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Computational Intelligence in Manufacturing
- 1st Edition - May 28, 2022
- Editors: Kaushik Kumar, Ganesh M. Kakandikar, J. Paulo Davim
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 1 8 5 4 - 1
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 1 8 5 5 - 8
Computational Intelligence in Manufacturing addresses applications of AI, machine learning and other innovative computational techniques across the manufacturing supply chain. Th… Read more
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Request a sales quoteComputational Intelligence in Manufacturing addresses applications of AI, machine learning and other innovative computational techniques across the manufacturing supply chain. The rapid development of smart or digital manufacturing known as Industry 4.0 has swiftly provided a large number of opportunities for product and manufacturing process improvement. Selecting the appropriate technologies and combining them successfully is a challenge this book helps readers overcome . It explains how to prepare different manufacturing cells for flexibility and enhanced productivity with better supply chain management, e.g., calibrating design machine tools for automation and agility.
Computational intelligence applications for non-conventional manufacturing processes such as ECM and EDM are covered alongside recent advances in traditional processes like casting, welding and metal forming. As well as describing specific applications, this practical guide also explains the computational intelligence paradigm for enhanced supply chain management.
- Includes hot topics such as augmented and virtual reality applications in manufacturing
- Provides details of computational techniques, such as nature inspired algorithms for manufacturing process modeling
- Gives practical technical advice on how to calibrate processes and tools to work efficiently in an industry 4.0 system
Researchers, graduate students, and engineers working with industry 4.0, advanced manufacturing, robotics, and automation
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Preface
- Chapter One: Multiverse multiobjective optimization of thinning and wrinkling in automotive connector
- Abstract
- 1.1: Sheet-metal forming
- 1.2: Automotive component under study: Connector
- 1.3: Taguchi design of experiments
- 1.4: Numerical simulation results—Connector
- 1.5: Connector—Forming zone
- 1.6: Connector—Thickness distribution
- 1.7: Connector—Safety zone
- 1.8: Connector—Safety margin
- 1.9: Connector—Forming limit diagram
- 1.10: Analysis of variance for thinning—Connector
- 1.11: Analysis of variance for wrinkling—Connector
- 1.12: Linear regression analysis
- 1.13: Mathematical modeling—Connector
- 1.14: Problem formulation—Thinning and wrinkling
- 1.15: Multiobjective Multiverse optimization algorithm (MOMVO)
- 1.16: Multiobjective optimization
- 1.17: Conclusion
- References
- Chapter Two: An approach for machining curve cooling hole in plastic injection mold
- Abstract
- 2.1: Introduction
- 2.2: Methodology
- 2.3: Experimentation
- 2.4: Results and discussion
- 2.5: Conclusions
- References
- Chapter Three: Experimental and numerical investigation of deformation behavior of dual phase steel at elevated temperatures using various constitutive models and ANN
- Abstract
- 3.1: Introduction
- 3.2: Experimental procedure
- 3.3: Results and discussion
- 3.4: Conclusions
- References
- Chapter Four: Optimization of thermal efficiency of Scheffler solar concentrator receiver using slime mold algorithm
- Abstract
- 4.1: Introduction
- 4.2: Experimental setup
- 4.3: Experimentation for thermal efficiency
- 4.4: Slime mold optimization algorithm
- 4.5: Results and discussions
- 4.6: Conclusion
- References
- Chapter Five: Study on drilling behavior of polymer nanocomposites modified by carbon nanomaterial with fiber: A case study
- Abstract
- 5.1: Introduction
- 5.2: Machining process (drilling) on laminated polymer nanocomposite
- 5.3: Machining process control through MCDM/algorithm approach
- 5.4: Drilling of laminated polymer nanocomposite: A case study
- 5.5: Summary of chapter
- References
- Chapter Six: Machining performance analysis of micro-ED milling process of titanium alloy (Ti-6Al-4V)
- Abstract
- 6.1: Introduction
- 6.2: Experimental details-materials and methods
- 6.3: Results and discussion
- 6.4: Conclusions
- References
- Chapter Seven: Computational analysis of provisional study on white layer properties by EDM vs. WEDM of aluminum metal matrix composites
- Abstract
- 7.1: Introduction
- 7.2: Heat-affected zone (HAZ) on EDM
- 7.3: Heat-affected zone (HAZ) on WEDM
- 7.4: White layer on EDM and WEDM
- 7.5: Thermophysical model for EDM
- 7.6: Thermophysical model for WEDM
- 7.7: Methodology of composite making
- 7.8: Machining setup
- 7.9: Results and discussion
- 7.10: Conclusion
- References
- Chapter Eight: Scope of industry 4.0 components in manufacturing SMEs
- Abstract
- 8.1: Introduction
- 8.2: What is AI?
- 8.3: Difference between CI and AI
- 8.4: Principles of CI
- 8.5: Neural network
- 8.6: Deep learning
- 8.7: Probabilistic method for uncertain reasoning
- 8.8: Small and medium enterprises (SMEs)
- 8.9: Industry 4.0
- 8.10: SMEs and industry 4.0
- 8.11: Artificial intelligence
- 8.12: Barriers to adoption of computational intelligence in SMEs
- 8.13: Computational intelligence and SMEs
- 8.14: Model formulation (data layer/information layer/intelligence layer)
- 8.15: Model discussion
- 8.16: Applications of CI in SMEs
- 8.17: Conclusion
- References
- Chapter Nine: Process parameter optimization in manufacturing of root canal device using gorilla troops optimization algorithm
- Abstract
- 9.1: Introduction
- 9.2: Mathematical model
- 9.3: Gorilla troops optimization for sliding frictional force
- 9.4: Exploration phase
- 9.5: Exploitation phase
- 9.6: Results and discussion
- 9.7: Conclusion
- References
- Chapter Ten: A comprehensive review of agriculture irrigation using artificial intelligence for crop production
- Abstract
- 10.1: Introduction
- 10.2: Influence of artificial intelligence technology on an agriculture irrigation system
- 10.3: Embedded robotics and autonomous in agriculture
- 10.4: Smart irrigation systems in agriculture
- 10.5: Weeding management systems
- 10.6: Conclusion
- 10.7: Future scope
- References
- Index
- No. of pages: 224
- Language: English
- Edition: 1
- Published: May 28, 2022
- Imprint: Woodhead Publishing
- Paperback ISBN: 9780323918541
- eBook ISBN: 9780323918558
KK
Kaushik Kumar
Dr Kaushik Kumar is an Associate Professor in the Department of Mechanical Engineering, Birla Institute of Technology, Mesra, Ranchi, India. He has 14 years of experience in teaching and research, and over 11 years of industrial experience working for a global manufacturing company. He has 9 patents, has authored/edited 20 books and has 120 international journal publications, and 18 International and 8 National Conference publications to his credit.
Affiliations and expertise
Associate Professor, Department of Mechanical Engineering, Birla Institute of Technology, Mesra, Ranchi, IndiaGK
Ganesh M. Kakandikar
Ganesh Kakandikar is Professor and Associate Head of the School of Mechanical Engineering at Dr. Vishwanath Karad MIT World Peace University, Pune, India. He has an MBA in finance as well as qualifications in production engineering. He has a proven record of Research Publications and Citations, mainly in the areas of advanced manufacturing design and innovation.
Affiliations and expertise
Professor, Associate Head of the School of Mechanical Engineering, Dr. Vishwanath Karad MIT World Peace University, Pune, IndiaJD
J. Paulo Davim
J. Paulo Davim is Full Professor in the Department of Mechanical Engineering of the University of Aveiro, Portugal. He has more than 30 years of teaching and research experience in manufacturing, materials, and mechanical and industrial engineering, with special emphasis in machining and tribology. He has also an interest in management, engineering education, and higher education for sustainability.
Affiliations and expertise
Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal.