| ISBN13: | 9781032192703 |
| ISBN10: | 1032192704 |
| Binding: | Paperback |
| No. of pages: | 250 pages |
| Size: | 234x156 mm |
| Weight: | 380 g |
| Language: | English |
| Illustrations: | 9 Illustrations, black & white; 106 Illustrations, color; 70 Halftones, black & white; 45 Line drawings, black & white; 88 Tables, black & white |
| 661 |
Number theory
Applied mathematics
Engineering in general
Electrical engineering and telecommunications, precision engineering
Mechanical Engineering Sciences
Civil and construction engineering
Energy industry
Theory of computing, computing in general
Computer programming in general
Environmental sciences
Product design
Evolutionary Optimization of Material Removal Processes
GBP 45.99
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This text covers the latest machining processes using evolutionary optimization and comparative analysis with conventional optimization techniques. It will an ideal reference text for graduate students and academic researchers working in diverse engineering fields including mechanical, aerospace, industrial, manufacturing.
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.
Chapter 1
Experimental Investigation of Surface Roughness for Turning of UD-GFRP Composite Using PSO, GSA and PSOGSA Technique
Meenu and Surinder Kumar
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
Jalumedi Babu, Khaleel Ahmed, Lijo Paul, Abyson Scaria, and J. Paulo Davim
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
Nikolaos A. Fountas, Ioannis Papantoniou, John Kechagias, Dimitrios E. Manolakos, and Nikolaos M. Vaxevanidis
Chapter 4
Multi-response Optimization in Turning of UD-GFRP Composites using Weighted Principal Component Analysis (WPCA)
Meenu and Surinder Kumar
Chapter 5
Processes parameters optimization on surface roughness in turning of E-glass UD-GFRP composites using Flower Pollination Algorithm (FPA)
Surinder Kumar and Meenu
Chapter 6
Application of ANN and Taguchi technique for material removal rate by Abrasive Jet Machining with special abrasive materials
Sachin P. Ambade, Chetan K. Tembhurkar, Sagar Shelare and Santosh Gupta
Chapter 7
Investigation of MRR in face turning unidirectional GFRP composites by using Multiple Regression Methodology and Artificial Neural Network
Surinder Kumar, Meenu, and Pawan Kumar
Chapter 8
Optimization of CNC Milling Parameters for Al-CNT composites using Entropy based Neutrosophic Grey Relational TOPSIS Method
Sachchida Nand, Manvandra K Singh, and C M Krishna
Chapter 9
Experimental investigation of EDM potential to machine AISI 202 using Copper-alloy electrode and its modelling by Artificial Neural Network
Subhash Singh, and Girija Nandan Arka
Chapter 10
Prediction and Neural Modelling of Material Removal Rate in Electrochemical Machining of Nimonic 263 Alloy
Dilkush Bairwa, Dr Ravi Pratap Singh, Dr Ravinder Kataria, Dr Ravi Butola, Dr Mohd Javaid, Shailendra Chauhan, Madhusudan Painuly
Chapter 11
Optimization of End Milling Process Variables Using Multi Objective Genetic Algorithm
Jignesh Girishbhai Parmar, and Dr. Komal Ghanshyambhai Dave
Chapter 12
Micro-Electrochemical Machining of Nimonic 263 Alloy: An Experimental Investigation and ANN-based Prediction of Radial Over Cut
Dilkush Bairwa, Dr Ravi Pratap Singh, Dr Ravinder Kataria, Dr Sandeep Singhal, Dr Narendra Kumar, Shailendra Chauhan, and Madhusudan Painuly
