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
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
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
Ra Surface Roughness
RSM Response Surface Methodology
TWR Tool Wear Rate
CHAPTER 1
INTRODUCTION
1.1 BACKGROUND OF THE STUDY
Composite materials consist of two or more materials that differ in chemical and physical properties and are not soluble in one another. The primary constituent in a composite material is the matrix phase that provides load transfer and structural integrity, while the reinforcement to enhance mechanical properties. The matrix and reinforcement materials can either be organic (polymers), inorganic (ceramic or glass) or metallic (aluminum, titanium, etc.). The most common forms of reinforcement materials are fibers (long and short), or particulates.
Composite materials have superior specific properties (high strength to weight ratio) compared to metals, high stiffness and good damage resistance over a wide range of operating conditions. These make them attractive option in replacing conventional materials for many engineering applications. The important properties of composite materials are improved strength & stiffness, excellent fatigue resistance, high heat resistant, high wear resistant, high corrosion resistant, and low weight etc. By suitable arrangement of metal matrix and reinforcement addition, it is possible to obtain desired properties for application.
Matrix materials in Metal Matrix Composites (MMC) are aluminum, magnesium and titanium alloys. Reinforcing materials in MMC are silicon carbide, boron carbide, alumina and graphite in the form of particles, short fibers (whiskers) or long fibers. In Aluminum Metal Matrix Composites (AMMC), matrix material is aluminum and reinforcement materials are silicon carbide, aluminum oxide, boron carbide, graphite etc. in the form of fibers, whiskers & particles (Ahamed et al., 2019).
Metal cutting is one of the most significant processes in material removal. It has been recognized that quantitative predictions of various technological performance measures, preferably in the form of equations, are essential to develop the optimization strategies for selecting the cutting conditions in process planning. Machining is the key technology of engineering to produce various mechanical components and products. Turning is a conventional material removal process. The geometric and material capabilities of machining have been studied by various researchers. The industrial application of machining has been hindered by lack of experience and knowledge on the machinability of materials. Fabrication of various mechanical components requires reliable and repeatable methods with accurate tools. One of the common methods of manufacturing machine components is machining (Selvakumara and Ravikumar, 2014).
However, Aluminum metal matrix composites (AMMCs) are one of the most extensively used composite, due to its light weight and high strength. It is used in aerospace engines and automotive mainly in the development of disc brake. The progress in the development of predictive models, based on the cutting theory, is yet to be met. The most important cutting performance measures in turning are tool life, cutting force, roughness of the machined surfaces, and energy consumption (Selvakumara and Ravikumar, 2014). Hence, this study presents the assessment of aluminum alloy 356 with cow horn composite as a machining material.
1.2 STATEMENT OF PROBLEM
Aluminum metal matrix composites (AMMCs) are manufactured to near net shape, a need often exists for machining. Machining characteristics depends on the reinforcement material, type of reinforcement (particle or whisker), distribution of reinforcement in the matrix, and volume fraction of the reinforcement and matrix. Machining results of AMMC are different than metal machining due to presence of hard and brittle reinforcements. While machining tool encounters matrix and reinforcement materials alternatively, whose response to machining is entirely different. The main problem in machining AMMC is the high tool wear, which leads to an uneconomical production process or makes the process impossible. Thus, machining of composite materials imposes special demands on the geometry and wear resistance of the cutting tools.
1.3 AIM AND OBJECTIVES OF STUDY
The aim of the work is to assess aluminum alloy 356 with cow-horn composite as a machining material. However, the specific objectives are to;
i. Carry out machining operation on the developed composite.
ii. Predict the surface roughness, tool wear, material removal rate using regression equation model.
iii. Analyze the effect of machining parameters on performance measures in (ii) above.
iv. Optimize the cutting parameters using response surface methodology.
1.4 SCOPE OF STUDY
This study focuses on assessment of aluminum alloy 356 with cow horn composite as a machining material (AACHC). Hence,
i. Sample specimens was produced using AACHC.
ii. The AACHC adopted was from Ocheze (2017) work.
iii. Regression equation model was developed to predict surface roughness, tool wear and material removal rate.
iv. Effect of machining parameters on performance measures of AACHC were analyzed.
1.5 JUSTIFICATION OF THE STUDY
i. Help manufacturing industries in producing parts of high quality.
ii. Be of great benefit to researchers because it will prompt further study on AACHC and its other applications.
iii. Aid manufacturing industries in identifying the most effective machining parameter for HSS cutting tools in machining AACHC.
iv. Be a head start for the commercialization of the AACHC in Nigeria. It will help the nation to generate revenue.
v. Institutions on their own can imbibe the results from this research work and establish research centers to further research on these materials.
vi. A356 alloy are commonly used in automotive industry due to their good castability, high strength-to-weight ratio, and good corrosion resistance (Pan et al., 1990). Comparing their castability characteristics to other aluminum alloys, they have good resistance to hot cracking and good pressure tightness (Rooy, 1988).
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