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HYBRID GREY RELATIONAL ANALYSIS-SUPPORT VECTOR MACHINE
(GR-SVM) IN PREDICTING MACHINING SURFACE ROUGHNESS
ASHANIRA BINTI MAT DERIS
UNIVERSITI TEKNOLOGI MALAYSIA
HYBRID GREY RELATIONAL ANALYSIS-SUPPORT VECTOR MACHINE
(GR-SVM) IN PREDICTING MACHINING SURFACE ROUGHNESS
ASHANIRA BINTI MAT DERIS
A thesis is submitted in fulfillment of the
Requirements for the award of the degree of
Master of Science (Computer Science)
Faculty of Computing
Universiti Teknologi Malaysia
AUGUST 2013
iii
“This thesis is special dedicated to my dear parents and lovely siblings for their
endless love, support and courage
iv
ACKNOWLEDGEMENTS
All praise to Allah, the Almighty, most Gracious and most Merciful. I would
like to express a heartfelt gratitude to my supervisors, Dr. Azlan Mohd Zain and
Assoc.Prof.Dr. Roselina Sallehuddin for their guidance, generous support, endless
advice and enormous patience throughout my research work.
My sincere appreciation goes to Ministry of Higher Education and University
Technology Malaysia for providing me financial support during the period of this
research work. I would also like to express my gratitude to all lab members of
Research Lab 3 in the Faculty of Computing for their help and support.
v
ABSTRACT
Machining process is defined as a process of material removal from a work
piece in the form of chips. This process has improved significantly over the years to
meet the field requirements. However, a major issue in the process is how to obtain
accurate results of the machining performance measurement value at optimal point of
cutting conditions. Machining performance for surface roughness has been widely
discussed by researchers but determining the optimal solution for surface roughness
remains as one of the most challenging problem due to the complexity of the
modeling process. Thus, this research proposed a hybrid model combining Grey
Relational Analysis (GRA) and Support Vector Machine (SVM) to predict surface
roughness values for end milling and abrasive water jet (AWJ) machining processes.
In the proposed hybrid Grey Relational-Support Vector Machine (GR-SVM)
prediction model, GRA acts as a feature selection method in pre-processing process
to eliminate irrelevant factors and SVM solves the regression functions in machining
problems to determine the surface roughness value. Efficiency of the proposed
prediction model was demonstrated by comparing the results of the hybrid model
with the experimental data and results of conventional SVM prediction model based
on correlation and Root Mean Square Error (RMSE) values. The results showed that
the hybrid GR-SVM prediction model presented the most accurate results due to its
ability to remove redundant features and irrelevant elements from the experimental
datasets. These results have shown that the optimal solution of machining
performance can be achieved by using the proposed hybrid GR-SVM prediction
model.
vi
ABSTRAK
Process pemesinan ditakrifkan sebagai satu proses pembuangan bahan
daripada bahan kerja dalam bentuk cip. Proses ini telah berkembang dengan pesat
sejak beberapa tahun untuk memenuhi bidang keperluan. Walau bagaimanapun isu
utama dalam proses ini ialah kaedah untuk mendapatkan keputusan yang tepat bagi
nilai pengukuran prestasi pemesinan pada titik optimum syarat pemotongan. Prestasi
pemesinan untuk kekasaran permukaan telah dibincangkan dengan meluas oleh para
penyelidik tetapi menentukan penyelesaian optimum untuk kekasaran permukaan
kekal sebagai salah satu masalah yang paling mencabar kerana kerumitan proses
model. Oleh itu kajian ini mencadangkan satu model hibrid yang menggabungkan
Analisis Hubungan Grey (GRA) dengan Sokongan Mesin Vektor (SVM) untuk
meramalkan nilai kekasaran permukaan bagi proses pemesinan pengisaran hujung
dan pelelas jet air (AWJ). Dalam model ramalan hibrid Hubungan Grey-Sokongan
Mesin Vektor (GR-SVM), GRA bertindak sebagai kaedah pemilihan ciri dalam
proses pra-pemprosesan untuk menghapuskan faktor-faktor tidak relevan sementara
SVM pula menyelesaikan fungsi regresi dalam masalah pemesinan untuk
menentukan nilai kekasaran permukaan. Kecekapan model ramalan yang
dicadangkan ditunjukkan dengan membandingkan keputusan model hibrid dengan
data eksperimen dan keputusan model ramalan SVM konvensional berdasarkan nilai-
nilai korelasi dan punca kuasa ralat kuasa dua (RMSE). Hasil kajian menunjukkan
bahawa model ramalan hibrid GR-SVM memberikan keputusan yang paling tepat
kerana kemampuannya untuk menghapuskan ciri-ciri berlebihan dan unsur-unsur
yang tidak relevan daripada set data eksperimen. Keputusan ini menunjukkan
bahawa penyelesaian prestasi pemesinan yang optimum dapat dicapai dengan
menggunakan cadangan model ramalan hibrid GR-SVM.
vii
TABLE OF CONTENTS
CHAPTER TITLE PAGE
DECLARATION ii
DEDICATION iii
ACKNOWLEDGEMENT iv
ABSTRACT v
ABSTRAK vi
TABLE OF CONTENTS vii
LIST OF TABLES xi
LIST OF FIGURES xiv
LIST OF ABBREVIATIONS xvi
LIST OF SYMBOLS xvii
1 INTRODUCTION 1
1.1 Background of the Study 1
1.2 Problem Statement 4
1.3 Aim of the Research 6
1.4 Objectives of the Research 6
viii
1.5 Scopes of the Research 7
1.6 Research Significant 7
1.7 Contribution of the Research 8
1.8 Summary 8
2 LITERATURE REVIEW 9
2.1 Modeling Theory in Machining 9
2.2 Surface Roughness 10
2.3 End Milling machining process 11
2.4 Abrasive water jet machining process 13
2.5 Hybridization of Computational Approaches in
Machining
2.6 Support Vector Machine (SVM) 14
2.6.1 SVM Modeling Theory 15
2.6.2 Kernel Function 17
2.6.3 C and Gamma Parameter 18
2.6.4 Previous Study on Application of SVM in
Machining
2.6.4.1 Previous studies on SVM in
Conventional Machining
2.6.4.2 Previous Studies on SVM in
Modern Machining
2.6.4.2 Summary of SVM Applications
in Machining
2.7 Feature Selection 32
2.8 Grey Relational Analysis (GRA) 33
2.8.1 Data Preprocessing 33
14
18
18
28
32
ix
2.8.2 Grey Relational Coefficient 34
2.8.3 Grey Relational Grade 35
2.8.4 Previous study of GRA in machining 37
2.9 Data of the Case Study 37
2.9.1 Case Study on End Milling 37
2.9.2 Case Study on AWJ 41
2.8 Summary 43
3 RESEARCH METHODOLOGY 44
3.1 Research Flow 44
3.2 Problem and Data Definition 46
3.3 Data Preprocessing 47
3.3.1 Data Normalization 47
3.3.2 Data Division 47
3.4 Conventional SVM Model Development 48
3.4.1 Selection of Kernel Function 48
3.4.2 Selection of C and Gamma Parameter 49
3.4.3 SVM Model Implementation
3.4.4 Validation of the Prediction Model
49
51
3.5 The Proposed of GR-SVM Model 52
3.6 Validation and Evaluation of Results 52
3.6 Summary 53
4 CONVENTIONAL SVM MODEL DEVELOPMENT 54
x
4.1 SVM Model Development 54
4.2 Development of SVM Prediction Model for End
Milling
4.3 Validation for SVM Models for End Milling 63
4.4 Development of SVM Prediction Model for AWJ 66
4.5 Validation for SVM Models for AWJ 69
4.6 Summary 72
5 PROPOSED HYBRID GR-SVM MODEL
DEVELOPMENT
5.1 Hybrid GR-SVM Prediction Model Development 73
5.2 Hybrid GR-SVM Prediction Model for End Milling 76
5.2.1 Hybrid GR-SVM Model for Uncoated 76
5.2.2 Hybrid GR-SVM Model for TiAlN 77
5.2.3 Hybrid GR-SVM Model for SNTR 78
5.3 Validation of Hybrid GR-SVM Model for End
Milling
5.3.1 Validation of Hybrid GR-SVM Model for
Uncoated
5.3.2 Validation of Hybrid GR-SVM Model for
TiAlN
5.3.3 Validation of Hybrid GR-SVM Model for
SNTR
5.3.4 Summary of Hybrid GR-SVM Model for
End Milling Machining
5.4 Hybrid GR-SVM Prediction for AWJ 85
5.5 Validation of Hybrid GR-SVM Model for AWJ 86
5.6 Summary 88
56
79
80
81
82
84
73
xi
6 VALIDATION AND EVALUATION 89
6.1 Validation and Evaluation for End Milling
Prediction Results
6.1.1 Validation of Hybrid GR-SVM Prediction
Results for Uncoated
6.1.2 Validation of GR-SVM Prediction Results
for TiAlN
6.1.3 Validation of GR-SVM Prediction Results
for SNTR
6.1.4 Evaluation of End Milling Prediction Result 98
6.2 Validation and Evaluation of AWJ Prediction
Results
6.2.1 Validation of Hybrid GR-SVM for AWJ
Prediction Results
6.2.2 Evaluation of Hybrid GR-SVM Prediction
Results for AWJ
6.3 Summary 104
7 CONCLUSION 105
7.1 Research Findings 105
7.3 Research Contributions 109
7.4 Recommendations for Future Works 109
7.5 Summary 110
REFERENCES 112
90
90
93
95
99
100
103
xii
LIST OF TABLES
TABLE NO. TITLE PAGE
2.1 Types of Kernel function 17
2.2 Applications of SVM for Conventional Machining 21
2.3 Applications of SVM for Modern Machining 30
2.4 Range influence factor based on GRG values 36
2.5 Chemical composition of Chemical composition and
properties of Ti-6Al- 4V (Ti-64) 38
2.6 The properties of the cutting tool 38
2.7 Setting of cutting condition values for real
machining 39
2.8 Experimental data of end-milling 40
2.9 Chemical composition of AI 7075 alloy 41
2.10 Levels process parameters and coding
implementation 41
2.11 Experimental data of AWJ 42
4.1 Normalized end milling experimental data 57
xiii
4.2 Correlations and RMSE values for end milling
prediction model 64
4.3 The results of SVM model for end milling prediction 65
4.4 Features of best model for end milling prediction 66
4.5 Scaled AWJ experimental data 67
4.6 Correlation and RMSE values for AWJ prediction
model 70
4.7 Features of best prediction model for AWJ 70
4.8 The results of SVM model for AWJ prediction 71
5.1 GRG value for machining parameters of uncoated 76
5.2 GRG value for machining parameters of TiAlN 78
5.3 GRG value for machining parameters of SNTR 79
5.4 Correlation and RMSE values of GR-SVM models
of uncoated
80
5.5 Correlation and RMSE values of GR-SVM models
for TiAlN 81
5.6 Correlation and RMSE values of GR-SVM models
for SNTR 82
5.7 Predicted surface roughness value for TiAlN in
hybrid GR-SVM 84
5.8 Minimum Ra values of hybrid GR-SVM in end
milling 83
5.9 Grey relational grade for the AWJ 85
5.10 Correlation value of GR-SVM models in AWJ 86
5.11 The result of GR-SVM prediction model for AWJ
(standoff distance discarded) 88
6.1 Paired sample statistic for uncoated end milling 91
6.2 Paired sample test of uncoated end milling 92
6.3 Paired sample statistic for TiAlN end milling 94
xiv
6.4 Paired sample test of TiAlN end milling 95
6.5 Paired sample statistic for SNTR end milling 96
6.6 Paired sample test of SNTR end milling 97
6.7 Minimum surface roughness values for end milling 98
6.8 GRG values in end milling parameters 99
6.9 Feature of SVM and hybrid GR-SVM models for
AWJ 101
6.10 Paired sample statistic of AWJ 102
6. 11 Paired sample test of AWJ 102
7.1 Summary result of end milling 107
7.2 Summary result of AJW 108
xv
LIST OF FIGURES
FIGURE NO. TITLE PAGE
2.1 Parameters that affect surface roughness 10
2.2 Illustration of end milling machining
process
12
2.3 Illustration of AWJ machining process 13
3.1 Research flow 45
3.2 Kernel function in LIBSVM 49
3.3 Sample training code for LIBSVM 50
3.4 Sample of testing code for LIBSVM 51
4.1 Flow of SVM prediction model 55
4.2(a) SVM model for uncoated with C= 1
Gamma= 0.333 58
4.2(b) SVM model for uncoated with C= 10
Gamma= 0.2 58
4.2(c) SVM model for uncoated with C= 10
Gamma= 0.333 59
xvi
4.3(a) SVM model for TiAlN with C= 10
Gamma= 0.333 60
4.3(b) SVM model for TiAlN with C= 0.5
Gamma= 0.333 60
4.3(c) SVM model for TiAlN with C= 500
Gamma= 0.4 61
4.4(a) SVM model for SNTR with C= 1
Gamma=0.333 62
4.4(b) SVM model for SNTR with C= 10 Gamma=
0.3 62
4.4(c) SVM model for SNTR with C= 100
Gamma= 0.4 63
4.5(a) SVM model for AWJ with C=0.1 and
Gamma=0.1 68
4.5(b) SVM model for AWJ with C=2.5 and
Gamma=0.1 68
4.5(c) SVM model for SNTR with C=0.5 and
Gamma=0.2 69
5.1 Flow of hybrid GR-SVM model 75
6.1 Prediction performances of SVM and GR-
SVM for uncoated 91
6.2 Prediction performances of SVM and GR-
SVM in TiAlN 93
6.3 Prediction performances of SVM and GR-
SVM in SNTR 96
6.4 Prediction performances of SVM and GR-
SVM 101
xvii
LIST OF ABBREVIATIONS
AIS Artificial Immune System
ANFIS Adaptive-Neuro-Fuzzy Inference System
ANN Artificial Neural Network
AWJ Abrasive Water Jet
EBM Electric Beam Machining
ECM Electrochemical Machining
FL Fuzzy Logic
GRA Grey Relational Analysis
GR-SVM Grey Relational- Support Vector Machine
LSE Least Square Error
PCM Photochemical Machining
RBF Radial Basis Function
SNTR Super Nitride Coating
SRM Structural Risk Minimization
SVM Support Vector Machine
TiAlN Coated Carbide Tools
xviii
LIST OF SYMBOLS
N Number of Sample Data
r Correlation value
Ra Surface Roughness
Al Aluminum
Cu Copper
Mg Magnesium
Cr Chromium
Zn Zink
Mn Manganese
CHAPTER 1
INTRODUCTION
This chapter discusses the brief overview about the research conducted in this
study. The topic includes background of the study, problem statement, objectives and
scopes of the study. Research significant and contributions of the study are also
discussed in this chapter.
1.1 Background of the study
As the demands of consumer economy grows rapidly, machining recently be
the most important and widely used in manufacturing process instead of forming,
molding, and casting processes. Generally, machining can be defined as a process of
material removal from a workpiece in the form of chips. In the recent years,
machining technology has been improved significantly to meet the requirements in
different fields. Basically, there are two types of machining process, namely
conventional machining and modern machining (Zain et al., 2011a). Conventional
machining consists of traditional process work piece removal in the form of chips
such as turning, milling, grinding and boring while modern machining comes in
terms of chemical items or advanced technologies such as abrasive water jet (AWJ),
2
electrochemical machining (ECM), electric beam machining (EBM) and
photochemical machining (PCM).
The need of modern machining or unconventional machining was firstly
emphasized by Merchant in 1960 for the development of newer concepts in
machining process (Panday and Shan, 1980). Due to the new advent technologies
that thrive nowadays, the machining process continuously evolve from time to time
as researcher have found and developed many new techniques and modern tool of
machining. There are two fields that have been interest for researches in machining,
which are modeling and optimization. According to Zain et al. (2011a), modeling in
machining refers to the process of estimating the potential minimum or maximum
value of machining performance while optimization refers to the process of
estimating optimal solution of cutting condition that leads to the minimum or
maximum machining value of machining performances. Various techniques were
considered and carried out to model and optimize the machining performances. This
study only focuses on modeling of machining performance. The primary purpose of
the machining modeling is to estimate the minimum values (such as surface
roughness, operation time, operation cost etc.) or maximum values (such as material
removal rate, tool wear etc.) of machining performance measurements.
It has been recognized that cutting force, power, torque, surface roughness,
tool-wear, chip form, chip breakability, tool-life, surface integrity and part accuracy
are the most common machining performances that evaluated by major measure
(Jawahir et al., 2003). According to Yusup et al. (2012), surface roughness is the
most machining performance that has been considered by the researchers for the past
five years (2007-2011), due to the facts that surface roughness affects several
functional attributes of the machining process (Wang, 2009). Practically, surface
roughness plays an important role in wear resistance, tensile, ductility, and fatigue
strength for machine parts (Wang and Chang, 2004). According to Caydas and
Hascalik (2008), surface roughness is a technological quality measurement of a
product and a factor that considerably affects the manufacturing cost. Consequently,
modeling of surface roughness, which is the process of predicting the minimum
3
value of surface roughness, is a crucial part in machining process in order to meet the
field requirements.
Due to the uncertainty and complexity in modeling of surface roughness in
machining process, computational approaches are being preferred apply by the
researchers (Jawahir et al., 2003). Computational approaches have become a most
potential research area and received a great deal of attention to the researchers to
develop a model for giving an improved value of surface roughness measurement. It
became exaggerate from time to time since it gave a best result to the researchers.
According to Zain et al., (2011a), single-based computational approaches were
managed to estimate the optimal process parameters, leading to the minimum value
of surface roughness. Computational approaches include fuzzy logic (FL), artificial
neural network (ANN), genetic algorithm (GA), particle swarm optimization (PSO),
ant colony optimization (ACO), simulated annealing (SA), Artificial Intelligent
System (AIS), Grey Relational Analysis (GRA), and support vector machine (SVM).
Moreover, the evolutionary of modern technology has lead to the new approaches in
modeling of machining performances.
The evolutionary of computational approach has become an important
problem solving methodology among researchers to improve the machining
performances (Grosan and Abraham, 2007). This approach includes the hybridization
process of single-based computational approach in order to get better performance in
machining modeling. This study considers the hybridization of SVM and GRA, in
estimating a better result for prediction of surface roughness value. Based on the
literature, an attempt of hybridization of SVM and GRA has been made in predicting
consumption of spare parts (Huang et al., 2010) and also agriculture field (Shi,
2012). For the predicting of spare parts consumption, the influencing factors were
selected by grey relational analysis and SVM regression was used as the prediction
model of the spare parts consumption for the military aircrafts and for agriculture
field, the production of China grain was predicted using the hybridization of SVM
and GRA. Both prediction results show that the hybrid SVM and GRA gave high
prediction performance with a small number of prediction errors.
4
However, there is still no attempt of hybridization SVM and GRA in
machining area. Thus, this study, which is modeling of surface roughness, is
considered as a new contribution to the machining field.
1.2 Problem Statement
The success of machining process depends on the proper selection of cutting
condition based on cost and quality factors. The major issue in machining process is
how to obtain accurate result of machining performances, such as surface roughness
values using various machining factors of cutting operations. Furthermore, the need
for estimating the best machining performances with the most suitable cutting tools
has been felt over the last few decades. These machining performances should be
selected to optimize the economics of machining operations. For instance, selection
of cutting condition of surface roughness in machining process still remains as one of
the most challenging problems due to the complexity of the process. Traditionally,
the selection of cutting condition in machining process is left to the machine
operator. However, this process mostly depends on the machinist expertise and also
gives a high cost. Moreover, in machining process, the cutting process was done
continuously in order to get the target value despite the material can be used only
once. So, this process will give highly cost with a lot of the wasted material used. In
such cases, machinist experience plays a major rule but sometimes it is difficult to
maintain the optimum values for each experiment (Aggawal and Singh, 2005).
With the help of computational approaches in predicting machining
performances, there is no machinist expertise will be used and the machining cost
can be reduced. Hence, this study promotes the use of SVM computational approach
in estimating surface roughness value of machining process. SVM becomes a new
trend in recent research especially in modeling of machining performances, which is
a relatively new computational learning method constructed based on the statistical
learning theory classifier (Chiu and Guao, 2008). SVM has widely been used in
modeling of various fields such as financial (Tay and Coa, 2001; Pai and Lin, 2005;
5
Min et al., 2006), health (Furey et al., 2000; Cai et al., 2001; Akay, 2009), and
agriculture. Based on the structural risk minimization (SRM) principal, SVM can get
decision-making rules and achieve small error for independent tests set and hence
can solve the learning problems efficiently (Samantha et al., 2003).
However, in some cases, the data in machining model may contain
irrelevance and redundant feature that is used as input features in development of
SVM model. This kind of data not only increased the training time, but it may lead to
the overfitting problem that reduces the prediction performance. This irrelevance and
redundant data also need to be eliminated from the input features in order to get a
better prediction model. Conventional SVM approach does not have the ability to
recognize this irrelevant element. So, there is a need of the model that is able to
remove the unwanted data in order to improve the model performance. As a result,
this research promotes the hybridization approach, hybrid GR-SVM, to estimate the
minimum values of surface roughness so that the machining target can be achieved.
GR-SVM is a hybridization model of SVM computational approach and also GRA
approach. GRA is basically an analysis technique that has been proposed in Grey
System Theory by Professor Deng Julong (Xuerui and Yuguang, 2004). GRA acts as
feature selection approach which is able to selects a subset of relevant features, and
also removes redundant and irrelevant features from the data to build robust learning
models.
In relation to the machining problem discussed above, three research question
of this study are:
i. How to predict potential values of the machining performances for giving a
possible value of minimum surface roughness?
ii. How to modify the existing single-based computational approach model in
order to give better results of surface roughness?
iii. How to identify the effectiveness of proposed hybridization model?
6
1.3 Aim of the research
The aim of the research is to identify the minimum value of surface
roughness in end milling and AWJ machining processes using the proposed hybrid
GR-SVM model.
1.4 Objectives of the research
The objectives of this study are:
i. To develop conventional SVM model in predicting the minimum value of
surface roughness.
ii. To develop a new hybridization GR-SVM model for predicting minimum
surface roughness value.
iii. To evaluate and validate the performance of the proposed hybrid model in
predicting minimum surface roughness value.
1.5 Scope of the research
The scopes of the study are:
i. Two machining process, end-milling (conventional machining) and
AWJ (modern machining) are considered.
ii. The machining performance considered for both end-milling and
AWJ is surface roughness (Ra).
iii. Process parameters of end-milling are cutting speed (v), feed rate (f),
and radial rake angle ( ).
7
iv. Process parameters of AWJ are traverse speed (V), waterjet pressure
(P), standoff distance (h), abrasive grit size (d), and abrasive flow rate
(m).
v. Experimental data of end-milling conducted by Mohruni (2008).
vi. Experimental data of AWJ conducted by Caydas and Hascalik (2008).
1.6 Research significant
This study is to develop and analyze the performance of proposed GR-SVM
model, which is a hybridization of SVM and GRA approach in prediction of
machining surface roughness value. In order to indicate the effectiveness of the
proposed model, the final results are compared with the conventional SVM
prediction model. With the development of proposed hybrid GR-SVM model, there
is no expertise or machinist will be used in order to find the minimum surface
roughness value hence the cost of the machining process can be reduced.
1.7 Contributions of the study
The contributions of this study can be divided into two parts, they are:
i. Improvement of conventional SVM model. The proposed hybrid GR-
SVM model has potential to estimate the influential factors of process
parameters to the surface roughness value. The influential factors of
process parameters are ranked based on Grey Relational Grade (GRG)
value obtained. Process parameter with the high GRG value is
considered to give a high effect to surface roughness value.
ii. Better quality of machined-work piece. The proposed hybrid model is
expected to improve the prediction accuracy that leads to much
minimum value of surface roughness.
8
1.8 Summary
This chapter discussed several topics related to the idea of research
implementation. Research background, problem statements, aims, objectives and
scopes were precisely mentioned in this chapter. The contributions of the study also
were highlighted.
111
REFERENCES
Aggarwal, A. and Singh, H. (2005). Optimization of machining techniques — A
retrospective and literature review. Sadhana 30: 699-711
Akay, M.F. (2009). Support vector machines combined with feature selection for
breast cancer diagnosis, Expert System with Applications 36, 3240-3247.
Benardos, P.G. and Vosnaikos, G.C. (2003). Predicting surface roughness in
machining: a review. International Journal of Machine Tools & Manufacture.
43, 833-844.
Brezak, D., Majestic, D., Udiljak, T. and Kasac, J. (2010). Tool wear estimation
using analytic fuzzy classifier and support vector machines, Journal of
Intelligent Manufacturing 23(3), pp 797-809.
Cai, Y.D., Liu, X.J., Xu, X.B and Zhou, G.P. (2001). Support vector machine for
predicting protein structural class, BMC Bioinformatics 2:3.
Caydas, U. and Ekici, S. (2010). Support vector machines models for surface
roughness prediction in CNC turning of AISI 304 austenitic stainless steel.
International Journal of Intelligent Manufacturing 23: 639-650
Caydas, U. and Ekici, S. (2012). Support vector machines models for surface
roughness prediction in CNC turning of AISI 304 austenitic stainless steel,
Journal of Intelligent Manufacturing 23:639–650.
Çaydaş, U. and Hascalik, A. (2008). A study on surface roughness in abrasive
waterjet machining process using artificial neural networks and regression
analysis method, Journal of Material Processing Technology 202, 574-582.
Chiu, N.H., and Guao, Y.Y. (2008). State classification of CBN grinding with
support vector machine, Journal of Material Processing Technology, 201,
601-605.
Chiu, N.H., Guao, Y.Y. (2008). State classification of CBN grinding with support
vector machine, Journal of Material Processing Technology, 201, 601-605
112
Cho, S., Asfour, S., Onar, A., and Kauninya, N. (2005). Tool breakage detection
using support vector machine learning in a milling process, International
Journal of Machine Tools & Manufactures, 45, 241-249.
Chuang, L.Y., Yang, C.H., and Jin, L.C. (2005) Classification of multiple cancer
types using support vector machines and outliner detection methods,
Biomedical Engineering- Applications, Basis & Communications, 300-308.
Deng, C., Wu. J., and Shao, X. ( 2008). Realibility assessment of machining
accuracy on suppor vector machine, Springer, LNAI 5315, pp. 669-678.
Dong, H., Wu, D. and Su, H. (2006). Use of least square support vector machine in
surface roughness prediction model, Third international Symposium on
president Mechanical Measurement, Kuang-Chao Fan, Vol 6280, 628022.
Elangovan, M., Sugumaran, V., Ramachandran, K.I. and Ravikumar, S. (2011).
Effect of SVM kernel functions on classification of vibration signals of a
single point cutting tool. Expect System with Application 38,15202-15207.
Friedrichs, F. and Igel, C. (2005). Evolutionary tuning of multiple SVM parameters,
Neurocomputing 64, 107-117.
Fu, C, Zheng, J., Zhao, J. and Xu, W. (2001) Application of Grey Relational
Analysis For Corrosion Failure Of Oil Tubes, Corrosion Science 43, 881-889
Furey, T.S., Cristianini, N., Duffy, N., Bednarski, D.,W., Shummer, M., and
Haussler, D. (2000). Support vector machine classification and validation of
cancer tissue samples using microarray expression data, Bioinformatics 16,
906-914.
Grosan, G and Abraham, A (2007) Hybrid evolutionary algorithms: Methodologies,
Architectures and Reviews. Studies in Computational Intelligent 75: 1-17.
Gupta, A.K. (2010) Predictive modeling of turning operations using response surface
methodology, artificial neural networks and support vector regression,
International Journal of Production Research, 48:3, 763-778.
Guyon, I. and Elisseeff, A. (2003) An introduction to variable and feature selection,
Journal of Machine Learning Research 3, 1157-1182.
Hascalik, A., Caydas, U and Gurun, H. (2007). Effect of traverse speed on abrasive
water jet machining of Ti-6Al-4V alloy, Material and Design, Vol 28, 1953-
1957.
Haykin, S.S. (1999).Neural networks: a comprehensive foundation Prentice Hall,
Upper Saddle River, NJ.
113
Hsu, C.W., Chang, C.C., and Lin, C.J. (2003). A practical guide to support vector
classification. Technical report, University of National Taiwan, Department
of Computer Science and Information Engineering, pp. 1–12.
Hsueh, Y.W., and Yang,C.Y. (2009). Tool breakage diagnosis in face milling by
support vector machine, Journal of Material Processing Technology 209,145-
152.
Huang, C., and Wang, C.J. (2006). A GA-based feature selection and parameters
optimization for support vector machines. Expert Systems with Applications,
31 (2006), pp. 231–240
Jangra, K., Grover, S. and Aggarwal, A. (2012). Optimization of multi machining
characteristics in WEDM of WC-5.3% Co composite using integrated
approach of Taguchi, GRA and entropy method, Frontiers of Mechanical
Engineering , Vol 7 Issue 3, 288-299.
Jawahir, I.S., Balaji, A.K., Rouch, K.E. and Baker, J.R. (2003). Towards integration
of hybrid models for optimized machining performance in intelligent
manufacturing systems. Journal of Material Processing Technology. 139,
488–498.
Jiang, Z. (2010). Intelligent prediction of surface roughness of milling aluminum
alloy based on least square support vector machine. In Control and Decision
Conference, pp. 2872-2876
Kalpakjian, S. (1991). Manufacturing Engineering and Technology, 2nd edition,
Addision-Wesley, Reading, MA.
Kerman, V. (2005) Support Vector Machine-An Introduction Stufuzz, Springer 177,
1- 47.
Lela, B., Bajie, D. and Jorie, S.(2009). Regression analysis, support vector machines,
and Bayesian neural network approaches to modeling surface roughness in
face milling. International Journal of Advance Manufacturing
Technology,42,1082–1088
Li, X., Zhou, J., Zeng, H., Wong, Y.S. and Hong, G.S. (2006). An intelligent
predictive engine for milling machine prognostic monitoring,IEEE
International Conference on Industrial Informatics, 1075- 1080.
Li, Y., Ou, Z., and Wang, G. (2006). An intelligent predictive engine for milling
machine prognostic monitoring, IEEE International Conference on Industrial
Informatics, 1075- 1080
114
Lin, S. W., Ying, K. C., Chen, S. C., & Lee, Z. J. (2008). Particle swarm
optimization for parameter determination and feature selection of support
vector machines. Expert Systems with Applications, 35(4), 1817-1824.
Lin, S.J., Lu, I.J., and Lewis, C. (2007) Grey Relation Performance Correlations
among Economics, Energy Use and Carbon Dioxide Emission in Taiwan,
Energy Policy 35, 1948-1955.
Lin, W., and Fu, J. (2010). Support vector machine and neural network united system
for NC machine tool thermal error modeling, Sixth International Conference
on Natural Computation (ICNC), 4305-4309.
Lin, W., Fu, J (2010) Support vector machine and neural network united system for
NC machine tool thermal error modeling, Sixth International Conference on
Natural Computation (ICNC), 4305-4309.
Min, S.H., Lee, J. and Han, I. (2006). Hybrid genetic algorithms and support vector
machines for bankruptcy prediction, Expert System with Applications 31,
652-660.
Mohruni, A.S. (2008). Performance evaluation of uncoated and coated carbide tools
when end milling of titanium alloy using response surface methodology.
Thesis for Doctor of Philosophy, Universiti Teknologi Malaysia, Skudai,
Johor, Malaysia.
Nayak, K.C. and Tripathy, R.K. (2012). Taguchi integrated least square support
vector machine an alternative to artificial neural network analysis of
electrochemical machining process, IOSR Journal of Mechanical and Civil
Engineering, Volume 1, Issue 3, 2278-1684.
Pai, P.F and Lin, C.S. (2005). A hybrid ARIMA and support vector machines model
in stock price forecasting, Omega 33, 497-503.
Panday PC, Shan HS (1980) Modern machining process, Tata McGraw-Hill
Publishing Company Limited, 1-2.
Pawade, R.S. and Joshi, S.S. (2011). Multi-objective optimization of surface
roughness and cutting forces in high-speed turning of Inconel 718 using
Taguchi grey relational analysis, International Journal of Advance
Manufacturing Technology 56, 47-62.
Prakasvudhisarn, C., Kunnapapdeelert, S., and Yenradee, P. (2009). Optimal cutting
condition determination for desired surface roughness in end milling. The
115
International Journal of Advanced Manufacturing Technology, 41(5), 440-
451.
Qian, Y., Tian, J., Liu,L., Zhang,Y. and Chen,Y. (2010) A tool wear predictive
model based on SVM, IEEE.
Qian,Y., Tian, J., Liu, L., Zhang, Y and Chen, Y. (2010). A tool wear predictive
model based on SVM, Chinese Control and Decision Conference, 1213-1217.
Radhika, Y., Shashi, M. (2009) Atmospheric temperature prediction using support
vector machines. International Journal of Computing Theory Eng 1:1793–
8201
Ramesh, R., Kumar, R.K.S., and Anil, G. (2009). Automated intelligent
manufacturing system for surface finish control in CNC milling using support
vector machines. International Journal of Advance Manufacturing
Technology 42, 1103–1117.
Ramesh, R., Mannan, M.A., and Poo, A.N.(2002). Support vector machines model
for classification of thermal error in machine tools, International Journal of
Advanced Manufacturing Technology 20,114-120.
Salgado, D.R., Alonso, F.J., Cambero, I., and Marcelo, A. (2009) In-process surface
roughness prediction system using vibrations in turning, International
Journal of Advance Manufacturing Technology 43,40-51.
Sallehuddin, R., Shamsuddin, S.M., and Hashim, S.Z.M (2010) Forecasting small
data set using hybrid cooperative feature selection, International Conference
on Computer Modeling and Simulation, IEEE, DOI
10.1109/UKSIM.2010.23, 80-85
Samanta, B., Al-Balushi, K.R., and Al-Araimi, S.A. (2003). Artificial neural
networks and support vector machine with genetic algorithm for bearing fault
detection. Engineering Application of Artificial Intelligent 16, 657-665.
Samantha, B. (2009). Surface roughness prediction in machining using soft
computing, International Journal of Computer Integrated Manufacturing,
22:3, 257-266.
Samui, P. and Dixon, B. (2011). Application of support vector machine and
relevance vector machine to determine evaporative losses in reservoirs.
Hydrological Processes, 26(9), 1361-1369.
116
Sanjay, C. and Jyothi, C. (2006). A study of surface roughness in drilling using
mathematical analysis and neural Networks, International Journal of
Advanced Manufacturing Technology. 29, 846-852.
Shi, D. and Gindy, N.N. (2007) Tool wear predictive model based on least squares
support vector machines, Journal of Material Processing Technology, 21,
1799-1814.
Shi, J.F. (2012). Application of the GRA and SVM for forecast of China grain
production, Materials Science and Information Technology, 433-440.
Song, Q. and Shepperd, M. (2011).Predicting software project effort: A grey
relational analysis based method, Expert System with Applications, Vol 38,
7302-7316.
Sugumaran,V., Muralidram,V. and Ramachandran.,K-I.( 2007). Feature selection
using decision tree and classification through proximal support vector
machine for fault diagnosis of roller bearing, Mechanical System and Signal
Processing 21,930-942.
Tay, F.E.H and Coa, L. (2001). Application of support vector machine in financial
time series forecasting, Omega 29 , 309-317.
Tosun, N. (2006) Determination of optimum parameters for multi performance
characteristics in drilling by using grey relational analysis. International
Journal of Advanced Manufacturing Technology 28: 450–5
Vafaie, H and Imam, I.F. (1994) Feature selection methods: Genetic Algorithms vs.
greedy-like Search, Preceedings of the 3rd International Fuzzy System and
Inteliggent Control Conference.
Vapnik, V. (1995) The Nature of Statistical Learning Theory. Springer, New York.
Wang, M.Y. and Chang, H.Y. (2004) Experimental study of surface roughness in slot
end milling AL2014-T6. International Journal of Machine Tools and
Manufacture 44:51-57
Wang, M.Y. and Chang, H.Y. (2004). Experimental study of surface roughness in
slot end milling AL2014-T6, International Journal of Machine Tools and
Manufacture. 44, Pages 51-57.
Wang, P., Meng. Q., Zhoa, J., Li, J. and Wang, X. (2011) Prediction of machine tool
condition using support vector machine, Journal of Physics: conference
series 305, 012113.
Wang, X. (2009). Intelligent modeling and predicting surface roughness in end
milling, Fifth International Conference on Natural Computation, 521-525.
117
Wedyan, A.H. (2001).A technique for fuzzy logic modeling of machining
process.IFSA World Congress and 20th NAFIPS International Conference,
vol 5. 3021-3026.
Widodo, A., and Yang, B.S. (2007). Support vector machine in machine condition
monitoring and fault diagnosis. Mechanical Systems and Signal Processing
21, 2560– 2574.
Wu, D. (2008). Surface hardness intelligent prediction in milling using support
vector regression, International Conference on Natural Computation.
Wu, S. X., and Banzhaf, W. (2010). The use of computational intelligence in
intrusion detection systems: A review. Applied Soft Computing, 10(1), 1-35.
Xu,Y., Deng, C., and Wu, J.,(2009), Least square support vector machines for
performance degradation modeling of CNC equipments, IEEE. 201-206.
Xuerui, T. and Yuguang, L. (2004) Using grey relational analysis to analyze the
medical data, Kybernetes 33 : 355 – 362
Yang, J., Zhang, Y., and Zhu, Y. (2007). Intelligent fault diagnosis of rolling element
bearing based on SVMs and fractal dimension, Mechanical Systems and
Signal Precessing 21, 2012-2024.
Yuan, X.H.,Wang,Y.Q., and Hu,Y.H. (2010). Failure probabilistic model of CNC
lathe based on support vector machine. International Conference on
Computer, Mechatronics,Control and Electronic Engineering.322-325.
Yusup, N., Zain, A.M. and Hashim, S.Z.M. (2012). Evolutionary techniques in
optimizing machining parameters: Review and recent applications (2007-
2011), Expert System with Application 39, 9909-9927.
Zain, A.M., Haron, H. and Sharif, S. (2010a). Prediction of surface roughness in end
milling machining using Artificial Neural Network. Expert Systems with
Applications 37, 1755–1768.
Zain, A.M., Haron, H. and Sharif, S. (2011a).Estimation of the minimum machining
performance in the abrasive waterjet machining using integrated ANN-SA,
Expert Systems with Applications 38, 8316–8326.
Zain, A.M., Haron, H. and Sharif, S. (2011b). Genetic Algorithm and Simulated
Annealing to estimate optimal process parameters of the abrasive waterjet
machining, Engineering with Computers 27:251–259.
118
Zain, A.M., Haron, H., Qasem, S.N. and Sharif, S. (2012). Regression and ANN
models for estimating minimum value of machining performance, Applied
Mathematical Modeling 36, 1477–1492.
Zhang, D. and Sui,W. (2011). The application of AR Model and SVM in rolling
bearings condition monitoring, CSIE 2011, Part 1, CCIS 152, pp.326-231.
Zhang, L. Xiong,G. Liu,H., Zou,H., and Guo,W. (2009). An Intelligent Fault
Diagnosis Method Based on Multiscale Entropy and SVMs. Springer-Verlag
Berlin Heidelberg, 724-732.
Zhang, L., Jia,Z., Wang,F., and Liu,W. (2010). A hybrid model using supporting
vector machine and multi-objective genetic algorithm for processing
parameters optimization in micro-EDM, International Journal of Advance
Manufacturing Technology 51,575–586.
Zhang, M. and Yao, J.T. (2004) A rough set based approach to feature selection,
Fuzzy Information,Processing NAFIPS 04, Vol.1, no., pp. 434- 439
