v
2.2.4 Types of chip produced 20
2.2.4.1 Continuous chips 20
2.2.4.2 Built-up edge chips 22
2.2.4.3 Serrated chips 24
2.2.4.4 Discontinuous chips 24
2.2.5 Tool life 26
2.3 Cutting parameters 28
2.3.1 Cutting speed 28
2.3.2 Feed 29
2.3.3 Depth of cut 29
2.4 Taguchi method 30
3 METHODOLOGY 3.1 Objective of the experiment
35 3.2 Research flow chart
36 3.3 Design of experiment-Taguchi method
37 3.3.1 Variable machining parameter
37 3.3.2 Design of experiment matrix
37 3.3.3 Response variables
38 3.4 Material preparation
38 3.4.1 Workpiece
38 3.5 Cutting tool material
39 3.5.1 Coated carbide
39 3.6 Machine preparation
40 3.6.1 CNC lathe machine
40 3.7 Test and measurement
41 3.7.1 Surface roughness measurement
41 3.7.2 Eliminate error
44 3.8 Data analysis
44 3.9 Discussion on the result
45 3.10 Conclusion and recommendation
45
vi
4 RESULTS 4.1 Introduction
46 4.2 Measurement Results
46 4.3 D.O.E Results
47 4.3.1 Main Effects Plot for Means
48 4.3.2 Main Effects Plot for SN Ratios
51 4.3.3 Analysis of Variance for Means ANOVA
54 4.3.4 Predicted Value of Surface Roughness
55
5 DISCUSSION 5.1 Introduction
57 5.2 Surface Roughness Numerical Analysis
57 5.3 Surface Roughness D.O.E Graphical Analysis
60
6 CONCLUSION AND RECOMMENDATION 62
REFERENCES
63
APPENDICES A
vii
LIST OF TABLES
2.1 Mechanical properties of mild steel
8 2.2
Thermal properties of mild steel 9
2.3 Processability of mild steel
9 2.4
Durability of mild steel 10
2.5 Mechanical properties of cutting tool
12 2.6
Thermal properties of cutting tool 13
2.7 Processability of cutting tool
13 2.8
Durability of cutting tool 13
2.9 Factors influencing machining operations
15 2.10 Factors affecting surfaces roughness
18 2.11 Cutting speed for some common metals
29 2.12 Key features of review study
33 2.13 Comparing DOE and Taguchi methods
34 3.1
Variable machining parameters 37
3.2 Design of experiment matrix
38 3.3
The function of surface roughness measuring machine accessories 42
4.1 Surface roughness result for set A
46 4.2
Surface roughness result for set B 47
4.3 Response table for means for set A
49 4.4
Response table for means for set B 51
4.5 Response table for Signal to noise ratio for set A: Smaller is better
52 4.6
Response table for Signal to noise ratio for set B: Smaller is better 53
4.7 Predicted value of surface roughness
55 4.8
Factor levels for predictions 56
5.1 Controllable Parameter Setting
59 5.2
Optimum Parameter Effect 60
viii
LIST OF FIGURES
2.1 Basic metal cutting theory
11 2.2
Process schematic, turning, boring and parting operations performed on a lathe
14 2.3
Effect of feed rate and edge preparation on shearing force and ploughing force
17 2.4
Effect of feed rate and edge preparation on surface roughness of the machined workpiece
17 2.5
Measuring surface roughness with a stylus. The rider supports the stylus and guards against damage
19 2.6
Path of the stylus in surface roughness measurements broken line compared to the actual roughness profile
20 2.7
Continuous chip with narrow, straight and primary shear zone 21
2.8 Continuous chip with secondary shear zone at the chip-tool
interface 22
2.9 Built- up edge
23 2.10 Hardness distribution with a built-up edge in the cutting zone
23 2.11 Surface finish produced in turning 5130 steel with a BUE
23 2.12 Segmented or nonhomogeneous chip
24 2.13 Discontinuous chip
25 2.14 Flank wear
26 2.15 Crater wear
27 2.16 Chipped cutting edge
27 2.17 Thermal cracking on rake face
27 2.18 Built-up edge
27 2.19 Catastrophic failure
28 2.20 Schematic illustration of the turning operation showing various
features 30
3.1 Research flow chart
36 3.2
Workpiece 39
ix
3.3 Inserts Coated Carbide
39 3.4
CNC lathe machine 40
3.5 Portable roughness measuring machine
41 3.6
Portable roughness measuring accessories 42
3.7 Stylus
42 3.8
Surface roughness of mild steel bar 43
3.9 Surface roughness result
44 4.1
Main Effects Plot for set A 48
4.2 Main Effects Plot for set B
50 4.3
Main Effect Plot for Signal to Noise Ratios Graph for set A 51
4.4 Main Effect Plot for Signal to Noise Ratios Graph for set B
53 4.5
Analysis of Variance for Means 54
5.1 Average Ra value against number of sample bar graph
58
x
LIST OF ABBREVIATIONS
CNC
- Computer Numerical Control
UTeM -
Universiti Teknikal Malaysia Melaka PSM
- Projek Sarjana Muda
AISI -
American Iron and Steel Institute SAE
- Society of Automotive engineers
TiC -
Titanium carbide TiCN
- Titanium carbonitride
CVD -
Chemical vapour deposition PVD
- Physical vapour deposition
R
a -
Average surface roughness value BUE
- Built-up edge
PDE -
Parameter Design Experiment SN
- Signal-to-noise
MRR -
Material Removal Rate DOE
- Design of experiment
FKP -
Fakulti Kejuruteraan Pembuatan
1
CHAPTER 1
INTRODUCTION
1.1 Background
The challenge of modern machining industries is mainly focused on the achievement of high quality, in terms of work piece dimensional accuracy, surface finish, high
production rate, less wear on the cutting tools, economy of machining in terms of cost saving and increase the performance of the product with reduced environmental
impact Hasan, 2007. Surface roughness plays an important role in many areas and is a factor of great importance in the evaluation of machining accuracy Palanikumar,
2006. Surface roughness, an indicator of surface quality is one of the most specified customer requirements in a machining process. For efficient use of machine tools,
optimum cutting parameters cutting speed, feed rate and depth of cut are required. So it is necessary to find a suitable optimization method which can find optimum
values of cutting parameters for minimizing surface roughness. In turning operation, it is an important task to select cutting parameters for achieving
high cutting performance. Usually, the desired cutting parameters are determined based on experience or by use of hand book. But the ranges given these sources are
actually starting values and not the optimal values. However, this does not ensure that the selected cutting parameters have optimal or near optimal cutting performance
for a particular machine and environment Srikanth, 2008. Since turning is the primary operation in most of the production processes in the industry, surface finish
of turned components has greater influence on the quality of the product. Surface finish in turning has been found to be influenced in varying amounts by a number of
2 factors such as feed rate, work material characteristics, work hardness, unstable built-
up edge, cutting speed, depth of cut, cutting time, and tool nose radius.
The Taguchi method is statistical tool, adopted experimentally to investigate influence of surface roughness by cutting parameters such as cutting speed, feed rate
and depth of cut. The Taguchi process helps to select or determine the optimum cutting conditions for turning process. Many researchers developed many
mathematical models to optimize the cutting parameters to get lowest surface roughness by turning process. The variation in the material hardness, alloying
elements present in the work piece material and other factors affecting surface finish and tool wear. The Taguchi design of experiments was used to optimize the cutting
parameters and it is a powerful tool for the design of high quality systems. It provides simple, efficient and systematic approach to optimize designs for
performance, quality and cost. Taguchi method is efficient method for designing process that operates consistently and optimally over a variety of conditions. To
determine the best design it requires the use of a strategically designed experiment. Taguchi approach to design of experiments in easy to adopt and apply for users with
limited knowledge of statistic, hence gained wide popularity in the engineering and scientific community. The desired cutting parameters are determined based on
experience or by hand book. Cutting parameters are reflected on surface roughness, surface texture and dimensional deviation turned product. In a manufacturing process
it is very important to achieve a consistence tolerance and surface finish. Taguchi method is especially suitable for industrial use, but can also be used for scientific
research Hasan, 2007.
1.2 Problem statements
Cutting parameters and surface roughness of mild steel has to be studied in this project. In machining operation, the quality of surface finish is an important
requirement for many turned workpieces. This project presents a study of application of Taguchi method in the optimization of cutting parameter for surface roughness in
turning which allow it to be examined in more detail.
3 For this research study, it is targeted to find out the answer for the following
questions:
1. What are the roles of optimized cutting parameters cutting speed, feed rate and depth of cut of the turning process for controlling the required surface
roughness?
2. How Taguchi process helps to select or to determine the optimum cutting conditions for turning process?
3. How surface roughness plays an important role in many areas and is a factor of great importance in the evaluation of machining accuracy?
1.3 Objectives
The objectives of this study are as follow: 1. To find the optimal cutting parameters for surface roughness in turning.
2. To define the number of levels for the process parameters and possible
interaction between the process parameters. 3. To select the appropriate orthogonal array and assign of process parameters
for the orthogonal array.
1.4 Scope
This project will involve machining by CNC turning in UTeM machine shop. Mild steel will be used as workpiece and coated carbide as a cutting tool in this project.
This project will focused on cutting parameters and surface roughness in turning by Taguchi method.
4
1.5 Structure of the report
The summary of each chapter was described in the structure of report. The structure of the report includes Chapter 1 until Chapter 6 of the report.
1.5.1 Chapter 1: Introduction
