ANN MODELING OF WIRE EDM AND OPTIMIZATION OF CUTTING PARAMETERS BY GA A DISSERTATION Submitted in partial fulfillment of the requirements for the award of the degree of MASTER OF TECHNOLOGY in MECHANICAL ENGINEERING (With Specialization in Production & Industrial Systems Engineering) By AMANUEL TESGERA BASHA DEPARTMENT OF MECHANICAL AND INDUSTRIAL ENGINEERING INDIAN INSTITUTE OF TECHNOLOGY ROORKEE ROORKEE - 247 667 (INDIA) JUNE, 2005
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ANN MODELING OF WIRE EDM AND OPTIMIZATION OF CUTTING PARAMETERS BY GA
A DISSERTATION
Submitted in partial fulfillment of the requirements for the award of the degree
of MASTER OF TECHNOLOGY
in MECHANICAL ENGINEERING
(With Specialization in Production & Industrial Systems Engineering)
By
AMANUEL TESGERA BASHA
DEPARTMENT OF MECHANICAL AND INDUSTRIAL ENGINEERING INDIAN INSTITUTE OF TECHNOLOGY ROORKEE
ROORKEE - 247 667 (INDIA) JUNE, 2005
CANDIDATE'S DECLARATION
I hereby declare that the work which is being presented in the report entitled "ANN MODELING
OF WIRE EDM AND OPTIMIZATION OF CUTTING PARAMETERS BY GA" in partial
fulfillment of the requirement for the award of the degree of Master of Technology in Mechanical
and Industrial Engineering with specialization in Production and Industrial systems engineering,
submitted in the Department of Mechanical and Industrial Engineering, Indian Institute of
Technology, Roorkee is an authentic record of my own work carried out from August 2004 to
June 2005, under the guidance of Dr. H.S. Shan, Professor and Dr. Navneet Arora, Assistant
Professor, Mechanical and Industrial Engineering Department, IIT—Roorkee.
The matter embodied in this dissertation has not been submitted by me for the award of any other
This is to certify that the above statement made by candidate is correct to the best of my
knowledge.
Date: 22~VA . 6S
Dr. H.S. Shan
Place: Professor, MIED
Dr. Navneet Arrora
Assist. Prof., MIED
Acknowledgement
I express my deep and sincere sense of gratitude from the core of my heart to Dr. H.S Shan,
Professor, Mechanical and Industrial Engineering Department, Indian Institute of Technology,
Rookee, for his inspiring and painstaking supervision, encouragement and invaluable help during
the course of this thesis work without which this work would not have been possible. I am
grateful for the long hours he spends in discussing and explaining minute details of the work. It
has been a wonderful association which I cherish. I consider my self privileged to have worked
under his supervision and guidance.
I am grateful to my co-guide Dr Navneet Aroar, Assistant Professor, Mechanical and Industrial
engineering department, Indian Institute of Technology Roorkee, Roorkee (India), for his
suggestions and constant encouragement.
The services of the staff of Machine Tool Laboratory, Mechanical and Industrial Engineering
department are acknowledged with sincere thanks. I am particularly thankful to Mr. Jasbir Singh,
for providing technical assistance during the experimental work.
I would also like to thank Dr. Pradeep Kumar, Professor, Mechanical and Industrial Engineering
Department, Indian Institute of Technology, Roorkee, for providing facilities to carry out the
experiments.
Last but not least, I would like to express my gratitude to my Parents for their kind blessing and
for providing moral support and encouragement throughout my life. Grateful acknowledgements
are also due to all my teachers and friends whose timely help has gone a long way in my studies.
AMANUEL TESGERA BASHA
11
Abstract
Wire electrical discharge machining (WEDM) technology has been widely used in conductive
material machining especially when intricate shapes and profiles have to be cut. Manufacturers
and users of this process always want to achieve higher machining productivity with a desired
accuracy and surface finish. The WEDM process's performance, in terms of surface fmish and machining productivity is however affected by; many factors such as applied machining voltage, ignition pulse current, pulse duration, time between two pulses, servo-speed variation, servo-
control reference voltage, wire speed, wire tension, conductivity of dielectric and injection
pressure for dielectric. The material of the work piece and its height also influence the process. If
the setting of one of the above parameters changes, it affects 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 skilled operator with a state-of-the-art WEDM
machine 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 i.e., model the
process through suitable mathematical techniques. However, the complex and stochastic nature of
the WEDM process has made it difficult to establish a conclusive analytical model; therefore, an
empirical method can be adopted. The present study is amid at exploiting the strong capabilities
of both ANN and GA, which are suitable for solving manufacturing problems that are amenable
for modeling using traditional methods.
A feed-forward back-propagation neural network based on Taguchi L18 experimental design is
developed to model the machining process. GA is then employed to find the optimal operating
conditions so that the productivity of wire EDM is improved for a given surface finish
requirement. The set of Pareto-optimal solutions is searched for the processing of titanium alloy.
The model was tested with experimental data and good correlation was obtained between the
expected and experimental results.
iii
Table of contents
Title Page no.
CANDIDATE'S DECLARATION .................................................................... i ACKNOWLEDGEMENTS ................................................................................ii ABSTRACT...................................................................................................iii TABLEOF CONTENTS ................................................................................. iv LISTOF FIGURES .......................................................................................vii LISTOF TABLES ......................................................................................... ix
1.1 Nontraditional processes defined ....................................................................2
1.2 Why Nontraditional Processes are Important....................... ..............................2
1.3 Classification of Nontraditional Processes by Type of Energy Used ..........................2 3 1.4 Thermal Energy Processes- Overview .............................................................
Chapter 7 Summary and Conclusions ...................................................................37 Scope for future research .....................................................................38
1 (b) EDM Close-up view of gap, showing discharge and metal removal .....................4 2 Schematic of wire EDM set up ...............................................................6 3 Definition of kerf and over cut in electric discharge wire cutting ........................7 4 Complicated shapes produced by wire EDM ..............................................11
5 Schematic diagram of a neuron and a sample of pulse train ..........................:.45
6 General symbol of neuron .....................................................................46
7 (a) Bipolar continuous activation functions of a neuron .....................................48 7 (b) Unipolar continuous activation functions of a neuron ....................................48 8 A standard artificial neuron ..................................................................48 9 Configuration and terminology of a multi-layered neural network ....................50
10 r
Neural Network Training Flow Chart ......................................................18
11 Configuration of the neural network ....................................................... 22
12 Sum of square error vs number of iterations in the training process ...................26
13 (a) Surface show the relationship of Gap Voltage with cutting rate (CR) .................27 13 (b) Surface show the relationship of Gap Voltage with surface roughness (SR) .........27 14 (a) Surfaces show the relationship of Ton with cutting rate (CR) .........................28 14 (b) Surfaces show the relationship of Ton with surface roughness (SR) .................28 15 (a) Surfaces show the relationship of Toff with cutting rate (CR) .........................28 15 (b) Surfaces show the relationship of Toff with surface roughness (SR) .................28 16 (a) Surfaces show the relationship of Ws with cutting rate (CR) .........................29 16 (b) Surfaces show the relationship of Ws with surface roughness (SR) ..................29 17 The basic structure of EA ...................................................................56 18 Structure of a single population evolutionary algorithm ..............................57
Today's manufacturing . industry is facing challenges from advanced difficult-to-machine
materials (i.e. tough super alloys, ceramics, and composites), stringent design requirements (i.e.
complex shapes, high precision, and high surface quality), and machining costs. In order to cope
up with these challenges, it has become necessary to change to more sophisticated tools of
manufacturing.
This need for more sophisticated tools has resulted in the creation of a new, unique family of
manufacturing processes known as nontraditional manufacturing processes. Generally speaking,
non-traditional processes differ from conventional processes either on account of utilizing
energy in novel ways or by applying forms of energy directly for the purpose of manufacturing.
Wire Electrical Discharge Machining (WEDM), one of the widely accepted non-traditional
material removal processes, has certain unique advantages as compared to other prevalent
nontraditional cutting technologies including laser cutting, plasma cutting and water jet cutting.
The most attractive advantages of this process are long cutting edge (maximal cutting edge >
500 mm), a small cutting kerf (minimal kerf < 0.05 mm), a small cutting taper and a
homogeneous surface. Due to these inherent advantages, it offers an effective and economical
alternative to present large-scale machining techniques. The realization of a methodology which
can optimize the productivity and surface quality requirement of this process is of great
significance to promote the process in to the growingly demanding tool manufacturing industry.
In this thesis work, an attempt is made to model and optimize the wire-EDM process parameters
using the combination of Artificial Neural Network (ANN) and Genetic Algorithm (GA).
In the first chapter a brief introduction to non-traditional machining processes is presented.
Electrical Discharge Machining (EDM) process as one of the thermal energy processes is
discussed in a detailed manner. The process overview based on the widely accepted principle of
thermal conduction and some highlights of its applications are also given. In the second chapter,
an explanation of wire-EDM process is given along with the similarity and difference it has with
the die sinking EDM. History of wire EDM process, process equipment and its applications are
1
discussed in detail for better understanding of the process. In chapter three, the review of
literature is made in order to understand what has been done so far in the modeling and
optimization of wire EDM process. There are several choices to be made when implementing
neural networks to solve a problem. These choices involve the selection of the training and
testing data, the network architecture, the training method, the data scaling method, and the
error goal. Chapter four is devoted to this part of discussion. The main section of chapter five
focuses on modeling of wire EDM process by using multi-layered back propagation neural
network. The experimental details together with the network architecture developed for this
purpose and the results of training and testing by applying the experimental data to the network
is given in this chapter. Chapter six is devoted to the optimization of wire EDM process. The
use of Genetic algorithm to solve constrained optimization problem is explored. The importance
of finding pareto-optimal points and how to find them from the predicted data is also given. The
final part of the thesis gives the conclusions drawn from the results and indicates the future
research direction in WEDM modeling and optimization.
1.1 Nontraditional Processes Defined
A group of processes that remove excess material by various techniques involving mechanical,
thermal, electrical, or chemical energy (or combinations of these energies) but do not use a
sharp cutting tool in the conventional sense. Developed since World War II in response to new
and unusual machining requirements that could not be satisfied by conventional methods.
1.2 Why Nontraditional Processes are Important
• Need to machine newly developed metals and non-metals with special properties that
make them difficult or impossible to machine by conventional methods.
• Need for unusual and/or complex part geometries that cannot easily be accomplished by
conventional machining.
• Need to avoid surface damage that often accompanies conventional machining.
1.3 Classification of Nontraditional Processes by the Type of Energy Used
• Mechanical - erosion of work material by a high velocity stream of abrasives or fluid (or
both) is the typical form of mechanical action
2
• Electrical - electrochemical energy to remove material (reverse of electroplating)
• Thermal — thermal energy usually applied to small portion of work surface, causing that
portion to be removed by fusion and/or vaporization
• Chemical — chemical etchants selectively remove material from portions of workpart,
while other portions are protected by a mask.
1.4 Thermal Energy Processes- Overview
Very high local temperatures are involved; material is removed by fusion or vaporization.
Physical and metallurgical damage to the new work surface is common in this case. In some
cases, resulting surface finish is so poor that subsequent processing is required.
1.4.1 Thermal Energy Processes
• Electric discharge machining
• Wire electrical discharge cutting
• Electron beam machining
+ Laser beam machining
• Plasma arc machining
• Conventional thermal cutting processes
1.5 Electrical Discharge Processes
EDM is a non-traditional manufacturing process that uses electric spark discharges to machine
electrically conducting materials. This process is typically used for materials such as tool and
die-steels, ceramics, etc., which are hard to machine using a more traditional approach. During
the process, a voltage is applied between two electrodes, the tool and the workpiece, closely
placed inside a liquid dielectric medium. When electrodes are very close to each other (gap
distance 0.05 mm), an electric spark discharge occurs between them forming a plasma channel
between the cathode and the anode. Fig. 1 shows a close-up of the machining region. The spark
generates enough heat to melt and even vaporize some of the workpiece material. As the spark
collapses, some of the molten and vaporized workpiece material is removed from the rest of the
workpiece and is carried away by the dielectric. Discharge duration is controlled by the process
parameters used and can be anywhere from a few microseconds to hundreds of microseconds.
3
I Gap
T
-- Overcut
(a)
Although quantity of material removed per discharge is miniscule, a large number of discharges
occurring over time result in removal of the desired amount of material. As material is removed
from the workpiece the tool slowly moves towards the workpiece surface (aided by servo-
control mechanism) so that a constant gap between the two can be maintained. The liquid
dielectric serves two purposes. It helps to keep the expanding plasma channel confined to a
small diameter so that the intensity of the heat flux is very high over a small surface area of the
electrodes. This ensures that melting, and even vaporization, can occur. The other use of the
dielectric is to flush some of the particles that gather in the gap between the electrodes. EDM
processes can be broadly classified into two categories, die-sinking EDM where the tool shape
complements the final desired shape of the workpiece, and wire-EDM where the discharge takes
place between a thin wire and the workpiece. The wire in wire-EDM applications acts almost
like an electrical saw.
(b)
Tool feed
4
Too[
Ionized fluid
Metal removed
from cavity "I
4
wear
Discharge
—Flow of dielectric fluid
—Cavity created I by discharge
Recast metal
Figure 1- Electric discharge machining (EDM): (a) overall setup, and (b) close-up view of gap,
showing discharge and metal removal.
1.5.1 Work material in EDM
• Only electrically conducting work materials
• Hardness and strength of the work material are not factors in EDM
• Material removal rate is related to melting point of work material
CI
1.5.2 Complex nature of the EDM material removal process
EDM involves the complex interaction of many physical phenomena. The electric spark
between the anode and the cathode generates a large amount of heat over a small area of the
workpiece. A portion of this heat is conducted through the cathode, a fraction is conducted
through the anode, and the rest is dissipated by the dielectric. The duration of the spark is of the
order of microseconds and during this time a plasma channel is formed between the tool and the
workpiece. Electrons and ions travel through this plasma channel. The plasma channel induces a
large amount of pressure on the workpiece surface as well. This pressure holds back the molten
material in its place. As the plasma starts forming, it displaces the dielectric fluid and a shock
wave passes through the fluid. As soon as the spark duration time is over and the spark collapses,
the dielectric gushes back to fill the void. This sudden removal of pressure results in a violent
ejection of the molten and vaporized material from the workpiece surface [1,2]. Ejected molten
particles quickly solidify as they come in contact with the colder fluid and are eventually
flushed out by the dielectric. Small craters are formed at locations where material has been
removed. Multiple craters overlap each other and the machined surface that is finally produced
consists of numerous overlapping craters. Although molten material ejection is not the only
means of material removal in EDM it is, however, the dominant mode of material removal in
case of metals [2]. In the machining of ceramics which have much higher melting and boiling
points, material spalling is the mechanism for material removal [2]. During machining the local
temperature in the workpiece gets close to the vaporization temperature of the material [1,2].
Thus, phase transformation from solid to liquid as well as liquid to vapor occurs during the
heating cycle. Part of the transformed material is removed but the rest re-solidifies on the
surface of the workpiece. This re-solidified layer is usually called the white layer, as it is not
easily etchable. EDM processes carried out in hydrocarbon dielectrics lead to the partial
breakdown of dielectrics and this further leads to some diffusion of carbon.
1.5.3 EDM Applications.
• Tooling for many mechanical processes: molds for plastic injection molding, extrusion
dies, wire drawing dies, forging and heading dies, and sheet metal stamping dies
• Production parts: delicate parts not rigid enough to withstand conventional cutting forces,
hole drilling where hole axis is at an acute angle to surface, and machining of hard and
exotic metals.
hi
Chapter 2 Wire EDM process
The WEDM process differs from the conventional EDM process in that a small wire is engaged
as the -tool electrode. The wire unwinding from a wire supply wheel is continuously fed through
the workpiece by the wire traction rollers and taken by a collection spool. The workpiece
mounted on the clamp frame. is almost never submerged in the dielectric medium that is
delivered at the, gap between the wire and workpiece via a hose or flushed through the sparking
area coaxially with the wire. The wire-workpiece gap usually ranges from 0.025 to 0.05 mm and
is constantly maintained by a computer-controlled (CNC) positioning system. This positioning
system is also responsible for controlling the movement of the wire to achieve the desired
complex two- and three-dimensional (2- and 3-D) shapes for the workpiece.
_ T\ Wire supply spool
Wire electrode
Dielectric 'fluid flow
Cutting .path
••.::. Wire take-up spool
Feed motion axes
Fig. 2 schematic of wire EDM set up
0
'ire diameter.
Overcut Figure 3- Definition of kerf and over cut in electric discharge wire cutting
2.1 History
WEDM was first introduced to the manufacturing industry in the late 1960s. The development
of the process was the result of seeking a technique to replace the machined electrode used in
EDM. In 1974, D.H. Dulebohn applied the optical-line follower system to automatically control
the shape of the component to be machined by the WEDM process [1]. By 1975, its popularity
was rapidly increasing, as the process and its capabilities were better understood by the industry
[2]. It was only towards the end of the 1970s, when computer numerical control (CNC) system
was initiated into WEDM that brought about a major evolution of the machining process. As a
result, the broad capabilities of the WEDM process were extensively exploited for any through-
hole machining owing to the wire, which has to pass through the part to be machined
2.2 EDM vs WEDM
While the material removal mechanisms of EDM and WEDM are similar, their functional
characteristics are not identical. WEDM uses a thin wire continuously feeding through the
workpiece by a microprocessor, which enables parts of complex shapes to be machined with
exceptional high accuracy. A varying degree of taper ranging froml5 ° for a 100 mm thick to 30
7 4
° for a 400 mm thick workpiece can also be obtained on the cut surface. The microprocessor
also constantly maintains the gap between the wire and the workpiece, which varies from 0.025
to 0.05 mm [2]. WEDM eliminates the need for elaborate pre-shaped electrodes, which are
commonly required in EDM to perform the roughing and finishing operations. In the case of
WEDM, the wire has to make several machining passes along the profile to be machined to
attain the required dimensional accuracy and surface finish (SF) quality. The typical WEDM
cutting rates (CRs) are 300 mm2/min for a 50 mm thick D2 tool. steel and 750 mm2/min for a
150 mm thick aluminium [2], and SF quality is as fine as 0.12-0.25µRa. In addition, WEDM
uses deionized water instead of hydrocarbon oil as the dielectric fluid and contains it within the
sparking zone. The deionized water is not suitable for conventional EDM as it causes rapid
electrode wear, but its low viscosity and rapid cooling rate make it ideal for WEDM [2].
2.3 Wire-EDM equipment
A wire-EDM machine consists of four sub-systems: the positioning system, the wire drive
system, the power supply, and the dielectric system. All the four subsystems have distinct
differences from conventional EDM.
2.3.1 Positioning system
Wire-EDM positioning systems usually consist of a CNC two-axis table and, in some cases, an
additional multi-axis wire-positioning system. The most unique feature of the CNC system is
that it must operate in adaptive control mode to always insure the consistency of the gap
between the wire and work piece. If the wire should come in contact with the work piece or if a
small piece of material bridges the gap and causes a short circuit, the positioning system must
sense this condition and back up along the programmed path to reestablish the proper cutting
conditions.
2.3.2 Wire drive system
The function of the wire drive system is to continuously deliver fresh wire under constant
tension to the work area. The need for constant wire tension is important to avoid such problems
as taper, machining streaks, wire breaks, and vibration marks.
As the wire passes through the work piece, it is guided by a set of sapphire or diamond guides.
Before being collected by the take-up spool, it passes through a series of tensioning rollers.
Many wire-EDM systems use a massive granite slab as the machine base to further guarantee
wire accuracy and stability.
Automatic wire threading is a recently introduced feature that boosts productivity. It
automatically re-threads the wire after breakage and enables a longer round after one pass
through the work piece and it is discarded.
2.3.3 Power supply
The most pronounced differences between the power supplies used for wire-EDM and
conventional EDM are the frequency of the pulses used and the current. To produce the
smoothest surface finish possible, pulse frequencies as high as 1 MHz may be used with wire-
EDM. Such a high frequency ensures that each spark removes as little material as possible, thus
reducing the size of EDM crater.
Because the diameter of the wire used is so small, its current —currying capability is limited.
Because of this limitation, wire-EDM power supplies are rarely built to deliver more than 20
amp of current.
2.3.4 Dielectric system
De-ionized water is the dielectric used for the wire-EDM process. De-ionized water is used for
four reasons: low viscosity, high cooling rate, high material removal rate and absence of fire
hazard.
The small cutting gap used with wire-EDM mandates that a low-viscosity dielectric be used to
ensure adequate flushing. Water meets this criterion. Water can also remove heat from the
cutting area much more efficiently than conventional dielectric oils. More efficient cooling
results in extremely thin recast layers.
Very high specific material removal rates can be achieved when using water as dielectric;
however, the wear rate on the tool (wire) is also high. Because the wire is not reused, the high
tool-wear rate is of no consequence. This explains however why water is not commonly used
with conventional EDM.
Finally, because of the slow processing speeds of wire-EDM, many users run their most time —
consuming jobs overnight or over the weekend unattended. With conventional EDM, the use of
7
flammable dielectric oils presents a fire hazard. When using water for the dielectric, the fire
hazard problem is eliminated.
Rather than submerge the entire part into de-ionized water, local delivery is most often used.
Some systems deliver the dielectric fluid via a hose directed at the cut interface. The most
efficient method of dielectric delivery (with respect to flushing) is to provide a stream of de-
ionized water coaxial with the wire.
2.4 Wire-EDM process parameters
The linear cutting rate for wire-EDM is approximately 38-115rmn/hr in 25 mm thick steel or
approximately 20mm/hr in 76 mm steel. The linear speed is dependent upon the thickness of the
material but not upon the shape of the cut. The linear cutting rate is the same whether a straight
cut or complex curves are being generated.
The speed of the wire passing through the work piece can vary from 8-40 mm/sec depending
upon cutting conditions.
2.5 Wire-EDM process capabilities
Wire-EDM is a specialized process that is capable of machining electrically conductive work
pieces to produce fine finishes, extremely high accuracies and cut edges that have a smooth,
matte finish.
The matte finish is a result of the thousands of microscopic pits remaining from the spark
erosion. When applied to punch-and-die application, the oil-retaining quality of these micro pits
has been known to increase the die life. Surface finishes ranging from 0.12 to 0.25µm are
routinely obtained, and by utilizing a second "finish pass", finishes as good as 0.05 - 0.12 µm
are possible. Many wire-EDM machines are available with a positioning resolution of 0.001mm
and can routinely obtain accuracies off 0.007mm [2].
Advantages • No electrode fabrication required • No cutting forces • Unmanned machining • Die costs reduced by 30 — 70 % • Cuts hardened materials • Intricate shapes can be cut with same ease as that for straight cut. • Very small kerf width
10
Disadvantages • High capital cost • Recast layer • - Electrolysis can occur in some materials • Slow cutting rates • Not applicable to very large workpieces
2.6 WEDM applications
• Ideal for stamping die components since kerf is so narrow, it is often possible to
fabricate punch and die in a single cut.
• Other tools and parts with intricate outline shapes, such as lathe form tools, extrusion
dies, flat templates and almost any complicated shapes (Fig.4).
Fig.4 Complicated shapes produced by wire EDM
2.6.1 Modern tooling applications
WEDM has been gaining wide acceptance in the machining of various materials used in modern
tooling applications. Several authors [3,4] have . investigated the machining performance of
WEDM in the wafering of silicon and machining of compacting dies made of sintered carbide..
The feasibility of using cylindrical WEDM for dressing a rotating metal bond diamond wheel
used for the precisionform grinding of ceramics has also been studied [5]. The results show that
the WEDM process is capable of generating precise and intricate profiles with small corner radii
but a high wear rate is observed on the diamond wheel during the first grinding pass. Such an
11
initial high wheel wear rate is due to the over-protruding diamond grains, which do not bond
strongly to the wheel after the WEDM process [6]. The WEDM of permanent NdFeB and `soft'
MnZn ferrite magnetic materials used in miniature systems, which requires small magnetic parts,
was studied by comparing it with the laser-cutting process [7]. It was found that the WEDM
process yields better dimensional accuracy and SF quality but has a slow CR, 5.5 mm/min for
NdFeB and 0.17 mm/min for MnZn ferrite. A study was also done to investigate the machining
performance of micro-WEDM used to machine a high aspect ratio meso-scale part using a
variety of metals including stainless steel, nitronic austentic stainless, beryllium copper and
titanium [8].
2.6.2. Advanced ceramic materials
The WEDM process has also been evolved as one of the most promising alternatives for the
machining of the advanced ceramics. Sanchez et al. [9] provided a literature survey on the EDM
of advanced ceramics, which have been commonly machined by diamond grinding and lapping.
In the same paper, they studied the feasibility of machining boron carbide (B4C) and silicon
infiltrated silicon carbide (SiC) using EDM and WEDM. Cheng et al. [10] also evaluated the
possibility 'of machining ZrB2 based materials using EDM and WEDM, whereas Matsuo and
Oshima [11] examined the effects of conductive carbide content, namely niobiumcarbide (NbC)
and titaniumcarbide (TiC), on the CR and surface roughness of zirconia ceramics (Zr02) during
WEDM. Lok and Lee [12] have successfully WEDMed sialon 501 and aluminium oxide--
titaniumcarbide (A1203—TiC). However, they realized that the MRR is very low as compared to
the cutting of metals such as alloy steel SKD-11 and the surface roughness is generally inferior
to the one obtained with the EDM process. Dauw et al. [13] explained that the MRR and surface
roughness are not only dependent on the machining parameters but also on the material of the
part. An innovative method of overcoming the technological limitation of the EDM and WEDM
processes requiring the electrical resistivity of the material with threshold values of
approximately 100 (1/cm [14] or 300 a /cm [15] has recently been explored. There are different
grades of engineering ceramics, which Konig et al. [ 14] classified as non-conductor, natural-
conductor
and conductor, which is a result of doping nonconductors with conductive elements.
Mohri et al. [ 16] brought a new perspective to the traditional EDM phenomenon by using an
assisting electrode to facilitate the sparking of highly electrical-resistive ceramics. Both the
EDM and WEDM processes have been successfully tested diffusing conductive particles from
12
assisting electrodes onto the surface of sialon ceramics assisting the feeding of electrode through
the insulating material. The same technique has also been experimented on other types of
insulating ceramic materials including oxide ceramics such as Zr02 and A1203, which have very
limiting electrical conductive properties [17].
2.6.3. Modern composite materials
Among the different material removal processes, WEDM is considered as an effective and
economical tool in the machining of modern composite materials. Several comparative studies
[18, 19] have been made between WEDM and laser cutting in the processing of metal matrix
composites (MMC), carbon fibre and reinforced liquid crystal polymer composites. These
studies showed that WEDM yields better cutting edge quality and has better control of the
process parameters with fewer workpiece surface damages. However, it has a slower MRR for
all the tested composite materials. Gadalla and Tsai [20] compared WEDM with conventional
diamond sawing and discovered that it produces a roughness and hardness that is comparable to
a low speed diamond saw but with a higher MRR. Yan et al. [21] surveyed the various
machining processes performed on the MMC and experimented with the machining of
A1203/6061Al composite using rotary EDM coupled with a disk-like electrode. Other studies
[22, 23] have been conducted on the WEDM of A1203 particulate reinforced composites
investigating the effect of the process parameters on the WEDM performance measures. It was
found that the process parameters have little influence on the surface roughness but have an
adverse effect on CR. -
13
Chapter 3 Literature review
Wire EDM manufacturers and users always want to achieve higher machining productivity with
a desired accuracy and surface finish. Performance of the WEDM process, however, is affected
by many factors (workpiece material, wire material, dielectric medium, adjustable parameters,
etc.) and a single parameter change will influence the process in a complex way. As surface
finish and cutting. speed are the most important parameters in manufacturing, investigations
have been carried out by several researchers [24-27] for improving the surface finish and cutting
speed of WEDM process. However, Because of the many variables and the complex and
stochastic nature of the process [28], achieving the optimal performance, even for a highly
skilled operator with a state-of-the-art WEDM machine 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 (i.e., model the process through suitable mathematical techniques),
and then determine the optimal parameters for a given set of conditions.
Investigation into the influences of machining input parameters on the performance of EDM and
WEDM have been reported widely [24-41] and several attempts have been made to model the
process.
3.1 Process modeling
Traditionally, the selection of the most favorable process parameters was based on experience or
handbook values, which produced inconsistent machining performance. However, the
optimization of parameters now relies on process analysis to identify the effect of operating
variables on achieving the desired machining characteristics. The modeling of the WEDM
process by means of mathematical techniques has also been applied to effectively relate the
large number of process variables to the different performance of the process. Spedding and
Wang [42] developed the modeling techniques using the response surface methodology and
artificial neural network technology to predict the process performance such as MR, SQ and
surface waviness within a reasonable large range of input factor levels. Liu and Esterling [43]
proposed a solid modeling method, which can precisely represent the geometry cut by the
14
WEDM process, whereas Hsue et al. [44] developed a model to estimate the MRR during
geometrical cutting by considering wire deflection with transformed exponential trajectory of
the wire centre. Spur and Scho"nbeck [451 designed a theoretical model studying the influence
of the workpiece material and the pulse-type properties on the WEDM of a workpiece with an
anodic polarity. Han et al. [46] developed a simulation system, which accurately reproduces the
discharge phenomena of WEDM. The system also applies an adaptive control, which
automatically generates an optimal machining condition for high precision WEDM.
3.2 Process optimization.
Many different types of problem-solving quality tools have been used to investigate the
significant factors and its inter-relationships with the other variables in obtaining an optimal
WEDM CR. Konda et al. [29] classified the various potential factors affecting the WEDM
performance measures into five major categories namely the different properties of the
workpiece material and dielectric fluid, machine characteristics, adjustable machining
parameters,. and component geometry. In addition, they applied the design of experiments (DOE)
technique to study and optimize the possible effects of variables during process design and
development, and validated the experimental results using noise-to-signal (S/N) ratio analysis.
Tarng et al [30] employed a neural network system with the application of a simulated
annealing algorithm for solving the multi-response optimization problem. It was found that the
machining parameters such as the pulse on/off duration, peak current, open circuit voltage,
servo reference voltage, electrical capacitance and table speed are the critical parameters for the
estimation of the CR and SF. Huang et at [31] argued that several published works are
concerned mostly with the optimization of parameters for the roughing cutting operations and
proposed a practical strategy of process planning from roughing to finishing operations. The
experimental results showed that the pulse on-time and the distance between the wire periphery
and the workpiece surface affect the CR and SF significantly. The effects of the discharge
energy on the CR and SF of a MMC have also been investigated.
15
Chapter 4 Neural Network Implementation Issues
Due to its ability to address complex and nonlinear problems (problems whose solutions have
not been explicitly formulated), the widely accepted method, artificial neural network (ANN) is
chosen to model the complex behavior between input and output in the WEDM process. It has
been used extensively in many fields such as forecasting, pattern recognition, robotics,
parameter selection, process modeling, monitoring, and controlling etc. It is originally based on
the human thoughts of receiving and transferring the information in making decision. A simple
model of ANN consists of an input layer, a hidden layer and an output layer. With sets of input—
output patterns stored in input and output layers, the hidden layer interconnects different
strength of information from the input to the output layers, through so-called weights. The
weights are adjusted in the learning process in which all the patterns of input—output are
presented in the learning phase repeatedly. There are many learning algorithms available and the
most popular and successful learning algorithm used to train multilayer network is the back
propagation scheme. Any output point can be obtained after this learning phase, and good
results can be achieved. In Appendix — 1, a brief review of the fundamentals of multilayered
feed-forward neural networks is provided. For more details, reference may be made to Freeman
and Skapura [47] and Vemuri [48].
Neural networks are highly flexible modeling tools with an ability to learn the mapping between
input variables and output feature spaces. Therefore, neural networks are considered in this
work to model the wire-EDM process with multi-dimensional input and output spaces.
There are several choices to be made when implementing neural networks to solve a problem.
These choices involve the selection of the training and testing data, the network architecture, the
training method, the data scaling method, and the error goal. Since over 90% of all neural
network implementations use back propagation trained multi-layer perceptrons, an attempt has
been made to discuss and implement it in this work.
4.1 Overview of Neural Network Training Methodology
Figure 10 shows the methodology to follow when training a neural network. First we must
collect or generate the data to be used for training and testing the neural network. In the present
16
case experimental data generated on wire EDM has been used. Once this data is collected, it
must be divided into a training set (Table 1) and a test set (Table 2). The training set should
cover the input space or should at least cover the space in which the network will be expected to
operate. If there is not training data for certain conditions, the output of the network should not
be trusted for those inputs. The division of the data into the training and test sets is somewhat of
an art and somewhat of a trial and error procedure. We want to keep the training set small so
that training is fast, but we also want to exercise the input space well which may require a large
training set.
Once the training set is selected, we must choose the neural network architecture. There are two
lines of thought here. Some designers choose to start with a fairly large network that is sure to
have enough degrees of freedom (neurons in the hidden layer) to train to the desired error goal;
then, once the network is trained, they try to shrink the network until the smallest network that
trains remains. Other designers choose to start with a small network and grow it until the
network trains and its error goal is met. We will use the second method which involves initially
selecting fairly small network architecture.
After the network architecture is chosen, the weights and biases are initialized and the network
is trained. The network may not reach the error goal due to one or more of the following reasons.
1. The training gets stuck in local minima.
2. The network does not have enough degrees of freedom to fit the desired
input/output model.
3. There is not enough information in the training data to perform the desired
mapping. J
17
Collect Data
Select Training and Test Sets
Select Neural Network Architecture
r
Initialize Weights
Change Weights N SSE Goal or
Increase NN Size Met?
Y Run Test Set
Reselect Training Set or Collect More Data
SSE Goal N Met?
Y Done
Fig.10 Neural Network Training Flow Chart
In case one, the weights and biases are reinitialized and training is restarted. In case two,
additional hidden nodes or layers are added, and network training is restarted. Case three is
usually not apparent unless all else fails. When attempting to train a neural network, you want
to end up with the smallest network architecture that trains correctly (meets the error goal); if
not, you may have over fitting. Over fitting is described in greater detail in Section 4.1.4.
Once the smallest network that trains to the desired error goal is found, it must be tested with the
test data set. The test data set should also cover the operating region well. Testing the network
• involves presenting the test set to the network and calculating the error. If the error goal is met,
training is complete. If the error goal is not met, there could be two causes:
1. Poor generalization due to an incomplete training set.
2. Over fitting due to an incomplete training set or too many degrees of freedom in the
network architecture.
The cause of the poor test performance is rarely apparent without using cross validation
checking which will be discussed in Section 4.1.6. If an incomplete test set is causing the poor
performance, the test patterns that have high error levels should be added to the training set, a
new test set should be chosen, and the network should be retrained. If there is not enough data
left for training and testing, data may need to be collected again or be regenerated.
4.1.1 Training and Test Data Selection
Neural network training data should be selected to cover the entire region where the network is
expected to operate. Usually a large amount of data is collected and a subset of that data is used
to train the network. Another subset of that data is then used as test data to verify the correct
generalization of the network. If the network does not generalize well on several data points,
that data is added to the training data and the network is retrained. This process continues until
the performance of the network is acceptable.
The training data should bound the operating region because a neural network's performance
cannot be relied upon outside the operating region. This ability is called a network's
extrapolation ability.
4.1.2 Scaling Input Vectors
Training data is scaled for two major reasons. First, input data is usually scaled to give each
input equal importance and to prevent premature saturation of sigmoidal activation functions.
Secondly, output or target data is scaled if the output activation functions have a limited range
and the unscaled targets do not match that range.
There are two popular types of input scaling: linear scaling and z-score scaling. Linearly
scaling transforms the data into a new range which is usually 0.1 to 0.9. 1
4.1.3 Initializing Weights
As mentioned above, the initial weights should be selected to be small random values in order to
prevent premature saturation of the sigmoidal activation functions. The most common method
is to use the random number generator and pass it the number of inputs plus 1 and the number of
hidden nodes for the first hidden layer weight matrix W1 and pass it the number of outputs and
hidden nodes plus 1 for the output weight matrix W2. One is added to the number of inputs in
19
W 1 and to hidden in W2 to account for the bias. To make the weights somewhat smaller, the
resulting random weight matrix is multiplied by 0.5.
4.1.4 Over fitting
Several parameters affect the ability of a neural network to over fit the data. Over fitting is
apparent when a networks error level for the training data is significantly better than the error
level of the test data. When this happens, the data learned the peculiarities of the training data,
such as noise, rather than the underlying functional relationship of the model to be learned.
Over fitting can be reduced by:
1. Limiting the number of free parameters (neurons) to the minimum necessary.
2. Increasing the training set size so that the noise averages itself out.
3. Stopping training before over fitting occurs.
4.1.5 Neural Network Noise
As discussed above, when there is noise in the training data, a method to calculate the RMS
error goal needs to be used. If there is significant noise in the data, increasing the number of
patterns in the training set can reduce the amount of over fitting.
4.1.6 Stopping Criteria and Cross Validation Training
The last method of reducing the chance of over fitting is cross validation training. Cross
validation, training uses the principle of checking for over fitting during training. This
methodology uses two sets of data during training. One set is used for training and the other is
used to check for over fitting. Since over fitting occurs when the neural network models the
training data better than it would other data, checking data is used during training to test for this
over learning behavior.
At each training epoch, the RMS error is calculated for both the test set and the checking set. If
the network has more than enough neurons to model the data, there will be a point during
training when the training error continues to decrease but the checking error levels off and
begins to increase.
In summary, there are four methods to reduce the chance of over fitting:
1. Limiting the number of free parameters.
20
2. Training to a realistic error goal.
3. Increase the training set size.
4. Use cross validation training to identify when over fitting occurs.
These methods can be used independently or used together to reduce the chance of over fitting.
21
Chapter 5 ANN modeling of WEDM
5.1 Neural network model
Commercial software MATLAB Version 6.3 is used for coding the Neural Network program.
The stopping criteria used in the current study was set at 2000 maximum epoch number, and the
characteristics of the training set was train multiplayer. Whereas the testing set was set once the
difference between sum square error of the actual and predicted values is g x 10"3.
A feed forward neural network is adopted here to model the wire-EDM process. The feed
forward neural network is composed of many interconnected artificial neurons that are often
grouped into input, hidden and output layers (Fig 11).
f (Hp)
CD CD
C) CD
Cd
VG
1 Ton
Toff
Ws
iparator
CR
[~— SR
performances des
Hidden nodes
Fig. 11. Configuration of the neural network.
22
The fundamental equation which defines input out put relationship can be expressed as follows:
Y= f (X, W) (vi)
Where Y represents the performance parameters, such as the MRR and surface roughness; X is
a vector of the input variables to the neural network, and W is the weight matrix that is
evaluated in the network training process. f (.) represents the model of the process that is to be
built through neural network training.
The modeling phase involves the establishment of the model using multilayer feed forward
neural network architecture. The back propagation algorithm finds the optimum values of the
weights that minimize the error between the target and the calculated (network output)
performance parameters. Fig. 11. shows the network architecture of the developed model.
The following relations were used to combine the inputs of the network at the nodes of the
hidden layer and the output layer, respectively.
Hp = EVhpXh , Oq = E pq.ZP
Both outputs at the hidden (Zh = f (Hp)) and output layer (Yq =f (0k)) are calculated using
sigmoid function, mainly because of its well-known use as a transfer function for many
applications. Combining equations (vi) & (vii), the relations for the output of the network is
given by the following relation:
Y q =f (Oq) =f( pq.Zp) = J (Wpq.( EVhpXh))
Finaly, the output of the network (Yq ) was compared with the measured performance (Tq ) of
the process using a simple sum of square error (Eq) as follows:
Eq = (Yqq - T9) 2 k1
The artificial neuron evaluates the inputs and determines the strength of each one through its
weighting factor calculated by the back-propagation learning algorithm [Appendix - 1]. The
weighted inputs are summed to determine the output of the neuron using a sigmoid transfer
function. The output of the neuron is then transmitted along the weighted outgoing connections
23
to serve as an input to subsequent neurons. In this study, the neurons of the input and output
layers are used to receive the input variable of cutting parameters and to send out the output
variable of cutting performance, respectively. To properly map the input and output
relationships in the wire-EDM process with the neural network, finite discrete samples of
experimental data are required for training the neural network given in section 5.2. During the
training process, the number of neurons in the hidden layer is determined by trial-and-error
experimentation. It is found that a single hidden layer with 11 neurons can provide better
convergence in modeling the wire-EDM process. As shown in Fig. 12, the sum of square error
(SSE) between the desired and predicted outputs is almost reduced to zero after 2000 iterations
during the training process. Therefore, a feed forward neural network with a 5-11-2 type is
adopted here to associate the cutting parameters with the cutting performance.
5.2 Experimental details
Titanium alloy was chosen as the work material and work piece thickness was kept as 5 mm.
Brass wire of 0.25mm was used for all the experiments. Experiments were planned using a
factorial design based on Taguchi's L18 orthogonal array with 21 x 34. The machining voltage
(Va) was maintained at 80V and conductivity of dielectric (Cd) at 50 and 250 p-mho. The other
four parameters were maintained at three levels; pulse duration (Ton) at 1.1, 1.2 and 1.31.ts; time
between two pulses (Toff) at 30, 34, 38µs; gap voltage (GV) at 50, 60, 70 volts; and wire speed
(Ws) at 4, 6, 8 m/min. For testing the results, 16 experiments were conducted, on the basis of
randomly selected input parameters. For each set of parameters the workpiece was straight cut
for a length of 10 mm.
The linear cutting rate reading was noted down. Each piece was cleaned and the
surfaceroughness was measured as Ra value using profilometer. The average of six readings.
taken perpendicular to the direction of cut was chosen as the surface roughness value. The
results of the experiments given in Table 1 are based on Taguchi's method and Table 2 gives
data obtained by randomly selecting the input parameters.
24
Table 1 Training data: MRR and surface finish for experiments planned according to Taguchi's method'