ABSTRACT
This work
presents the assessment of aluminum alloy 356 with cow horn composite as a
machining material. In other to enable manufacturers to maximize their gains
from utilizing hard turning, an accurate model of the process must be
constructed. In course of this work, a mathematical model was developed to
relate the material removal rate (MRR), tool wear ratio (TWR) and surface
roughness (Ra) to machining parameters (feed rate, depth of cut and cutting
speed). To achieve this, A356/cow horn particles (CHp) composite was adopted
from Ochieze, 2017. A design of experiment was generated using the optimal
custom design techniques in Response Surface Methodology (RSM) from the Design
Expert Software 11.0. after the optimization, the results from the ANOVA tables
of the tool wear, surface roughness and material removal rate show some models
are significant with the probability value (P-value) 0.0203, 0.0412. The
results of the analysis of variance (ANOVA) indicate that the proposed
mathematical models, can adequately describe the performance within the limits
of the factors being studied. It was also observed that the cutting speed plays
a dominant role in tool wear rate (TWR) and surface roughness (Ra) while the
depth of cut has the least influence on the tool wear rate (TWR) and surface
roughness (Ra). Finally, the good surface quality with the minimum tool wear can
be achieved when cutting speed and feed rate are set nearer to their middle
level (900rpm, 0.25 rev/mm) and depth of cut is at high level of the
experimental range (1.5mm). In summary, in order to enable manufacturers to
maximize their gains in utilizing hard turning of AACHC, they should employ the
optimized cutting parameters.
Cover page PAGE
Title page i
Certification ii
Declaration iii
Dedication iv
Acknowledgement v
Table of Contents vi
List of Tables ix
List of Figures x
Nomenclatures xi
Abstract xii
CHAPTER 1: INTRODUCTION
1.1 Background of the Study 1
1.2 Statement of Problem 3
1.3 Aim and Objectives of Study 3
1.4 Scope of Study 4
1.5 Justification of the Study 4
CHAPTER 2: LITERATURE REVIEW
2.1 Machining Parameters and Their Effects 6
2.1.1
Feed Rate 6
2.1.2 Cutting Speed 17
2.1.3
Depth of Cut 23
2.2 Summary of Review 41
2.3 Research
Gap 42
CHAPTER 3: MATERIALS AND METHODS
3.1 Materials 43
3.2
Methods 43
3.2.1 Experimental
Procedure 43
3.2.2
Machining
operation 44
3.3 Design
of Experiment 45
CHAPTER 4: RESULTS AND DISCUSSION
4.1 Machining
Operation using Design of Experiment (DOE) 47
4.1.1 Surface Roughness 52
4.1.2 Material Removal Rate 56
4.1.3 Tool Wear 60
4.2 Effect of The Machining Parameters on the
Performance Measures 64
4.2.1 Effect of feed rate on surface roughness 64
4.2.2 Effect of feed rate on material removal rate 64
4.2.3 Effect of feed rate on tool wear 65
4.2.4 Effect of cutting speed on surface roughness 66
4.2.5 Effect of cutting speed on material removal
rate 66
4.2.6 Effect of cutting speed on tool wear 67
4.2.7 Effect of depth of cut on surface roughness 68
4.2.8 Effect of depth of cut on material removal
rate 68
4.2.9 Effect of depth of cut on tool wear 69
4.3 Optimization using RSM 70
CHAPTER 5: CONCLUSION AND RECOMMENDATIONS
5.1 Conclusion 71
5.2 Recommendations 72
5.3 Contributions to Knowledge 73
References 74
Appendix 80
LIST OF TABLES
PAGE
3.1 Independent
process variable and design level 45
3.2 Machining
design matrix and measured responses 45
4.1 Summary
data table of the actual design after experiment 47
4.2 Build
Information 48
4.3 Independent
process variable and design level 48
4.4 Responses 48
4.5 Model
terms of build information 49
4.6 Model
terms of surface roughness 52
4.7 Fit
statistics of surface roughness 52
4.8 Model
comparison statistics of surface roughness 52
4.9 Coefficients
in terms of coded factors of surface roughness 53
4.10 Model
terms of MRR 56
4.11 Fit
statistics of MRR 56
4.12 Model
comparison statistics of MRR 56
4.13 Coefficients
in terms of coded factors of MRR 57
4.14 Model
terms of tool wear 60
4.15 Fit
statistics of tool wear 60
4.16 Model
comparison statistics of tool wear 60
4.17 Coefficients
in terms of coded factors of tool wear 61
LIST OF FIGURES
PAGE
4.1 Fraction
of Design Space 49
4.2 Interaction
between the factors and the response 50
4.3 Perturbation
Plot 51
4.4 3D
graph for surface roughness 54
4.5 Graph
of predicted values and actual values of surface roughness 55
4.6 3D
graph for material removal rate 58
4.7 Graph
of predicted values and actual values of material removal rate 59
4.8 3D
graph for tool wear 62
4.9 Graph
of predicted values and actual values of tool wear 63
4.10 Effect of feed rate on surface roughness 64
4.11 Effect of feed rate on material removal
rate 65
4.12 Effect of feed rate on tool wear 65
4.13 Effect of cutting speed on surface
roughness 66
4.14 Effect of cutting speed on material removal
rate 67
4.15 Effect of cutting speed on tool wear 67
4.16 Effect of depth of cut on surface roughness 68
4.17 Effect of depth of cut on material removal rate 69
4.18 Effect of depth of cut on tool wear 69
4.19 Numerical
optimization 70
NOMENCLATURE
AACHC Aluminum Alloy Cow-horn Composite
AMMC
Aluminum Metal Matrix Composites
BBD Box-Behnken Design
BUE Built-up Edge
CBN Cubic Boron Nitride
CNC Computer Numerical Control
CS Cutting
Speed
CVD Chemical Vapor Deposition Diamond
DC Depth
of Cut
DOE Design of Experiment
FR Feed
Rate
HSS High-Speed
Steel
MMC Metal Matrix Composites
MRR Material
Removal Rate
OCD Optimal
Custom Design
PCD Poly Crystalline Diamond
RSM Response Surface Methodology
TWR Tool
Wear Rate
BETHEL, C (2023). Assessment Of Aluminum Alloy 356 With Cow Horn Composite As A Machining Material. Repository.mouau.edu.ng: Retrieved Nov 21, 2024, from https://repository.mouau.edu.ng/work/view/assessment-of-aluminum-alloy-356-with-cow-horn-composite-as-a-machining-material-7-2
CHIDIADI, BETHEL. "Assessment Of Aluminum Alloy 356 With Cow Horn Composite As A Machining Material" Repository.mouau.edu.ng. Repository.mouau.edu.ng, 04 Sep. 2023, https://repository.mouau.edu.ng/work/view/assessment-of-aluminum-alloy-356-with-cow-horn-composite-as-a-machining-material-7-2. Accessed 21 Nov. 2024.
CHIDIADI, BETHEL. "Assessment Of Aluminum Alloy 356 With Cow Horn Composite As A Machining Material". Repository.mouau.edu.ng, Repository.mouau.edu.ng, 04 Sep. 2023. Web. 21 Nov. 2024. < https://repository.mouau.edu.ng/work/view/assessment-of-aluminum-alloy-356-with-cow-horn-composite-as-a-machining-material-7-2 >.
CHIDIADI, BETHEL. "Assessment Of Aluminum Alloy 356 With Cow Horn Composite As A Machining Material" Repository.mouau.edu.ng (2023). Accessed 21 Nov. 2024. https://repository.mouau.edu.ng/work/view/assessment-of-aluminum-alloy-356-with-cow-horn-composite-as-a-machining-material-7-2