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.
Author(s): Kaushik Kumar, Ganesh M. Kakandikar, Paulo Davim
Series: Woodhead Publishing Reviews: Mechanical Engineering Series
Publisher: Woodhead Publishing
Year: 2022
Language: English
Pages: 223
City: Cambridge
Front Cover
Computational Intelligence in Manufacturing
Copyright
Contents
Contributors
Preface
Chapter One: Multiverse multiobjective optimization of thinning and wrinkling in automotive connector
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
2.1. Introduction
2.2. Methodology
2.2.1. Mechanical-electrical methodology
2.2.2. Programmable ECS
2.2.3. Curved tool and workpiece material
2.3. Experimentation
2.3.1. Operation of the proposed mechanism
2.3.2. Pilot experimentation
2.3.3. Primary experimentation
2.3.4. Secondary experimentation
2.4. Results and discussion
2.4.1. Analysis of TWR and MRR
2.4.2. Dimensional analysis of curved hole machined
2.4.3. Analysis of surface roughness
2.4.4. Scanning electron microscopy (SEM) analysis
2.5. Conclusions
References
Chapter Three: Experimental and numerical investigation of deformation behavior of dual phase steel at elevated temperatu ...
3.1. Introduction
3.2. Experimental procedure
3.2.1. Material characterization
3.2.2. Tensile testing
3.3. Results and discussion
3.3.1. Experimental flow stress behavior
3.3.2. Constitutive models
3.3.2.1. Modified Johnson-Cook (m-JC) model
3.3.2.2. Modified Zerilli-Armstrong (m-ZA) model
3.3.2.3. Johnson-Cook and Zerilli-Armstrong (JC-ZA) model
3.3.3. Artificial neural network (ANN)
3.4. Conclusions
References
Chapter Four: Optimization of thermal efficiency of Scheffler solar concentrator receiver using slime mold algorithm
4.1. Introduction
4.2. Experimental setup
4.3. Experimentation for thermal efficiency
4.3.1. Design of experiments
4.3.2. Analysis
4.3.3. Analysis of variance (ANOVA)
4.3.4. General equation
4.4. Slime mold optimization algorithm
4.4.1. Originality
4.4.2. Concept and elicitation
4.4.3. Model based on mathematics
4.4.4. Approach toward food
4.4.5. Wrap food
4.4.6. Grab food
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
5.1. Introduction
5.1.1. Carbon nanomaterials
5.1.2. Polymer nanocomposite
5.2. Machining process (drilling) on laminated polymer nanocomposite
5.2.1. Machining-induced damages in laminated polymer nanocomposite
5.3. Machining process control through MCDM/algorithm approach
5.4. Drilling of laminated polymer nanocomposite: A case study
5.4.1. Mathematical model
5.4.2. Antlion optimization (ALO) algorithm
5.5. Summary of chapter
Chapter Five. References
Chapter Five. References
Chapter Five. References
References
Chapter Six: Machining performance analysis of micro-ED milling process of titanium alloy (Ti-6Al-4V)
6.1. Introduction
6.2. Experimental details-materials and methods
6.3. Results and discussion
6.3.1. Micro-ED milling process statistical analysis
6.3.2. Mathematical models equations for MRR, TWR, and SR
6.3.3. Model adequacy testing
6.3.4. Analysis of MRR
6.3.5. Analysis of TWR
6.3.6. Analysis of SR
6.4. Conclusions
References
Chapter Seven: Computational analysis of provisional study on white layer properties by EDM vs. WEDM of aluminum metal ma ...
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
7.7.1. Chemical elements of pure Al6061 perimental material
7.7.2. Experimental procedure
7.8. Machining setup
7.8.1. EDM machining
7.8.2. WEDM machining
7.9. Results and discussion
7.9.1. White layer and globules on EDM
7.9.2. White layer and globules on WEDM
7.9.3. Microhardness on EDM- and WEDM-machined samples
7.10. Conclusion
References
Chapter Eight: Scope of industry 4.0 components in manufacturing SMEs
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 algo ...
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
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
10.4.1. Dielectric method of moisture content determination
10.4.2. Neutron moderation management systems
10.5. Weeding management systems
10.6. Conclusion
10.7. Future scope
References
Index
Back Cover
