Evolutionary Optimization of Material Removal Processes

Description

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. 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