, and costs may be reduced by increasing the cutting speed and/or the

feed rate, but there are limits to the speed and feed above which the life of the

tool is shortened excessively. With these higher melting point metals and

alloys, the tools are heated to high temperatures as metal removal rate

increases and, above certain critical speeds, the tools tend to collapse after a

very short cutting time under the influence of stress and temperature (Edward

Trent & Paul 2000). It is, therefore, important to understand the factors which

influence the generation of heat, the flow of heat, and the temperature

distribution in the tool and the work material near the tool edge.

The heat energy produces high temperature in the deformation

zones and surrounding regions of the chip, tool and work piece. This

temperature rise propagates tool wear, devastates the work piece quality and

increases tooling cost. The temperature rise affects the work material

properties, as the moderate temperature rise induces residual stress in the

machined surface, while the high temperature rise may leave a hardened layer

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on the machined surface. The cutting tool which possesses high hardness at

room temperature is unable to retain the hardness at high temperature during

milling. Temperature rise on the rake face of the tool has a strong influence

on tool life. As temperature in this area increases, the tool softens and wears

more rapidly, the tool material diffuses into chips and leads to tool failure and

the workpiece material adheres to the tools which causes rapid wear. The

softening of the tool at high temperature rise propagates wear rapidly,

therefore determining the critical value of the temperature becomes important

for the reduction of tool wear. Temperature rise on the relief face of the tool

affects the surface finish and metallurgical state of the machined surface.

1.5.5 Prediction of Tool Wear

The cutting tool wear reduces the surface integrity of the product in

the end milling process, hence it is essential to know tool wear level to inhibit

any deterioration in machining quality. During machining cutting tools are

subjected to rubbing process, where the friction between cutting tool and

workpiece materials results in progressive loss of materials in cutting tool.

Tool wear is a change of shape of the tool from its original shape resulting

from the gradual loss of tool material. This tool wear becomes an important

parameter in end milling operation. The worn tool may cause significant

degradation in the work piece quality .The consequence of the tool wear is

poor surface finish, increase in cutting force, increase in vibration of the

machine tool, increase in tool-workpiece temperature during machining,

decreases in dimensional accuracy, increases in the cost and lowering of the

production efficiency and component quality. Prediction of tool wear

becomes important to increase the maximum utilization of the tool and to

minimize the machining cost. An effective model to predict the tool wear

becomes imperative.

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1.5.6 Study on Surface Topography of Machined Specimens

Surface topography effects are assessed with regard to different tool

geometry orientations on its surface. 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 is defined as the inherent or enhanced condition of a surface

produced during machining or other surface operations .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 (Helmi&Youssef Hassan 2008). Therefore, it is

essential to study the surface topography of the machined Specimens.

1.5.7 Finite Element Models

Computer simulations based on FEA have seen increased attention

in the last two decades because they also offer the possibility to reduce the

cost of experimental research. Advancements in remeshing procedures and

damage models has brought the accuracy of FEA metal cutting simulations to

a higher level. Amongst the most popular commercial software used at

present are AbaqusTM , Deform (2D, 3D) and LS-DYNA . An accurate

simulation makes a detailed examination of physical phenomena possible.

Simulation of metal cutting allows for example to evaluate the chip forming

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process and to predict chip shapes which are dependent on several material

and process parameters. FEA based modelling and simulation of machining

processes furthermore allows to predict physical parameters such as strain,

stress, velocity, temperature and cutting force, but it can also provide

information regarding the integrity of the machined surface and cutting tools.

In order to reduce the experimental costs, FEM of machining can

be employed to qualitatively predict tool forces, stress, temperature, strain ,

strain rate and velocity fields.

1.5.8 Design of Experiments

Design of experiments is a scientific approach of planning and

conducting experiments to generate, analyze and interpret the data so that

valid conclusions can be drawn efficiently and economically (Adler et al

1975). It has been proved to be very effective for improving the process yield,

process performance and process variability.

The DOE procedure has been applied in this work to develop

mathematical models to surface roughness, vibration amplitude, cutting force,

temperature rise and tool wear. Direct and interactive effects of the process

parameters have been analyzed and presented in graphical form. This enables

the CNC end milling technologists to choose optimum process parameters to

achieve minimum surface roughness, vibration amplitude, cutting force ,

temperature rise and tool wear of Al7075-T6 aluminum.

The following methodology was adopted in this work to achieve

the above said objectives.

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1. The ranges of process parameters were identified by means of

the following methodology. The upper and lower limit of each

process variable was estimated initially through trial runs. For

instance, trial runs for varying values of cutting speed between

50 and 160m/min were conducted in order to identify the

lower limit and upper limit of cutting speed. During the trial

runs, the other variables were fixed at a constant value, i.e. at

12 º, R at 0.8 mm, fz at 0.04 mm/tooth, and ap at 2.5 mm.

Later the specimen was scrutinized on the basis of surface

roughness and the same factors form the basis for fixing the

levels.

2. Five factor five level central composite rotatable designs with

32 experimental runs for developing mathematical models to

predict, surface roughness, vibration amplitude, cutting force,

temperature rise and tool wear for HSS end mill cutter.

3. Three factor (R,Vc,fz) five level central composite rotatable

designs with 20 experimental runs for developing

mathematical models to analysis the main effect parameter for

2D FEA simulation process.

4. Three factor (Vc,fz,ap) five level central composite rotatable

designs with 20 experimental runs for developing

mathematical models to analysis the main effect parameter for

3D FEA simulation process.

5. The value of the regression coefficients gives an idea as to

what extent the control parameters affect the response

quantitatively. Less significant coefficients were eliminated by

finding p-values of the coefficients. If the p-value of the

coefficient is less than 0.05, the coefficient becomes

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significant otherwise it becomes insignificant. The final

mathematical models were developed using only the

significant coefficients.

6. The adequacies of the models were tested using analysis of

variance technique (ANOVA). As per this technique the

calculated value of F-ratio of the model developed should not

exceed the standard value of F-ratio for a desired level of

confidence (selected as 95%) and the calculated value of R-

ratio of the model developed should exceed the standard

tabulated value for the same confidence level. If these

conditions are fulfilled then the models are considered to be

adequate. The validity of the final mathematical models was

further tested by drawing scatter diagrams which compare

observed and predicted values and shows the agreement

between them in graphical form.

7. Contour plots and response surfaces were drawn using Design

expert software to study the two way interaction effects of the

process variables on responses.

1.5.9 Artificial Neural Networks

Due to the complexity of cutting-process phenomena, there is a

heavy nonlinearity in the relationships between the involved variables. For

this reason, several researchers have pointed out the shortcomings of the

statistical approaches in modeling these relationships .On the contrary, some

artificial-intelligence-based tools have proved their ability to match complex

nonlinear relationships. The most popular and deeply studied techniques in

soft computing are the artificial neural networks.

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The artificial neural network is designed to simulate the

information processing of the human brain or neural system. Neural network

consist of a basic elementary element called neuron which process the input

signal and feed it to a differentiable transfer function to generate output in a

similar way the brain neuron works. The advantage of neural network is that it

can accommodate larger input data and filter the noisy and incomplete data.

The most widely used technique is feed forward back propagation neural

network. This neural network uses network which training functions that

updates weights and bias values according to gradient decent to reduce errors

(Hazim et al 2010). The network is a multi layer network consists of an input

layer (input parameter fed), output layer (generates output response) and at

least one hidden layer (uses training function to process input to yield output).

The backward propagation network is a supervised learning

algorithm where the set of inputs and response obtained from the experiment

are provided at the training stage. The input is feed forward from the input

layer, propagates through the hidden layer where by means of training

function the output is obtained. Training plays an important role in the

accuracy of the prediction of response. The accuracy of the network was

measured by the Mean sum of Squared Error (MSE) between the measured

and predicted values. During the training the output obtained from the

network is compared with the experimental value and the error is minimized

by adjusting the weights used in the network. The newly adjusted weight and

bias value is again propagated backwards for further training. The training is

an iterative process and will stop once an acceptable error is reached.

The experimentally measured values are used to train back

propagation neural network model to predict the average surface roughness

(Ra), tool wear (Tw), cutting force (Fx, Fy&Fz), acceleration amplitude (Am)

and temperature rise (Tr) by using MATLAB R2011a software.

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1.5.10 Need for Modeling and Optimization of Machining Processes

Manufacturing includes various types of processes and today’s

machining processes are caught between the growing needs for quality, high

process safety, minimal machining costs, and short manufacturing times. In

order to meet the demands, manufacturing process setting parameters has to

be chosen in the best possible way. In today’s manufacturing environment

many large industries use highly automated and computer-controlled

machines as their strategy to adapt to the ever-changing competitive market

requirement. Due to high capital and manufacturing costs, there is an

economic need to operate these machines as efficiently as possible in order to

obtain the required pay back. The success of the machining process depends

upon the selection of appropriate process parameters. The selection of

optimum process parameters plays a significant role to ensure the quality of

product, to reduce the manufacturing cost and to increase productivity in

computer controlled manufacturing process. In the case of milling operation

the significant parameters that need to be optimized are cutting speed, radial

and axial depths of cut, feed, and number of passes.

Modeling and optimization of process parameters of any

manufacturing process are usually a difficult task where the following aspects

are required: knowledge of the manufacturing process, empirical equations to

develop realistic constraints, specification of machine capabilities,

development of an effective optimization criterion, and knowledge of

mathematical and numerical optimization techniques. A human process

planner selects proper parameters using his own experience or from the

handbooks. The performance of these processes, however, is affected by

many factors and a single parameter change will influence the process in a

complex way. Because of the many variables and the complex and stochastic

nature of the process, achieving the optimal performance, even for a highly

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skilled operator is rarely possible. An effective way to solve this problem is to

discover the relationship between the performance of the process and its

controllable input parameters by modeling the process through suitable

mathematical techniques and optimization using a suitable optimization

algorithm.

The first necessary step for process parameter optimization is to

understand the principles governing the manufacturing process by developing

an explicit mathematical model which may be mechanistic and empirical.The

model in which the functional relationship between input–output and in-

process parameters is determined analytically is called mechanistic modelling.

However, as there is a lack of adequate and acceptable mechanistic models for

manufacturing processes, the empirical models are generally used in

manufacturing processes. The modeling techniques of input–output and in-

process parameter relationships are mainly based on statistical regression,

fuzzy set theory, and artificial neural networks.

The optimization algorithms can be classified into two distinct

types:

1. Traditional optimization algorithms: These are deterministic

algorithms with specific rules for moving from one solution to

the other. These algorithms have been in use for quite some

time and have been successfully applied to many engineering

design problems. The examples of these algorithms include

non-linear programming, geometric programming, quadratic

programming, dynamic programming, etc. However, the

optimization problems related to manufacturing are usually

complex in nature and characterized by mixed continuous–

discrete variables and discontinuous and non-convex design

spaces. Hence, the traditional optimization methods fail to

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give the global optimum solution, as they are usually trapped

at the local optimum. Also these techniques are usually slow

in convergence. To overcome these problems, researchers

have proposed non-traditional methods for optimization of

process parameters of various manufacturing processes.

2. Nontraditional optimization algorithms: These algorithms are

stochastic in nature, with probabilistic transition rules. These

algorithms are comparatively new and gaining popularity due

to certain properties, which the deterministic algorithms do

not have. These methods are mainly based on biological,

molecular, or neurological phenomenon that mimics the

metaphor of natural biological evolution and/or the social

behavior of the species. To mimic the efficient behavior of these

species, various researchers have developed computational

systems that seek fast and robust solutions to complex

optimization problems. Examples of these algorithms include

Simulated Annealing (SA), Genetic Algorithm (GA), Particle

Swarm Optimization (PSO), Artificial Bee Colony (ABC),

Shuffled Frog Leaping (SFL), Harmony Search (HS), etc.

Figure1.2.(a) and (b) provides a general classification of different

input-output and in-process parameter relationship modelling and

optimization techniques in metal cutting processes, respectively (Mukherjee

& P.K. Ray,2006, NorfadzlanYusup et al. 2012). Whereas conventional

techniques attempt to provide a local optimal solution, non-conventional

techniques based on extrinsic model or objective function developed, which is

only an approximation, and attempt to provide near-optimal cutting

conditions.

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Figure 1.2 Classification of modelling (a) and optimization (b) Techniques in metal cutting process problems

The traditional methods of optimization and search do not fare well

over a broad spectrum of problem domains. Recently the focus is on to bring

out the utility and advantages of non-traditional optimization techniques, such

as genetic algorithm, simulated annealing and particle swarm optimization. In

this work the new evolutionary techniques like GA, SA and PSO were applied

for the CNC end milling optimization of process parameters to minimize

surface finish, cutting force, vibration amplitude, temperature rise and tool wear.

The results obtained from these techniques were compared and the appropriate

method that could be employed to get accurate results and presented.

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1.6 SUMMARY

End milling operation is the most common metal removing process

in automotive and valve industries in order to produce components with

complex profile. Metal cutting at high speed results in distortion of workpiece

surface finish, rapid tool wear, increase in cutting forces and acceleration

amplitude and high temperature rise at the tool-workpiece interface.

Prediction of these responses which affect the quality of machining in end

milling becomes important. This report depicts the description of the

investigations on the effect of machining parameters of the measured response

in end milling; and of the prediction and optimization of these responses in

terms of machining parameters.

In the light of above mentioned chronology of investigations,

different chapters in this report are incorporated in the following sequence:

Literature survey, Experimental designs, Study on surface finish, cutting

force, vibration amplitude, temperature rise and tool wear, which includes

development of mathematical and artificial neural network model to predict

surface finish, cutting force, vibration amplitude, temperature rise and tool

wear, optimization of process parameters to minimize surface finish, cutting

force, vibration amplitude, temperature rise and tool wear using PSO, SA and

GA. A study on the surface topography of the machined surface was also

carried out to determine the effect of process parameters on the specimens.

Finally, a focused finite element simulation of cutting processes was also

carried out to determine the effect of machining parameters on machining

behavior.The conclusions of the investigations are summed up in the last

chapter, which is followed by a list of references for different chapters.