Preface
Contents
About the Authors
1 Overview of Advanced Machining Process
1.1 Introduction of Overview of the Advanced Machining Process
1.2 Computer Numerical Control Machining
1.2.1 Working Principle of CNC Machining Operations
1.2.2 Advantages and Disadvantage of CNC
1.2.3 Milling Machining
1.2.4 Lathes Machining
1.2.5 Grinding Machining
1.2.6 Drilling Machining
1.3 Wire Electro Discharge Machining (WEDM)
1.3.1 Working Principle of Wire Electro Discharge Machining Operations
1.3.2 Advantages and Disadvantage of WEDM
1.4 Laser Beam Machining (LBM)
1.4.1 The Operation of Laser Beam Machining
1.4.2 Advantages and Disadvantage of Laser Beam Manufacturing
1.5 Abrasive Water Jet Machining (AWJM)
1.5.1 Working Principle of Abrasive Jet Machining Operations
1.5.2 Advantages and Disadvantage of AWJM
1.6 Electro Discharge Machining (EDM)
1.6.1 Working Principles of Electrical Discharge Machining
1.6.2 Advantages and Disadvantage of EDM
1.7 Conclusion
References
2 Cutting Fluid and Its Application with Different Delivering Machining Techniques
2.1 Introduction
2.2 Lubricant Delivery Techniques for Machining Operations
2.2.1 Flood Cooling Techniques Machining Operations
2.2.2 Solid Coolants/Lubricants Operation
2.2.3 Cryogenic Cooling in Machining Operation
2.2.4 MQL Machining Operations
2.2.5 Compressed Air Vapour Gas as Coolant
2.2.6 High-Pressure Lubrication in Machining Operations
2.3 Advantages and Disadvantages of the Lubricant Delivery Techniques
2.3.1 Advantages of Lubrication Delivery System
2.3.2 Disadvantages of Lubrication Delivery System
2.4 Machining Optimization
2.5 Conclusion
References
3 Development and Application of Nano-lubricant in Machining: A Review
3.1 Introduction
3.2 Different Methods of Nano-lubricant Preparations
3.3 Application of Nano-lubricant in Milling Machining
3.4 Turning Operations
3.5 Grinding Operations
3.6 Current Modern Optimization Techniques for Advanced Machining Processes
3.6.1 Response Surface Methodology
3.6.2 Box-Behnken Design
3.6.3 Central Composite Design
3.6.4 Taguchi Method
3.6.5 The Grey-Taguchi Multi-objective Optimization Method
3.6.6 Factorial Design
3.6.7 Full Factorial Design
3.6.8 Fractional Factorial Design
3.6.9 PlackettâBurman Design
3.7 Conclusion
References
4 Global Machining Prediction and Optimization
4.1 Introduction to Optimization in Machining
4.2 Artificial Neural Network (ANN) as a Global Prediction Technique
4.3 Adaptive Neuro-fuzzy Inference System
4.4 Particle Swarm Optimization (PSO)
4.5 Genetic Algorithm (GA)
4.6 Whale Optimization Algorithm
4.7 Ant Lion Optimization Algorithm (ALOA)
4.7.1 Random Walks of Ants
4.7.2 Trapping in the Pits of Antlions
4.7.3 Building Traps
4.7.4 Ants Sliding Toward the Antlion
4.7.5 Re-building the Pit and Catching Prey
4.7.6 Elitism
4.8 Grasshopper Optimization Algorithm (GOA)
4.9 Review of Related Literature of the Optimization Techniques in Machining Process
4.10 Conclusion
References
5 Multi-objective Grey Wolf Optimizer for Improved Machining Performance
5.1 Introduction
5.2 Grey Wolf Optimization (GWO) Background
5.3 Brief Description of Data Extraction and Processing
5.4 Formulation of Quadratic Equations and Mathematical Formulation
5.4.1 Design Variables and Objectives Functions
5.4.2 Multi-objective Optimization
5.5 Results and Discussion
5.6 Conclusion
References
6 Multi-objective Ant Lion Optimizer for Improved Machining Performance
6.1 Introduction
6.2 Ant Lion Optimization (ALO) Background
6.3 Brief Description of Data Extraction and Processing
6.4 Formulation of Quadratic Equations and Mathematical Formulation
6.4.1 Design Variables and Objectives Functions
6.4.2 Multi-objective Optimization
6.5 Results and Discussion
6.6 Conclusion
References
7 Multi-objective Grasshopper Optimizer for Improved Machining Performance
7.1 Introduction
7.2 Grasshopper Optimisation Algorithm (GOA) Background
7.3 Brief Description of Data Extraction and Processing
7.4 Formulation of Quadratic Equations and Mathematical Formulation
7.4.1 Design Variables and Objectives Functions
7.4.2 Multi-objective Optimization
7.5 Results and Discussion
7.6 Conclusion
References
8 A Multi-objective Optimization Approach for Improving Machining Performance Using the General Algebraic Modelling System (GAMS)
8.1 Introduction
8.2 Modelling with GAMS
8.3 Brief Description of Data Extraction and Processing
8.4 Formulation of Quadratic Equations and Mathematical Programming Models
8.4.1 Design Variables and Objectives Functions
8.4.2 Single Objective Optimization
8.4.3 Emphasising All Objective Components
8.5 Results and Discussion
8.5.1 Pareto Optimal Solutions
8.5.2 Parametric Analysis of the Solutions
8.6 Conclusion
References
9 ANN and QRCCD Prediction of Surface Roughness Under Biodegradable Nano-lubricant
9.1 Introduction
9.2 Methodology
9.2.1 Experimental Procedures for the Preparation of the Nano-lubricant and Machining Process
9.2.2 Mathematical Modelling Using Artificial Neural Network
9.2.3 Mathematical Modelling Using Quadratic Rotatable Central Composite Design
9.2.4 Validation of the Surface Roughness Results
9.3 The Experimental Data and Mathematical Model for Surface Roughness
9.3.1 The Results of Surface Roughness Obtained in Machining Operation
9.3.2 The QRCCD Model and the Prediction of the Surface Roughness
9.3.3 The Prediction of Surface Roughness Using ANN
9.4 The Comparison of Predicted Results Between ANN and QRCCD and the Machining Optimization
9.5 Study of the Machining Parameters Interaction for Optimal Performance
9.6 Conclusion
References
10 Cutting Force Optimization Under ANN and QRCCD
10.1 Introduction
10.2 Materials and Method
10.2.1 Mathematical Modelling Using Artificial Neural Network
10.2.2 Mathematical Modelling Using Quadratic Rotatable Central Composite Design
10.2.3 Validation of the Surface Roughness Results
10.3 Results and Discussion
10.3.1 The Cutting Force Models Prediction via QRCCD
10.3.2 The Prediction of the Cutting Force Using ANN for TiO2 Nano-lubricant
10.3.3 Validation and Comparison of the Predicted Result for Cutting Force from ANN and QRCCD Model
10.4 Parameters Interactions Study for Cutting Force
10.5 Conclusion
References
11 Material Removal Rate Optimization Under ANN and QRCCD
11.1 Introduction
11.2 Materials and Method
11.2.1 Artificial Neural Network (ANN) and Quadratic Rotatable Central Composite Design (QRCCD)
11.3 Results and Discussion
11.3.1 The Models and Prediction Analysis for MRR Using QRCCD
11.3.2 Material Removal Rate Prediction Using ANN
11.4 Comparative Analysis Between the ANN and QRCCD and Machining Optimization
11.5 Interactions Study Between the Machining Parameters for MRR
11.6 Conclusion
References
12 Application of Hybrid ANN and PSO for Prediction of Surface Roughness Under Biodegradable Nano-lubricant
12.1 Background
12.2 Modelling Approaches
12.3 Description of Dataset Extraction
12.4 Methodology
12.5 Results and Discussions
12.5.1 ANN Results
12.5.2 ANN-PSO Results
12.6 Conclusion
References
13 Adaptive Neuro-Fuzzy Inference System for Prediction of Surface Roughness Under Biodegradable Nano-lubricant
13.1 Background
13.2 Modelling Approach
13.3 Description of Dataset Extraction
13.4 Methodology
13.5 Results and Discussions
13.6 Conclusion
References
Appendix A
Appendix B
MATLAB Codes Grey Wolf Optimizer
Appendix C
MATLAB Codes Multi-Objective Grey Wolf Optimizer
Appendix D
MATLAB Codes Ant Lion Optimizer
Appendix E
MATLAB Codes Multi-Objective Ant Lion Optimizer (MALO)
Appendix F
MATLAB Codes Grasshopper Optimisation Algorithm (GOA)
Appendix G
MATLAB Codes Multi-Objective Grasshopper Optimization Algorithm (MOGOA)
Appendix H
GAMS Code: AUGMENCON2
Appendix I
ANN Model
Appendix J
ANN-PSO Model