20320130406019

International Journal of Advanced Research in Engineering RESEARCH IN ENGINEERING
INTERNATIONAL JOURNAL OF ADVANCED and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online)TECHNOLOGY (IJARET)
AND Volume 4, Issue 7, November – December (2013), © IAEME

ISSN 0976 - 6480 (Print)
ISSN 0976 - 6499 (Online)
Volume 4, Issue 7, November - December 2013, pp. 161-169
© IAEME: www.iaeme.com/ijaret.asp
Journal Impact Factor (2013): 5.8376 (Calculated by GISI)
www.jifactor.com

IJARET
©IAEME

WEDM PROCESS MODELING WITH DATA MINING TECHNIQUES
S V Subrahmanyam*,

M. M. M. Sarcar**

*Assistant Professor, Dept of Mechanical Engg, Gayatri Vidya Parishad College of Engineering,

Vizag, A.P. INDIA
**Professor and Head of the Department of Mechanical Engg., A.U.College of Engineering, Vizag,

A.P. INDIA

ABSTRACT
To satisfy the needs like Better finish, low tolerance, higher production rate,
miniaturization, complex shapes and profiles a different class of modern machining techniques,
unconventional in nature, like Wire Electrical discharge Machining (WEDM) emerged. Due to
its’ unique machining capabilities WEDM is preferred in various manufacturing industries like
Aerospace, nuclear, missile, turbine, automobile, tool and die making. There has been an
ongoing research to understand the interaction mechanism between the input parameters Vs
output measures like Cutting Speed, Surface Finish and to develop WEDM as a Automated
Process System, to avoid as far as possible the operator intervention in machining. In this paper
an attempt is made to apply Data mining technique to model the WEDM process and to make the
WEDM as Automated Process Control System and Expert System. Using the Experimental data
the models are built, trained, tested and validated. It is observed and reported here that desired
accuracy is obtained with the built models.
INDEX TERMS: C5 for WEDM, QUEST for WEDM, Classification, Data Mining for Wire
EDM, WEDM Modelling, Machine Learning,
I. INTRODUCTION
WEDM involves complex physical and chemical process including heating and cooling.
Metal erosion effect takes place in WEDM Process, when electric sparks are generated between the
work piece and a wire electrode flushed or immersed in the dielectric fluid. The WEDM machining
plays a major role in manufacturing sectors especially industries like aerospace, ordinance,
automobile and general engineering etc. This is because the WEDM process provides an effective
solution for machining hard materials with intricate shapes, which are difficult to machine through
conventional machining methods. [14,15].
161

International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 4, Issue 7, November – December (2013), © IAEME

WEDM provides high accuracy, repeatability, and a better surface finish but the tradeoff is a
very slow machining rate and machining tasks take many hours depending on the complexity of the
job. Also the cost of machining is very high in WEDM because the high initial investment and cost of
the wire–electrode tool. But if WEDM is used to cut difficult to machine materials with complex,
precise and accurate contours in low volume and greater variety, then the WEDM process
compensates slow machining rate and RI.
With Proper selection of suitable input parameter values with a pre-programmed system,
which may be termed as Expert System for WEDM, it is possible to predict machining time along
with requisite cutting speed, surface finish etc., and increases the Productivity. Improperly selected
parameters may result in serious consequences like short-circuiting of wire and wire breakage and in
turn reduces productivity. [4,5].
Even though up-to-date CNC-WEDM machines are available, the problem of selection of
cutting parameters in WEDM process is not fully solved. Several researchers are trying to develop a
method for improving the surface ﬁnish and cutting speed of WEDM process and make it into a
Expert System. So far there is no established standard method for predicting machining rate based on
the input parameters because of the complex machining mechanism of WEDM. Thus the inability to
predict efficient automated machining rate with a method has been one of the major obstacles in
developing automated process control systems and expert systems for WEDM.[6]
With WEDM’s inherent complexity it is difficult to model WEDM. From the Literature
review it is observed that a limited success is achieved in modeling WEDM using artiﬁcial neural
networks, fuzzy logic, etc. Hence to model the WEDM into a Knowledge base system, or Expert
System, there is a need to develop a set of IF..Then Rules. With data mining technique, relatively a
new technique, an attempt is made in this research to generate the simplest IF…THEN rules, with a
very high overall accuracy. The experimental data is classified and analyzed Data mining algorithms
coupled with categorizing methods. Classification is used to develop a model that maps a data item
into one of several predefined classes.
II. EXPERIMENTAL DETAILS
En31 alloy steel has been considered in the present set of research work. Brass wire of 0.25
mm diameter was used as tool electrode in the experimental set up. SPRINTCUT (AU) WITH
PULSE GENERATOR ELPULS 40A DLX CNC Wire-cut EDM machine[8] was used to conduct
the experiments. All the axes of the are servo controlled and through a control panel a
preprogrammed CNC program code can be fed into the system and can be programmed to follow a
CNC code which is fed through the control panel. The 3 sizes of the work piece considered for
experimentation on the wire-cut EDM is 5 mm x 5 mm (24 mm,36mm, 49mm thickness). A small
gap of 0.025 mm to 0.05 mm is maintained in between the wire and work-piece. The high energy
density erodes material from both the wire and work piece by local melting and vaporizing. The dielectric fluid (de-ionized water) is continuously flashed through the gap along the wire, to the
sparking area to remove the debris produced during the erosion. Nine Input process parameters such
as Pulse On time (TON), Pulse Off time (TOFF), Peak Current (IP), Spark gap Voltage Setting
(SV), Wire tension setting (WT), Wire Feed rate setting (WF), Servo Feed Setting (SF), Flushing
pressure of dielectric fluid (WP) and Material Thickness (THICK) used in this study.
[1, 4, 5, 6, 13, 14, 17, 9].

162


Table 1
Sl.N
o.
1
2
3
4
5
6
7
8
9

PARAMETERS
Pulse On time
Pulse Off time
Peak Current
Spark gap Voltage Setting
Wire tension setting
Wire Feed rate setting
Servo Feed Setting
Flushing pressure of dielectric fluid
Material Thickness

SYMBO
L
TON
TOFF
IP
SV
WT
WF
SF
WP
THICK

LEVEL
1
122
63
130
20
4
4
500
9
24

LEVEL
2
125
58
180
30
8
5
1300
12
36

LEVEL
3
128
53
230
40
12
6
2100
15
49

UNITS
µsec
µsec
Ampere
Volts
Kg-f
m/min
mm/min
Kg/cm2
mm

The range values for the input parameters have been carefully chosen based on the
experimental evaluation of one factor at a time method. The results of the experiments given in
Table 2 are based on Experiments planned using a Custom Design option of IBM SPSS software.
IBM software is statistical design software. Data mining techniques does not require following any
statistical design. The use of statistical design of experiments was chosen for the simple reason that
if the data is not indicative of the domain, then the machine leaning may not find patterns that may
be present in the domain.[2,3]
Table 2
EXPNOO
1
2
:
.
:

TON
122
122
:

15
16
17
:
:
34
35

:
122
122
122
:
:
125
125

36
37
:
:
48
49
50
:
:
71
72

122
128
:
:
128
128
125
:
:
128
128

TOFF SV
63
20
63
20
:
:
:
:
53
40
63
40
58
30
:
:
:
:
63
40
58
40
53
53
:
:
58
53
63
:
:
63
58

40
40
:
:
40
20
0
30
0
:
:
20
30

IP
130
130
:
:
180
180
230
:
:
130
130

WF
6
6
:
:
4
5
6
:
:
5
4

150
150
:
:
130
230
130
:
:
230
230

6
5
:
:
6
5
6
:
:
6
6

163

WT
12
12
:
:
4
4
8
:
:
4
8

SF
500
2100
:
:
2100
1300
1300
:
:
1300
500

THICK
36
24
:
:
49
49
24
:
:
49
24

WP
15
12
:
:
15
9
12
:
:
9
9

4
12
:
:
4
8
8
:
:
12
4

500
1300
:
:
1300
2100
500
:
:
2100
500

36
49
:
:
24
49
24
:
:
24
49

9
12
:
:
15
9
12
:
:
12
12

Ra(µ)
2.4812
3.2549
:
:
1.7728
1.7358
1.8458
:
:
2.6059
2.6648
2.7012
3.4276
:
:
3.5998
3.7742
3.8852
:
:
4.0312
4.1025


III. M E T HO D O L O G Y
Using data mining techniques the experimental results were analyzed[10]. During the
process models are constructed from the experimental data pattern with the data mining
techniques. The same models are validated with the obtained trained, tested data from the
experimental data. The system uses top down induction of decision tree method to analyze the
data. The advantage of decision tree method is that decision trees represent rules that can be easily
understood by everyone and gives the actual process knowledge to develop a knowledge base
system. A set of rules can be derived from this decision tree representing the relationship between
the various input parameters of WEDM like, Pulse on, Pulse off, Thickness of material etc. and the
output measures namely surface finish and material removal rate [1,2].
3.1 THE DATA MINING PROCESS
Data mining process has the following four steps to follow:
1. Data collection, Data Cleaning, and Data Transformation are the three steps used to gather
data, clean the data and to transform the collected data for further processing. This is known
as DATA PRE PROCESSING.
2. To find and compare the patterns in data this method was used. This is the most crucial part
of the data mining process. Neural networks, Machine Learning and statistical methods are
the different data mining algorithms that are used for this purpose. This step is known as
PATTERN SEARCH.
3. In the ANALYSIS step the output of the pattern search is analyzed and investigated to decide
to stop or to perform a revised search.
4. Finally investigated findings are further interpreted, in the INTREPRETATION step.
3.1.1. DATA PREPARATION
In the data mining process Data preparation is an essential step. The data must be
reformulated in terms that can suitable to apply the data mining algorithm. The following
classification strategy is used:
(a)

The WEDM process target output values are surface roughness “Ra” and cutting speed
“CSPEED”, Material Removal Rate MRR. The Ra and CSPEED, MRR are treated
separately. In this process the effect of the input parameters analyzed on CSPEED and then
repeated the exercise for surface roughness Ra and MRR. For the present paper only the Ra
is considered.
(b) A set of attributes/input parameters with their ranges are predefined as shown in table1.
(c ) The experiments are carried out with the input parameters and their ranges. In each
experiment a work piece of size 5x5x24(or36 or49) is removed by the Machine. The Ra
values of each piece measured by Mitutoyo surftest SJ-201P, by taking the average readings
on all over the entire cut-off length.
(d) The input parameters/attributes values in WEDM can only be changed on the machine in a
step-wise manner; hence they will be treated as categorical data. The target values are
categorized into four (2, 3, 4, 5) classes by using an incremental categorization approach.
The incremental approach is something like a common man’s language which helps in
classifying the data based on whether the output is below or above a threshold value. The
categorizations of target values were performed by the above two, three, four, and five
classes using the mean and standard deviation method. There are several such methods to
process the data in data mining.
164


(e)

In the standard deviation method the threshold values are chosen with the aid of mean and
standard deviation by applying some of the basic statistical rules to the outputs in the
training set, testing set and validation set.

3.1.2. SEARCH FOR PATTERNS
3.1.2.1. DATA MINING ALGORITHM: There is several rule induction algorithms for classification,
and C4.5, ID3, C5, CN2 and QUEST, MARS are examples of such algorithms. Some studies have
compared the prediction accuracy, complexity and training time of different classification
algorithms.
In the present study C5, QUEST (Quick Unbiased Efficient Statistical Test) algorithms
have been chosen for the following reasons: One of the main differences between these algorithms
is the method of splitting criteria that they use in selecting attributes for root and internal decision
nodes in building the decision tree. Also, another difference is that whether an algorithm can split
the node into two or more child nodes.
The C5 node builds either a decision tree or a rule set. The model works by splitting the
sample based on the filed that provides the maximum information gain at each level. The target
field must be categorical. Multiple splits into more than two subgroups are allowed.
QUEST is a relatively new binary tree-growing algorithm. It deals with split field selection
and split-point selection separately. The univariate split in QUEST performs approximately
unbiased field selection. QUEST is a binary-split decision tree a1agorithm for classification and
data mining. QUEST stands for Quick, Unbiased and Efficient Statistical Tree. The QUEST node
provides a binary classification method for building decision tree, designed to reduce the
processing time required also reducing the tendency found in classification tree methods to favor
predictors that allow more splits. Predictor fields can be ranges, but the target field must be
categorical AIl splits are binary..
Then after the 2,3,4,5 classification, the model was built using data from Table 2. Then the
data was subjected to the two data mining methods (learning methods to validate the applicability
of the rules to the pattern found in the data though the training, testing. The software provides the
option of selecting a part of the data for training and another part of data for testing and some
another part of the data for validating the suitability of algorithm, classification etc. This is in
terms of %. The following % allocation of data chosen by the author as per the standards: For eg.,
In QUEST algorithm 70% of data were used as training data and 20% and 10% for validating the
algorithm were employed as test ones and the a factor equal to 0.05 was used to split data and also
the tree was pruned at level five. Time for executing the algorithm is 1 second and in 91.67% of
cases algorithm arrival to correct answers. In case of C5 also it is 87.5%. The results are tabulated
in Table3, Table 4.
IV. RESULTS AND DISCUSSIONS
Table 3

165


Table 4

From the decision trees it is seen that TON, TOFF, IP, SV, SF, WF and Thickness of the
material are the important parameters influencing the Ra. Surface roughness increases with the
increase of TON, IP, SF and THICKNESS and decreases with increase in TOFF, SV, and WF and
WP.
Table 5
2CLSD
1_Training 2_Testing 3_Validation
QUEST

100.00%

100.00%

100.00%

C5

94.23%

100.00%

91.67%

Table 6
3CLSD
QUEST

100.00%

100.00%

91.67%

C5

100.00%

100.00%

91.67%

Table 7
4CLSD
QUEST

94.23%

83.33%

83.33%

C5

94.23%

100.00%

83.33%

166


Table 8
5CLSD
QUEST

88.46%

75.00%

75.00%

C5

92.31%

86.75%

83.33%

Table 9
2CL
SD
3CL
SD
4CL
SD
5CL
SD

QUEST
100.00%

C5
100.00%

QUEST
91.67%

C5
91.67%

QUEST
83.33%

C5
83.33%

QUEST
75.00%

C5
83.33%

From the results of data mining with four classes, it is seen that material thickness (THICK)
is influencing the material removal rate. As the thickness is increasing the Ra is increasing. The
result is obtained from both the C5 and QUEST confirms this. After the Thickness equally important
parameters which influence the Ra are TON, IP, SF, SV, WF, WP in the order written in C5
algorithm. In C5 the Order differs but the final conclusion is agreeable with QUEST.
It may be noted from the trees and also from the above tables 5, 6, 7, 8 that as the number of
classes are increasing the importance of accuracy in pattern search is increasing as result the
validation and the misclassifying the training and test data is also increasing. Hence it may be worth
noting that as the classification increase into number of classes then the categorizing methods
followed should be more math oriented and care should be taken in framing the rules for
classification. There are total 8 models (Table 9) for the 4 classes(2,3,4,5 classes) with standard
deviation method and 2 data mining machine learning algorithms. Out of 8 models 2 models yield
100% , 2 models yield 91.67% 3models yield 83.33%.Whereas for the last class one model yields
75.0%. Thus an overall basis the success rate is 7/8=85% (75% and above is considered as
threshold). Thus if such a model is developed then the trial and error methods to be adopted by the
operator will reduce and the machining time reduces and increases productivity and the can be more
compatible to CAPP and knowledgebase system. The invalidated cases in the classification are
16.33% up to 16 to 25% in case of QUEST and it is 16.33% in case of C5. It is also to be noted
from the results that data misclassifying is only to the tune of 7 to 11.54% (table3) of the cases
hence it is presumed that the model performed reasonably well on the test data misclassifying only
16.33% of the cases. If we observe the Trees generated by both the algorithms we understand what
parameters to be used to get a better output. For eg. If we take Class 2 to get a low Surface
Roughness of High Surface finish we have to choose TON values <=125.332 and corresponding
thickness 32.526mm, corresponding to this IP=230, SV=30,SF=1300, WF=4, TOFF=58 for QUEST
MODEL. Similarly for C5 Model TON values <=125.332 and corresponding thickness 32.526mm,
corresponding to this IP=180, SV=40, WF=5, TOFF=63, SF=2100.
167


V. CONCLUSION
In this paper WEDM domain was dealt as classification problem. The data mining approach
provides the important decision parameters that are useful in obtaining the desired output results
and making the WEDM process much more automation process to incorporate into CAPP. The
QUEST, C5 algorithms has been used to automatically learn rules from WEDM data. Similarly
other algorithms can also be tried with suitable categorization, classification approaches. This
approach can be applied to any manufacturing process and can significantly reduce cycle times by
increasing the level of automation and also providing insight into the process. For improved
performance and prediction accuracy Hybrid approaches in combination of artificial neural
networks and decision trees may also be employed. However there will be question when to use C5
and when to use QUEST analysis. QUEST analysis is especially useful to reduce the tendency
found in classification tree methods to favor inputs that allow more splits, that is, continuous
(numeric range) input fields or those with many categories. The QUEST node provides a binary
classification method for building decision trees, designed to reduce the processing time required
for large. All splits are binary. For C5 the model works by splitting the sample based on the field
that provides the maximum information gain at each level. The target field must be categorical.
Multiple splits into more than two subgroups are allowed.

DECISION TREES FOR C5 ALGORITHM

2CLASS

3CLASS

4CLASS

5CLASS

DECISION TREES FOR QUEST ALGORITHM

2CLASS

3CLASS

4CLASS

168

5CLASS


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