Assessment Of Aluminum Alloy 356 With Cow Horn Composite As A Machining Material
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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.
TABLE OF CONTENTS
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
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APA
CHIDIADI, B., & MBA (2023). Assessment Of Aluminum Alloy 356 With Cow Horn Composite As A Machining Material. Michael Okpara University of Agriculture. Retrieved June 7, 2026, from http://repository.mouau.edu.ng/works/assessment-of-aluminum-alloy-356-with-cow-horn-composite-as-a-machining-material-7-2
MLA
CHIDIADI, BETHEL, and MBA. "Assessment Of Aluminum Alloy 356 With Cow Horn Composite As A Machining Material." Michael Okpara University of Agriculture, 4 Sep. 2023, http://repository.mouau.edu.ng/works/assessment-of-aluminum-alloy-356-with-cow-horn-composite-as-a-machining-material-7-2. Accessed June 7, 2026.
Chicago
CHIDIADI, BETHEL, and MBA. "Assessment Of Aluminum Alloy 356 With Cow Horn Composite As A Machining Material." Michael Okpara University of Agriculture (2023). Accessed June 7, 2026. http://repository.mouau.edu.ng/works/assessment-of-aluminum-alloy-356-with-cow-horn-composite-as-a-machining-material-7-2