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UNIVERSITI TEKNIKAL MALAYSIA MELAKA
OPTIMIZATION OF MACHINING PARAMETERS IN TURNING
PROCESS OF ALUMINUM ALLOY 6063
This report submitted in accordance with requirement of the Universiti Teknikal
Malaysia Melaka (UTeM) for the Bachelor Degree of Manufacturing Engineering
(Robotic and Automation) with Honours.
by
LEE CHEE YOONG
FACULTY OF MANUFACTURING ENGINEERING
2010
UNIVERSITI TEKNIKAL MALAYSIA MELAKA
BORANG PENGESAHAN STATUS LAPORAN PROJEK SARJANA MUDA
TAJUK: Optimization of Machining Parameters in Turning Process of Aluminum Alloy
6063
SESI PENGAJIAN: 2009/10 Semester 2
Saya LEE CHEE YOONG
mengaku membenarkan tesis Laporan PSM ini disimpan di Perpustakaan Universiti Teknikal
Malaysia Melaka (UTeM) dengan syarat-syarat kegunaan seperti berikut:
1. Laporan ini adalah hak milik Universiti Teknikal Malaysia Melaka dan penulis.
2. Perpustakaan Universiti Teknikal Malaysia Melaka dibenarkan membuat salinan untuk
tujuan pengajian sahaja dengan izin penulis.
3. Perpustakaan dibenarkan membuat salinan laporan PSM ini sebagai bahan pertukaran
antara institusi pengajian tinggi.
4. **Sila tandakan (√)
SULIT
TERHAD
√ TIDAK TERHAD
(Mengandungi maklumat yang berdarjah keselamatan atau
kepentingan Malaysia yang termaktub di dalam AKTA
RAHSIA RASMI 1972)
(Mengandungi maklumat TERHAD yang telah ditentukan oleh
organisasi/badan di mana penyelidikan dijalankan)
(TANDATANGAN PENULIS)
Alamat Tetap:
No. 9, Lorong Kledang Utara 1,
31450, Menglembu,
Ipoh, perak
Tarikh: _________________________
Disahkan oleh:
(TANDATANGAN PENYELIA)
Cop Rasmi:
Tarikh: _______________________
** Jika Laporan PSM ini SULIT atau TERHAD, sila lampirkan surat daripada pihak
berkuasa/organisasi berkenaan dengan menyatakan sekali sebab dan tempoh laporan ini perlu
dikelaskan sebagai SULIT atau TERHAD.
DECLARATION
I hereby, declared this report entitled “Optimization of Machining Parameters in
Turning Process of Aluminum Alloy 6063” is the results of my own research except as
cited in references.
Signature : ………………………
Author’s Name : LEE CHEE YOONG
Date : 9 APRIL 2010
APPROVAL
This report is submitted to the Faculty of Manufacturing Engineering of UTeM as a
partial fulfillment of the requirements for the degree of Bachelor of Manufacturing
Engineering (Robotic and Automation) with Honours. The member of the supervisory
committee is as follow:
(Signature of Supervisor)
………………………………….
(Official Stamp of Supervisor)
OPTIMIZATION OF MACHINING PARAMETERS IN
TURNING PROCESS OF ALUMINUM ALLOY 6063
LEE CHEE YOONG
UNIVERSITI TEKNIKAL MALAYSIA MELAKA
i
ABSTRACT
This report presents an optimization of machining parameters in turning process of
Aluminum alloy 6063 T6. OKUMA LCS-15 horizontal turning machines have been
used to run the experiments on Aluminum alloy 6063 T6. Aluminum is widely used in
automotive industry, aerospace applications, architectural applications and others. The
main objective of the research was to determine the effect of different parameters setting
such as depth of cut, feed rate and cutting speed on surface roughness and hardness. In
this research, the RSM (response surface methodology) method with Central Composite
Design (CCD) was used to design the experiment and 20 trials were conducted. The
machined surface was checked by using portable surface roughness tester and hardness
was measured by using Digital Micohardness tester. The quadratic model was selected
in the analysis of variance (ANOVA) and the model graph revealed the interaction
between the three controllable parameters. The analysis revealed that cutting speed was
the significant effect to the surface roughness followed by the feed rate while depth of
cut was less effect. The best surface finish can be obtained with the combination of
lowest cutting speed and feed rate. Besides, the numerical optimization method was
employed in the optimization and the best predicting setting was depth of cut 0.69 mm,
feed rate 0.09 mm/rev and cutting speed 153.14 m/min. The average deviation
percentage value was 54.84%. In the experiment, the hardness was influenced by the
cutting speed and feed rate. The highest of cutting speed and feed rate will cause
hardness decreasing.
ii
ABSTRAK
Laporan ini menerangkan pengoptimuman parameter dalam proses pemesinan larik pada
Aluminium alloy 6063 T6. Okuma SKB-15 mesin larik melintang digunakan untuk
menjalankan eksperimen pada bahan kerja Aluminium 6063 T6. Aluminium banyak
digunakan dalam industri automotif, angkasa aplikasi, arsitektur aplikasi dan lain-lain.
Objektif utama dari penelitian ini adalah untuk menentukan kesan daripada tatacara
parameter yang berbeza seperti kedalaman pemotongan, kadar suapan dan kelajuan
dalam pemotongan ke atas kekasaran permukaan dan kekerasan. Dalam kajian ini,
kaedah RSM (response surface methodology) bersama dengan Central Composite
Design (CCD) digunakan untuk merancang percubaan bagi eksperimen ini dan 20 uji
kaji telah dijalankan dengan merujuk kepada jadual matriks. Permukaan yang telah
dimesin diperiksa dengan menggunakan alat uji kekasaran permukaan mudah alih dan
kekerasan diukur dengan menggunakan Digital Micohardness tester. Model kuadratik
dipilih dalam analisis varians (ANOVA) dan model grafik mendedahkan interaksi antara
tiga parameter yang dikendalikan. Analisis menunjukkan bahawa kelajuan pemotongan
adalah pembolehubah yang signifikan terhadap kekasaran permukaan diikuti oleh kadar
suapan sementara kedalaman pemotongan adalah kurang signifikan. Permukaan terbaik
boleh didapati dengan kombinasi kelajuan pemotongan dan kadar suapan dengan nilai
yang terendah. Selain itu, kaedah pengoptimuman berangka telah digunakan dalam
optimasi dan tatacara yang terbaik untuk meramalkan adalah kedalaman pemotongan
0.69 mm, kadar suapan 0.09 mm/rev dan kelajuan pemotongan 153.14 m / min. Nilai
rata-rata deviasi peratusan adalah 54.84%. Dalam eksperimen ini, kekerasan telah
dipengaruhi oleh kelajuan pemotongan dan kelajuan secara maklumnya. Peningkatan
kelajuan pemotongan dan kadar suapan secara maklum akan menyebabkan penurunan
kekerasan.
iii
DEDICATION
To my lovely family and fellow friends that accompanying me along the difficult
pathway in my university life, thanks for your help and support.
iv
ACKNOWLEDGEMENT
I would like to take this opportunities to express my sincere thank to all people that
helping me to succeed this project. Firstly, I would like to thank to my project supervisor,
Ms LIEW PAY JUN. Without her guidance, I would not be succeeding to accomplish
my project. Once again thanks for her guidance, advice, and help in this project.
Secondly, I would like to thank to Manufacturing Engineering Laboratory of Universiti
Teknikal Malaysia Melaka (UTeM) for providing me the equipments and machines.
Moreover, I would like to thank to Mr. Farihan for giving me lot convenience in using
the equipments in the Laboratory. Besides, I would like to thank to laboratory of
Universiti Tun Hussein Onn Malaysia (UTHM) for providing equipments to accomplish
my project.
Furthermore, special thanks to all my friends Lee Weng Sum, Liew Tong Ken and Tan
King Hwang. They had giving me advice, ideas, comments and sharing their time to
completed my project.
Lastly, unforgettable I would like to thank to my parents and family that always give me
encourage and supports to me. I was very appreciating the supports they give to me with
heartfelt.
v
TABLE OF CONTENT
Abstract i
Abstrak i i
Dedication iii
Acknowledgement iv
Table of Content v
List of Tables ix
List of Figures x
List Abbreviations xii
1. INTRODUCTION 1
1.1 Introduction 1
1.2 Problem statement 3
1.3 Objective 3
1.4 Scope 3
1.5 Importance of study 4
1.6 Result expected 4
2. LITERATURE REVIEW 5
2.1 Turning Process 5
2.2 Parameters 6
2.2.1 Cutting Speed 7
2.2.2 Feed Rate 8
2.2.3 Depth of Cut 8
2.3 Aluminum alloy 6063 9
2.4 Cutting Tool Material 9
2.5 Responses of Turning Process 1 1
2.5.1 Surface Roughness 1 2
2.5.2 Surface Texture 1 3
vi
2.5.3 Vickers Hardness Test 1 4
2.6 Design of Experiment 1 6
2.6.1 Response Surface Methodology 1 6
2.6.1.1 Central Composite Design (CCD) 1 7
2.6.1.1.1 Factorial Points 1 7
2.6.1.1.2 Star or Axial Points 1 8
2.6.1.1.3 Center Points 1 8
2.7 Summary of Journal 1 9
2.8 Conclusion 2 3
3. RESEARCH METHODOLOGY 2 4
3.1 Flow chart of study 2 5
3.2 Stage 1: Define the objective of the experiment 2 6
3.3 Stage 2: Determine the parameters speed at low level and high level 2 6
3.4 Stage 3: Identify the appropriate responds variable 2 7
3.5 Stage 4: Design of experiment matrix 2 7
3.6 Stage 5: Preparation of the experiment 2 8
3.6.1 OKUMA LCS-15 horizontal machines 2 8
3.6.2 Abrasive cutting machine 2 9
3.6.3 Workpiece preparation 3 0
3.6.3.1 Material properties 3 1
3.6.4 Cutting inserts 3 2
3.6.5 Tool Holder 3 3
3.7 Stage 6: Run the experiment 3 3
3.7.1 Surface Roughness Testing 3 5
3.7.2 Cutting Process (Cross Section) 3 6
3.7.3 Mounting Process 3 7
3.7.4 Hardness Test 3 9
3.8 Stage 7: Analysis the Results 4 0
3.9 Stage 8: Conclusion 4 0
3.10 Gantt chart for PSM 1 4 1
vii
3.11 Gantt chart for PSM 2 4 2
4. RESULTS AND DISCUSSION 4 3
4.1 Results 4 3
4.2 Analysis of Surface Roughness 4 5
4.2.1 Transformation 4 5
4.2.2 Fit Summary 4 6
4.2.2.1 Sequential Model Sum of Squares [Type I] 4 6
4.2.2.2 Lack of Fit Tests 4 8
4.2.2.3 Model Summary Statistics 4 8
4.2.3 ANOVA 4 9
4.2.3.1 Backward Elimination 5 1
4.2.3.2 Regression Statistics 5 2
4.2.4 Model Diagnostics Plots 5 3
4.2.5 Model Graph 5 5
4.2.5.1 Effect of Feed Rate on Surface Roughness 5 5
4.2.5.2 Effect of Cutting Speed on Surface Roughness 5 7
4.2.5.3 Effect of Depth of Cut on Surface Roughness 6 0
4.2.5.4 Perturbation Graph 6 1
4.2.5.5 Effect of Interaction between Cutting Speed and Feed Rate
on Surface Roughness 6 2
4.2.5.6 Effect of Interaction between Depth of Cut and Feed Rate
on Surface Roughness 6 3
4.2.5.7 Cube Plot 6 4
4.3 Optimization 6 5
4.4 Confirmation Run 6 8
4.4.1 Average Deviation Percentage Value 7 0
4.5 Hardness Test 7 1
5. CONCLUSION AND RECOMMENDATIONS 7 3
5.1 Conclusion 7 3
viii
5.2 Recommendations 7 4
REFERENCES 7 5
APPENDICES
A Results of surface roughness
ix
LIST OF TABLES
2.1 Summary of journal 1 9
3.1 Factors and their entire unit and levels 2 6
3.2 Input variables from RSM software 2 7
3.3 Specification of OKUMA LCS-15 horizontal turning machines 2 9
3.4 Chemical composition 3 1
3.5 Mechanical properties 3 2
3.6 Physical properties 3 2
3.7 Constant parameter 3 4
4.1 Surface roughness value with different factor 4 4
4.2 Sequential Model Sum of Squares [Type I] 4 7
4.3 Lack of Fit Test 4 8
4.4 Model Summary Statistics 4 8
4.5 Analysis of variance table [Partial sum of squares – Type III] 5 0
4.6 Analysis of variance table [Partial sum of squares – Type III] after removing
the significant model 5 2
4.7 Regression statistic 5 2
4.8 Solution Value generates by Design Expert software 6 7
4.9 Optimization value for surface roughness 6 8
4.10 Average Deviation Percentage Value 7 0
4.11 Hardness test value with difference distance 7 1
x
LIST OF FIGURES
2.1 The hardness of various cutting-tool materials as a function of
temperature (hot hardness) 1 0
2.2 Ra of a surface profile P on a sampling length L 1 3
2.3 Surface characteristic 1 4
2.4 Degree of diamond intender 15
2.5 Indenter shape 15
2.6 Central Composite Design (CCD) 18
3.1 Stages of experiments 2 4
3.2 OKUMA LCS-15 horizontal turning machines 2 8
3.3 METACUT A250 Automatic Abrasive Cutting Machine 3 0
3.4 Horizontal band saw machine 3 0
3.5 Flow chart of material preparation 3 1
3.6 TNMG 160408 ML TT3500 carbide inserts 3 2
3.7 MTJNR 2525 M16W tool holder 3 3
3.8 Flow chart of procedures 3 4
3.9 Portable surface roughness tester, SJ-301 3 5
3.10 Precision reference specimen 3 5
3.11 Detector stylus parallel to the workpiece surface 3 6
3.12 Cutting process with abrasive cutting 3 6
3.13 METKON FINOPRESS MOUNTING PRESS 3 7
3.14 Phenolic resin 3 8
3.15 A mounted specimen 3 8
3.16 Digital Microhardness Tester (Affri) 3 9
3.17 Diamond shape on the surface 4 0
3.18 Distance between each diamond shape (1mm) 4 0
4.1 Box-Cox plot for power transform 4 5
xi
4.2 Normal Plot of Residuals for Quadratic graph 5 3
4.3 Predicted versus residuals for Quadratic graph 5 4
4.4 One factor graph of feed rate versus surface roughness 5 5
4.5 One factor graph of cutting speed versus surface roughness 5 7
4.6 One factor graph of depth of cut versus surface roughness 6 0
4.7 Perturbation graphs of Factor A, B and C 6 1
4.8 3D model and contour graph of feed rate and cutting speed to respond of
surface roughness 6 2
4.9 Effect of interaction depth of cut and feed rate on surface roughness 6 3
4.10 Cube plot of three factors that influence the surface roughness 6 4
4.11 Numerical optimization criteria 6 5
4.12 Ramp function graph 6 8
xii
LIST OF ABBREVIATIONS
AA - Arithmetic Average
ANOVA - Analysis of Variance
BUE - Build-up-edge
CCD - Central Composite Design
CLA - Center Line Average
CNC - Computerized Numerical Control
CVD - Chemical-vapor Deposition
DF - Degree of Freedom
DOC - Depth of Cut
DOE - Design of Experiment
HRC - Rockwell Hardness Number
HSS - High Speed Steel
HV - Vickers Hardness
Prob - Probability
PVD - Physical-vapor Deposition
RPM - Revolution per Minute
Ra - Roughness Average
RSM - Response Surface Methodology
SCEA - Side Cutting Edge Angle
SEM - Scanning Electron Microscope
SiC - Silicon Carbide
SS - Sum of Squares
TiC - Titanium Carbide
TiCN - Titanium Carbonitride
TiN - Titanium Nitride
UTeM - University Teknikal Malaysia Melaka
UTHM - University Tun Hussein Onn Malaysia
1
CHAPTER 1
INTRODUCTION
This chapter describes the introduction of the project and briefly explained the problem
statements and objective in the research. This chapter also included the scope and the
importance of the study for the project.
1.1 Introduction
In this challenge world, industries around the world constantly strive for lower cost
solutions with reduced lead time and better surface quality in order to maintain their
competitiveness. Automated and flexible manufacturing systems are employed for that
purpose along with computerized numerical control (CNC) machines that are capable of
achieving high accuracy and very low processing time (Nalbant, 2006). In the CNC
machining, determining optimal cutting conditions or parameters under the given
machining situation is difficult in practice. Conventional way for selecting these
conditions such as cutting speed and feed rate has been based upon data from machining
handbooks and/or on the experience and knowledge on the part of programmer. As a
result, the metal removal rate is low because of the use of such conservative machining
parameters (Kyung and Soung, 1997).
Turning is the first most common method for cutting and especially for the finishing
machined parts. In a turning operation, it is important task to select cutting parameters
for achieving high cutting performance. Cutting parameters are reflected on surface
roughness, surface texture and dimensional deviations of the product (Nalbant, 2006).
2
Surface finish obtained in manufacturing processes mainly depends on the combination
of two aspects: the ideal surface finish provided by marks that manufacturing process
produces on the surface and the actual surface finish which is generated taking into
account irregularities and deficiencies that may appear in the process, changing
manufacturing initial conditions (Arbizu and Perez, 2002).
The surface of every part includes some type of texture created by any combination of
the following factors; the microstructure of the material, the action of the cutting tool,
cutting too instability, errors in tool guide ways and deformations caused by stress
patterns in the component.
The surface texture of an engineering component was very important. It was affected by
the machining processes by changes in the conditions of either the component or
machine (Abouelatta and Madl, 2000). A machined surface carries a lot of valuable
information about the process including tool wear, built-up edge, vibrations, damaged
machine elements etc. Under stable machining conditions, the surface texture changes
remarkably due to the changes in the cutting tool shape caused by wear. Since the
cutting tool operates directly on the workpiece during a machining operation, it affects
the texture of the workpiece surface which in turn provides reliable and detectable
information to categorize the condition of the tool. Consequently, a machined surface is
a replica of the cutting edge which carries valuable information related to the tool
condition (i.e., sharpness or bluntness) (Kasim et. al. 2006).
The purpose of this project was to investigate the surface roughness and hardness on
engineering component by turning process. To achieve the result of surface roughness
and hardness, the material used for the investigation was aluminum alloy 6063 T6. Thus,
the surface roughness and the hardness was measured and observed in the experiment.
The RSM method was used to design the variables of cutting parameter in the
experiment in order to obtained data of various specimens. Optimization was involved in
the research to provide a better surface roughness prediction for turning aluminum alloy.
3
1.2 Problem statement
In machining operations, the quality of the surface finish was playing an important role
for many turned workpieces. However, human operators or programmer normally
inspecting the surface according to their experiences or refer from machining handbooks.
With regarded to the quality characteristics of turning parts, some of the problems
included surface roughness, burr, and tool wear, etc. The machining parameters such as
cutting speed, feed rate, depth of cut, features of tools, work piece material and coolant
conditions were highly affect the performance characteristics. It was necessary to select
the most appropriated machining settings in order to improved cutting efficiency,
process at low cost, and produced high-quality products.
a) What are the relationships between the controllable factors (in the study: spindle
speed, feed rate, and depth of cut) and the response factor (surface roughness and
hardness)?
b) What are the most controllable factors that produced a better surface finish?
1.3 Objective
To study the effect of different parameters to the surface roughness and hardness
of aluminum alloy 6063 using RSM (response surface methodology) method.
To optimize the parameters for surface roughness.
1.4 Scope
The study was aim on the spindle speed, feed rate, and depth of cut on the surface
roughness and hardness of workpiece. The aluminum alloy 6063 T6 was used as the
workpiece material and the insert that used in the cutting process was TNMG 160408
ML TT3500 CVD coated carbide inserts. In addition, the machining process was under
4
the wet environment. The surface roughness was tested by using portable surface
roughness tester, SJ301. Besides, the hardness of the materials was tested by using Affri
Digital Microhardness Tester. The parameters that not cover in the research were tool
wear, nose radius, edge preparation, insert angle, cutting forces and others.
1.5 Importance of study
The importance of this study was to investigate the surface roughness and hardness
produce by different controllable parameter such as cutting speed, feed rate and depth of
cut by turning aluminium alloy 6063. By using the RSM method, it was easy to
established variables parameters in design the experiment. Besides, the technique of the
optimization was implemented for obtained better surface finish values.
1.6 Result expected
At the end of this study, it was expected that the effect of different parameters to the
machined surface and hardness can be obtained. The optimum parameters were obtained
simultaneously.
5
CHAPTER 2
LITERATURE REVIEW
This chapter describes the turning process and the parameters influenced to the surface
roughness and hardness by referred from the previous journal. Besides, the method that
used for design the experiment was also explained in this chapter.
2.1 Turning Process
In modern industry the goal is to manufacture low cost, high quality products in short
time. Automated and flexible manufacturing systems were employed for that purpose
along with computerized numerical control (CNC) machines that are capable of
achieving high accuracy and very low processing time. Turning was the most common
method for cutting and especially for the finishing machined parts. Furthermore, in order
to produce any product with desired quality by machining, cutting parameters should be
selected properly (Chorng et. al., 2008).
Turning was a widely used machining process in which a single-point cutting tool
removes material from the surface of a rotating cylindrical workpiece. Three cutting
parameters, i.e., cutting speed, feed rate, and depth of cut, must be determined in a
turning operation. Common methods of evaluating machining performance in a turning
operation are based on the following performance characteristics: tool life, cutting force,
and surface roughness. Basically, tool life, cutting force, and surface roughness are
strongly correlated with cutting parameters such as cutting speed, feed rate, and depth of
cut (Nian et. al., 1998).
6
Turning constitutes the majority of lathe work. Thus, it was the process of machining
straight, conical surfaces, external cylindrical, curved and grooved workpieces. The
cutting tool was attached to the tool post, which is driven by the lead screw, and
removes material by travelling along the bed (Kalpakjian, 2006).
2.2 Parameters
In turning process, there have lot of parameters that will influence the process such as
tool geometry, cutting speed, depth of cut, etc. The consideration needed to make to
determine the effect of cutting parameters on the work piece. Lalwani et al. (2007) has
made an experimental investigation on the effect of cutting parameters such as cutting
speed, feed rate and depth of cut on the feed force, thrust force, cutting force and surface
roughness in finish hard turning of MDN250 (50 HRC) steel using coated ceramic tool.
They found that the cutting speed has no significant effect on cutting forces and surface
roughness. But before the researcher has stated that the cutting speed has greater
influence on roughness followed by the feed and the depth of cut has no significant
influence on the roughness according by Davim (2001). He also mentioned that the
interaction cutting velocity/feed is the most important of the other analyzed parameters.
Besides that, Nalbant et al. (2006) also found that the insert radius and feed rate are the
main parameters among of three controllable factors (insert radius, feed rate and depth
of cut) that influence the turning process. Noordin et al. (2003) has applied the RSM in
investigation into the effect of feed rate, side cutting edge angle (SCEA) and cutting
speed on the surface roughness and tangential force. He revealed that feed is the most
significant factor influencing the response followed by the SCEA. Moreover, a test were
conducted to find the effect of tool parameters such as insert shape, cutting edge
preparation, type and nose radius by Arunachalam et al. (2004). Another authors,
Dahlman et al. (2003) show that tool geometry as well as cutting parameters affects
residual stresses in the experiments. The authors also show that a negative rake angle
7
gives higher compressive stresses and feed was significantly higher compressive stresses.
But the cutting depth does not affect the residual stresses as well.
According to the past researches, there have lot of parameters such as cutting feed, feed
rate, depth of cut, nose radius, rake angle, insert material, etc. But cutting speed, feed
rate and depth of cut were considered as the most significant cutting parameters for
turning process.
2.2.1 Cutting Speed
In general, speed (V) was the primary cutting motion, which relates the velocity of the
rotating workpiece with respect to the stationary cutting tool. The cutting speed refers to
the edge speed of the rotating workpiece. It is generally given in unit of surface feet per
minute (sfpm) or inches per minute (in. /min), or meters per minute (m/min). For a given
material there will be an optimum cutting speed for a certain set of machining conditions,
and from this speed the spindle speed (RPM) can be calculated (Kalpakjian, 2006).
Factors affecting the calculation of cutting speed are:
i. The material being machined (steel, brass, tool steel, plastic, wood)
ii. The material the cutter is made from (Carbon steel, High speed steel(HSS),
carbide, ceramics)
iii. The economical life of the cutter (the cost to regrind or purchase new, compared
to the quantity of parts produced).
𝑉 = πDNs (1)
Where,
V = speed (m/min, in/min, m/rev)
Ns = Spindle speed (RPM)
D = diameter of the material