Nature-Inspired Optimization in Advanced Manufacturing Processes and Systems

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Foreword
Preface
Editors
Contributors
Chapter 1 Investigation on Process Parameters of EN-08 Steel by Using DoE and Multi-Objective Genetic Algorithm Approach
1.1 Introduction
1.2 Materials and Methodology
1.3 Results and Discussion
1.3.1 Rank Identification for Cutting Time (CT)
1.3.2 Optimal Solution for CT
1.3.3 Rank Identification for Surface Roughness (Ra)
1.3.4 Optimal Solution for RA
1.3.5 Contour Plot Analysis for Cutting Time and Surface Roughness
1.3.6 Interaction Plot for Cutting Time and Surface Roughness
1.3.7 Adequacy Check Analysis
1.3.8 Regression Modeling Equation
1.3.9 MOGA Optimization Technique
1.4 Conclusion
References
Chapter 2 Multi-Objective Optimization for Improving Performance Characteristics of Novel Curved EDM Process Using Jaya Algorithm
2.1 Introduction
2.2 Experimental Methodology
2.2.1 Design, Development and Operation of the Novel Curved EDM Mechanism
2.2.2 Experimental Investigation of Curved Machining Mechanism
2.2.3 Statistical Analysis for the Machining Responses Using Analysis of Variance
2.2.4 Multi-objective Optimization for the Optimum Machining Responses
2.2.5 Multiple Regression Analysis
2.3 Jaya Algorithm
2.4 Results and Discussion
2.5 Conclusions
References
Chapter 3 Artificial Neural Networks (ANNs) for Prediction and Optimization in Friction Stir Welding Process: An Overview and Future Trends
3.1 Friction Stir Welding (FSW) Process
3.1.1 FSW Process Parameters
3.2 Artificial Neural Networks (ANNs)
3.2.1 Applications of ANNs
3.3 ANN Utilization in Friction Stir Welding
3.4 Conclusion and Future Trends
Acknowledgements
References
Chapter 4 Energy-Efficient Cluster Head Selection for Manufacturing Processes Using Modified Honeybee Mating Optimization in Wireless Sensor Networks
4.1 Introduction
4.2 Literature Review
4.3 Proposed System
4.3.1 Honeybee Optimization (HBO)
4.3.2 Least Mean Squares (LMS) Classification
4.3.3 Mathematical Description of LMS and Its Variants
4.4 Implementation
4.4.1 Modified Honeybee Mating Optimization Algorithm
4.4.2 Simulation Parameters
4.5 Results and Discussion
4.6 Conclusion
Acknowledgment
References
Chapter 5 Multiobjective Design Optimization of Power Take-Off (PTO) Gear Box Through NSGA II
5.1 Introduction
5.2 Mathematical Formulation of Multiobjective Problems
5.3 Non-dominated Sorting Genetic Algorithm – NSGA II
5.4 Problem Statement of PTO Gear Box Design Optimization
5.4.1 Case Study
5.4.2 Objective Functions and Constraints
5.4.3 Design Variables
5.5 Problem Formulation for Optimization
5.5.1 Planetary Gear Design Optimization Formulation
5.5.2 Variable Bounds
5.5.3 Input Parameters
5.6 Results and Discussion
5.6.1 Condition for Proper Assembly
5.7 Conclusions
References
Chapter 6 Improving the Performance of Machining Processes Using Opposition-Based Learning Civilized Swarm Optimization
6.1 Introduction
6.2 Methodology
6.2.1 Particle Swarm Optimization
6.2.2 Society Civilization Algorithm
6.2.3 Civilized Swarm Optimization
6.2.4 Opposition-Based Learning Civilized Swarm Optimization
6.3 Application Examples
6.3.1 Optimization of Abrasive Water Jet Machining (AWJM) Process
6.3.2 Objective Function
6.3.2.1 Constraint
6.3.2.2 Variable Bounds
6.3.3 Results of Optimization of AWJM Process Using Opposition-Based CSO Algorithm
6.3.4 Optimization of CNC Turning Process
6.3.5 Results of Optimization of CNC Turning Process Using Opposition-Based CSO Algorithm
6.4 Conclusions
References
Chapter 7 Application of Particle Swarm Optimization Method to Availability Optimization of Thermal Power Plants
7.1 Introduction
7.2 System Description
7.2.1 Assumptions
7.2.2 Nomenclature
7.2.3 Availability Simulation Modeling of Thermal Power Plants
7.3 Results and Discussion of Markov-Based Analysis
7.4 Particle Swarm Optimization (PSO) to Optimize the Availability of TPPs
7.5 Conclusion
References
Chapter 8 Optimization of Incremental Sheet Forming Process Using Artificial Intelligence-Based Techniques
8.1 Introduction
8.2 Materials and Methods
8.2.1 Development of ANN Model to Predict Forming Force
8.2.2 Support Vector Machine (SVM) Model
8.2.3 Gaussian Process Regression (GPR) Model
8.3 Results and Discussion
8.3.1 Experimental Results and Analysis
8.3.2 Prediction of Axial Peak Forces Using AI Techniques
8.3.3 HLANN Used for Prediction of Maximum Axial Force
8.3.4 Comparison of the Estimated and Experimental Values of Axial Forces
8.4 Conclusions
References
Chapter 9 Development of Non-dominated Genetic Algorithm Interface for Parameter Optimization of Selected Electrochemical-Based Machining Processes
9.1 Introduction
9.2 Methodology
9.2.1 Non-dominated Sorting Genetic Algorithm – Graphical User Interface (NSGA-GUI)
9.3 Applications of NSGA-GUI in Advanced Machining Processes
9.3.1 Electrochemical Machining (ECM)
9.3.2 Electrochemical Micromachining (EMM)
9.3.3 Electrochemical Turning (ECT)
9.4 Conclusions
References
Chapter 10 ANN Modeling of Surface Roughness and Thrust Force During Drilling of SiC Filler-Incorporated Glass/Epoxy Composites
10.1 Introduction
10.2 Materials and Experimentation
10.2.1 Materials
10.2.2 Drilling Test
10.3 ANN Modeling and Prediction of Thrust Force and Surface Roughness
10.4 Results and Discussion
10.4.1 Experimental Results
10.4.2 Regression Analysis
10.4.3 ANN Modeling and Prediction
10.5 Conclusions
References
Chapter 11 Multi-objective Optimization of Laser-Assisted Micro-hole Drilling with Evolutionary Algorithms
11.1 Introduction
11.2 Formulation of the Problem
11.3 Use of Nature-Inspired Algorithms for Optimization
11.3.1 Genetic Algorithms
11.3.2 Particle Swarm Optimization (PSO)
11.4 Results and Discussion
11.4.1 GA Applied to Micro-hole Fabrication Using Laser Energy
11.4.2 PSO Applied to Micro-hole Fabrication Using Laser Energy
11.4.3 Comparison between GA and PSO
11.5 Conclusion
References
Chapter 12 Modeling and Pareto Optimization of Burnishing Process for Surface Roughness and Microhardness
12.1 Introduction
12.2 Motivation
12.3 Experiment Methodology and Model Development
12.3.1 Empirical Model Development for Surface Roughness and Microhardness
12.3.2 The Development of Pareto Front
12.3.3 Pareto Optimal Solution
12.4 Particle Swarm Optimization
12.4.1 Multi-objective Particle Swarm Optimization
12.4.2 Algorithm for MOPSO
12.4.2.1 Initialize the Population
12.4.2.2 Initialize the Velocity
12.4.2.3 Evaluation of the Fitness
12.4.2.4 Best Fitness and Position
12.4.2.5 Non-dominated Points
12.4.2.6 Generate Hypercube
12.4.2.7 Select Leader
12.4.2.8 Update Velocity
12.4.2.9 Mutation Operator
12.4.2.10 Maintain the Particles in Search Space
12.4.2.11 Update Repository
12.4.2.12 Update the Best Positions
12.4.3 MOPSO for Surface Roughness and Microhardness
12.5 Performance Assessment of the Pareto Front
12.5.1 Metrics Evaluating Closeness to the Pareto Front
12.5.2 Metrics Evaluating Diversity Among Non-dominated Solutions
12.6 Conclusions
References
Chapter 13 Selection of Components and Their Optimum Manufacturing Tolerance for Selective Assembly Technique Using Intelligent Water Drops Algorithm to Minimize Manufacturing Cost
13.1 Introduction
13.2 Related Research
13.2.1 Selective Assembly
13.2.2 Intelligent Water Drops Algorithm
13.2.3 Inference from the Past Works
13.2.4 Problem Background and Definition
13.3 Methodology
13.4 Numerical Illustration
13.5 Results and Discussion
13.6 Conclusion
References
Chapter 14 Enhancing the Surface Roughness Characteristics of Selective Inhibition Sintered HDPE Parts: An Integrated Approach of RSM and Krill Herd Algorithm
14.1 Introduction
14.2 Proposed Methodology
14.2.1 Response Surface Methodology
14.2.2 Krill Herd Algorithm
14.3 Experimental Details
14.4 Results and Discussion
14.4.1 Statistical Analysis of the Developed Models
14.4.2 Influence of Sintering Parameters on Roughness Characteristics
14.5 Multi-objective Optimization using Krill Herd Algorithm
14.6 Conclusion
Acknowledgement
References
Chapter 15 Optimization of Abrasive Water Jet Machining Parameters of Al/Tic Using Response Surface Methodology and Modified Artificial Bee Colony Algorithm
15.1 Introduction
15.2 Materials and Methods
15.3 Results and Discussion
15.3.2 Effect of Input Parameters on MRR
15.3.3 Effect of Input Parameters on SR
15.4 Bee Colony Algorithm
15.4.1 Proposed Modified ABC (MABC) Algorithm
15.4.2 Computational Procedure of the Proposed MABC Algorithm
15.5 Conclusions
References
Index