Product Details
ISBN 9780367532604
November 17, 2020 - hardback
282 Pages 120 B/W Illustrations
The manufacturing system is going through substantial changes and developments in light of industry 4.0. Newer manufacturing technologies are being developed and applied. There is need to optimize these techniques when applied in different circumstances with respect to materials, tools, product configurations, and process parameters.
This book covers computational intelligence applied to manufacturing. It discusses nature inspired optimization of processes and the design and development with manufacturing systems. It will explore all manufacturing processes including macro and micro levels and offers manufacturing philosophies. Non-conventional manufacturing, real industry problems and case studies, research on generative processes and how everything is relevant to Industry 4.0 will be also be included.
Researchers, students, academicians, and industry professionals will find this reference title very useful.
Table of Contents
Chapter 1
Investigations on Process Parameters of EN-08 Steel by Using DoE and Multi Objective Genetic Algorithm Approach
Syed Anjum Alam, Ashish Goyal, Manish Dadhich
Chapter 2
Multi Objective Optimization for Improving Performance characteristic of Novel Curved EDM Process using Jaya Algorithm
Diwesh B. Meshram, Yogesh M. Puri and Neelesh Kumar Sahu
Chapter 3
Artificial neural networks (ANNs) for prediction and optimization in friction stir welding process: An overview and future trends
Mukuna Patrick Mubiayi and Veeredhi Vasudeva Rao
Chapter 4
Energy Efficient Cluster Head Selection for Manufacturing Process Using Modified Honey Bee Mating Optimization in Wireless Sensor Networks
Pramod D Ganjewar, Barani S., Sanjeev J. Wagh
Chapter 5
Multiobjective design optimization of Power Take off gear box through Non-Dominated Sorting Genetic Algorithm - II
R. Saravanan, G. Chandrasekaran and V. S. Sree Balaji
Chapter 6
Improving the performance of machining processes using Opposition based learning civilized swarm optimization
Chapter 7
Application of particle swarm optimization method for availability optimization of thermal power plant
Hanumant P. Jagtap, Anand K. Bewoor, Firozkhan Pathan, Ravinder Kumar
Chapter 8
Optimization of Incremental Sheet Forming Process using Artificial Intelligence Based Techniques
Ajay Kumar, Deepak Kumar, Parveen Kumar, Vikas Dhawan
Chapter 9
Development of non-dominated genetic algorithm interface for parameter optimization of selected electrochemical based machining processes
D. Singh and R. S. Shukla
Chapter 10
Ann Modelling of Surface Roughness and Thrust Force During Drilling of Sic Filler Incorporated Glass/Epoxy Composites
Ajith G. Joshi, M. Manjaiah, R. Suresh, Mahesh B. Davangere
Chapter 11
Multi-Objective Optimization of Laser-assisted Micro-hole Drilling with Evolutionary Algorithms
Hrudaya Jyoti Biswal, V Pandu Ranga, Ankur Gupta
Chapter 12
Modelling and Pareto Optimization of Burnishing Process for Surface Roughness and Microhardness
Vijay Kurkute and Sandip Chavan
Chapter 13
Selection of Components and its Optimum Manufacturing Tolerance for Selective Assembly Technique using Intelligent Water Drops Algorithm to Minimize Manufacturing Cost
M. Siva Kumar, N. Lenin and D. Rajamani
Chapter 14
Enhancing the surface roughness characteristics of selective inhibition sintered HDPE parts: An integrated approach of RSM and krill herd algorithm
D. Rajamani*, E. Balasubramanian and M. Sivakumar
Chapter 15
Optimization of Abrasive Water Jet Machining Parameters of Al/Tic Using Response Surface Methodology and Modified Artificial Bee Colony Algorithm
K. Kiran, K. Ravi Kumar and K. Chandrasekar