Computational 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.
1. Computational Intelligence in Manufacturing - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iv 5. Copyright - p. Page v 6. Contributors - p. Page vi 7. Preface - p. Page vi 8. List of Illustrations - p. Page xi 9. List of Tables - p. Page xii 10. Chapter One : Multiverse multiobjective optimization of thinning and wrinkling in automotive connector - p. Page xii 11. Chapter Two : An approach for machining curve cooling hole in plastic injection mold - p. Page 19 12. Chapter Three : Experimental and numerical investigation of deformation behavior of dual phase steel at elevated temperatures using various constitutive models and ANN - p. Page 43 13. Chapter Four : Optimization of thermal efficiency of Scheffler solar concentrator receiver using slime mold algorithm - p. Page 66 14. Chapter Five : Study on drilling behavior of polymer nanocomposites modified by carbon nanomaterial with fiber: A case study - p. Page 83 15. Chapter Six : Machining performance analysis of micro-ED milling process of titanium alloy (Ti-6Al-4V) - p. Page 104 16. Chapter Seven : Computational analysis of provisional study on white layer properties by EDM vs. WEDM of aluminum metal matrix composites - p. Page 124 17. Chapter Eight : Scope of industry 4.0 components in manufacturing SMEs - p. Page 156 18. Chapter Nine : Process parameter optimization in manufacturing of root canal device using gorilla troops optimization algorithm - p. Page 172 19. Chapter Ten : A comprehensive review of agriculture irrigation using artificial intelligence for crop production - p. Page 184 20. Index - p. Page 196
Computational 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.
1. Computational Intelligence in Manufacturing - p. Page i 2. Cover - p. Page i 3. Front Matter - p. Page i 4. Table of Contents - p. Page iv 5. Copyright - p. Page v 6. Contributors - p. Page vi 7. Preface - p. Page vi 8. List of Illustrations - p. Page xi 9. List of Tables - p. Page xii 10. Chapter One : Multiverse multiobjective optimization of thinning and wrinkling in automotive connector - p. Page xii 11. Chapter Two : An approach for machining curve cooling hole in plastic injection mold - p. Page 19 12. Chapter Three : Experimental and numerical investigation of deformation behavior of dual phase steel at elevated temperatures using various constitutive models and ANN - p. Page 43 13. Chapter Four : Optimization of thermal efficiency of Scheffler solar concentrator receiver using slime mold algorithm - p. Page 66 14. Chapter Five : Study on drilling behavior of polymer nanocomposites modified by carbon nanomaterial with fiber: A case study - p. Page 83 15. Chapter Six : Machining performance analysis of micro-ED milling process of titanium alloy (Ti-6Al-4V) - p. Page 104 16. Chapter Seven : Computational analysis of provisional study on white layer properties by EDM vs. WEDM of aluminum metal matrix composites - p. Page 124 17. Chapter Eight : Scope of industry 4.0 components in manufacturing SMEs - p. Page 156 18. Chapter Nine : Process parameter optimization in manufacturing of root canal device using gorilla troops optimization algorithm - p. Page 172 19. Chapter Ten : A comprehensive review of agriculture irrigation using artificial intelligence for crop production - p. Page 184 20. Index - p. Page 196
