Evolutionary Optimization of Material Removal Processes
- 230 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Evolutionary Optimization of Material Removal Processes
About this book
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.
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Information
Table of contents
- Cover
- Half Title
- Title Page
- Copyright Page
- Contents
- Acknowledgments
- Preface
- Editors
- Contributors
- Introduction
- 1 Experimental Investigation of Surface Roughness for Turning of UD-GFRP Composite Using PSO, GSA, and PSOGSA Techniques
- 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
- 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
- 4 Multi-Response Optimization in Turning of UD-GFRP Composites Using Weighted Principal Component Analysis (WPCA)
- 5 Processes Parameters Optimization on Surface Roughness in Turning of E-Glass UD-GFRP Composites Using Flower Pollination Algorithm (FPA)
- 6 Application of ANN and Taguchi Technique for Material Removal Rate by Abrasive Jet Machining with Special Abrasive Materials
- 7 Investigation of MRR in Face Turning Unidirectional GFRP Composites by Using Multiple Regression Methodology and an Artificial Neural Network
- 8 Optimization of CNC Milling Parameters for Al-CNT Composites Using an Entropy-Based Neutrosophic Grey Relational TOPSIS Method
- 9 Experimental Investigation of EDM Potential to Machine AISI 202 Using a Copper-Alloy Electrode and Its Modeling by an Artificial Neural Network
- 10 Prediction and Neural Modeling of Material Removal Rate in Electrochemical Machining of Nimonic-263 Alloy
- 11 Optimization of End Milling Process Variables Using a Multi-Objective Genetic Algorithm
- 12 Micro-Electrochemical Machining of Nimonic 263 Alloy: An Experimental Investigation and ANN-Based Prediction of Radial Over Cut
- Index