The text comprehensively focuses on the concepts, implementation, and application of evolutionary algorithms for predicting, modeling, and optimizing the various material removal processes from their origin to the current advancements. This one-of-a-kind book encapsulates all the features related to the application and implementation of evolutionary algorithms for the purpose of predicting and optimizing the process characteristics of different machining methods and their allied processes that will provide comprehensive information. It broadly explains the concepts of employing evolutionary algorithm-based optimization in a broad domain of various material removal processes. Therefore, this book will enable prospective readers to take full advantage of recent findings and advancements in the fields of traditional, advanced, micro, and hybrid machining, among others. Moreover, the simplicity of its writing will keep readers engaged throughout and make it easier for them to understand the advanced topics.
The book-
• Offers a step-by-step guide to implement evolutionary algorithms for the overall optimization of conventional and contemporary machining processes
• Provides in-depth analysis of various material removal processes through evolutionary optimization
• Details an overview of different evolutionary optimization techniques
• Explores advanced processing of various engineering materials-based case studies
It further discusses different nature-inspired algorithms-based modeling, prediction, and modeling of machining responses in attempting advanced machining of the latest materials and related engineering problems along with case studies and practical examples. It will be an ideal reference text for graduate students and academic researchers working in the fields of mechanical engineering, aerospace engineering, industrial engineering, manufacturing engineering, and materials science.
Author(s): Ravi Pratap Singh, Narendra Kumar, Ravinder Kataria, Pulak Mohan Pandey
Publisher: CRC Press
Year: 2022
Language: English
Pages: 250
City: Boca Raton
Cover
Half Title
Title Page
Copyright Page
Contents
Acknowledgments
Preface
Editors
Contributors
Introduction
Chapter 1: Experimental Investigation of Surface Roughness for Turning of UD-GFRP Composite Using PSO, GSA, and PSOGSA Techniques
1.1 Introduction
1.2 Literature Review
1.3 Experimental Procedure
1.4 Methodology
1.4.1 Taguchi Method
1.4.2 Multiple Regression Methodology
1.4.3 Gravitational Search Algorithm
1.4.4 Particle Swarm Optimization
1.4.5 Hybridized PSOGSA
1.5 Results and Discussion
1.5.1 Analysis of Variance
1.5.2 Multiple Regression Prediction Model
1.6 Optimization
1.6.1 Setting of Parameters for GSA
1.6.2 Setting of Parameters for PSO
1.6.3 Setting of Parameters for PSOGSA
1.7 Confirmation of Results
1.8 Conclusions
References
Chapter 2: Multi-Response Optimization During High-Speed Drilling of Composite Laminate Using Grey Entropy Fuzzy (GEF) and Entropy-Based Weight Integrated Multi-Variate Loss Function
2.1 Introduction
2.2 Materials and Methods
2.3 Results and Discussions
2.4 Optimization with Entropy Weight-Based Grey Relational Analysis
2.4.1 Grey Relational Analysis
2.4.2 Grey Relational Coefficient
2.4.3 Grey Relational Grade
2.4.4 Entropy Method
2.4.5 Optimisation with GREG
2.5 Optimisation Using Grey Entropy Fuzzy Method (GEFM)
2.5.1 Grey Entropy Fuzzy Model
2.6 Optimisation Using Entropy-Based Weight Integrated Multi-Variate Loss Function
2.7 Conclusions
References
Chapter 3: Implementation of Modern Meta-Heuristic Algorithms for Optimizing Machinability in Dry CNC Finish-Turning of AISI H13 Die Steel Under Annealed and Hardened States
3.1 Introduction
3.2 Experimental
3.2.1 Design of Experiments
3.2.2 Materials and Equipment
3.2.3 Experimental Results and Analysis
3.2.4 Statistical Analysis
3.3 Multi-objective Optimization Using Modern Meta-heuristics
3.4 Conclusions
Acknowledgments
References
Chapter 4: Multi-Response Optimization in Turning of UD-GFRP Composites Using Weighted Principal Component Analysis (WPCA)
4.1 Introduction
4.2 Experimental Study
4.2.1 Work Material, Cutting Tool, and Cutting Conditions
4.2.2 Response Variables
4.3 Weighted Principal Component Analysis
4.3.1 Weighted Principal Components Analysis
4.3.2 Single-Value Decomposition
4.4 Analysis and Evaluation of Experimental Results
4.5 Confirmation Experiment
4.6 Conclusions
Acknowledgment
References
Chapter 5: Processes Parameters Optimization on Surface Roughness in Turning of E-Glass UD-GFRP Composites Using Flower Pollination Algorithm (FPA)
5.1 Introduction
5.2 Literature Review
5.3 Experimental Concept
5.3.1 Fabrication of UD-GFRP Rod and Specification
5.3.2 Turning Process
5.4 Methodology
5.4.1 Design of Experiments
5.4.2 Multiple Regression Analysis
5.4.3 Flower Pollination Algorithm (FPA)
5.5 Optimization Using the Flower Pollination Algorithm and Taguchi Technique
5.6 Results and Conversation
5.6.1 Analysis of Variance
5.6.2 Multiple Regression Prediction Model
5.6.3 Optimization (FPA)
5.7 Confirmation of Results
5.8 Conclusions
Acknowledgments
References
Chapter 6: Application of ANN and Taguchi Technique for Material Removal Rate by Abrasive Jet Machining with Special Abrasive Materials
6.1 Introduction
6.2 Experimentation
6.2.1 Development of Experimental Set-Up
6.2.1.1 Frame
6.2.1.2 Nozzle and Mixing Chamber
6.2.1.3 FRL Unit
6.2.1.4 Funnel
6.2.1.5 Assembly for Movement of Nozzle
6.2.1.6 Mounting for Workpiece
6.2.1.7 Outer Cover
6.2.2 Methodology for Experimentation
6.2.2.1 Design and Parameters
6.3 Results & Discussion
6.3.1 Neural Network Methodology
6.3.2 Analysis of Single response
6.4 Conclusions
Acknowledgement
References
Chapter 7: Investigation of MRR in Face Turning Unidirectional GFRP Composites by Using Multiple Regression Methodology and an Artificial Neural Network
7.1 Introduction
7.2 Material and Methodology
7.2.1 Work Material
7.2.2 Experimental Details
7.2.3 Selection of Experimental Design
7.2.4 Multiple Regression Methodology
7.2.5 Artificial Neural Network
7.3 Results and Discussion
7.3.1 Multiple Regression Analysis
7.3.2 Artificial Neural Network
7.4 Conclusion
References
Chapter 8: Optimization of CNC Milling Parameters for Al-CNT Composites Using an Entropy-Based Neutrosophic Grey Relational TOPSIS Method
8.1 Introduction
8.2 Literature Review
8.3 Methodology
8.4 Results and Analysis
8.5 Conclusion
References
Chapter 9: Experimental Investigation of EDM Potential to Machine AISI 202 Using a Copper-Alloy Electrode and Its Modeling by an Artificial Neural Network
9.1 Introduction
9.2 Experimental Details
9.2.1 Details of the Tool Electrode
9.2.2 Workpiece Details
9.2.3 Design of Experiment
9.3 Characterization
9.4 Results and Discussion
9.4.1 Mathematical Representation of Different Responses
9.4.2 Analysis of Variance
9.4.3 EDM Machining Parameters Influence on MRR
9.4.4 Study of the Surface Alteration
9.5 Artificial Neural Network (ANN)
9.6 Conclusion
References
Chapter 10: Prediction and Neural Modeling of Material Removal Rate in Electrochemical Machining of Nimonic-263 Alloy
10.1 Introduction
10.1.1 Micro-Electrochemical Machining
10.1.2 Materials
10.2 Materials and Methods
10.3 Results and Discussion
10.3.1 Material Removal Rate for Means
10.3.2 Material Removal Rate for SN Ratios
10.3.3 Residual Plots for Means
10.3.4 Residual Plots for SN Ratios
10.4 Prediction of MRR Values through the ANN Model
10.4.1 Artificial Neural Network Model
10.5 Conclusion
References
Chapter 11: Optimization of End Milling Process Variables Using a Multi-Objective Genetic Algorithm
11.1 Introduction
11.2 Mathematical Model
11.3 Multi-Ojective Optimization
11.3.1 To Maximize
11.3.2 To Minimize
11.4 Experimentation
11.5 Results and Discussion
11.6 Conclusion
References
Appendix
Chapter 12: Micro-Electrochemical Machining of Nimonic 263 Alloy: An Experimental Investigation and ANN-Based Prediction of Radial Over Cut
12.1 Introduction
12.1.1 Micro-Electrochemical Machining
12.1.2 Materials
12.2 Materials and Methods
12.3 Results and Discussion
12.3.1 Radial Over Cut for Means
12.3.2 Radial Over Cut for SN Ratios
12.3.3 Residual Plots for Means
12.3.4 Residual Plots for SN Ratios
12.4 Prediction of Radial Over Cut (ROC) Values Using an Artificial Neural Network
12.4.1 Artificial Neural Network Model
12.5 Conclusion
References
Index
