Soft Computing in Smart Manufacturing and Materials

1st Edition - January 20, 2025
Imprint: Elsevier
Editors: Sudan Jha, Shubhabrata Datta, Deepak Prashar, Sarbagya Ratna Shakya, Valentina Emilia Balas
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 2 9 9 2 7 - 8
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 2 9 9 2 8 - 5

Soft Computing in Smart Manufacturing and Materials explains the role of soft computing in the manufacturing industries. It presents the techniques, concepts and design princi… Read more

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Soft Computing in Smart Manufacturing and Materials explains the role of soft computing in the manufacturing industries. It presents the techniques, concepts and design principles behind smart soft computing, and describes how they can be applied in the development and manufacture of smart materials. It provides perspectives for design and commissioning of intelligent applications, including in health care, agriculture, and production assembly, and reviews the latest intelligent technologies and algorithms related to the methodologies of monitoring and mitigation of sustainable engineering.

Title of Book
Cover image
Title page
Table of Contents
Copyright
Contributors
Preface
Acknowledgments
Introduction
Chapter 1. Introduction to smart soft computing in engineering: Embracing intelligence and efficiency
1 What is smart soft computing?
2 Data processing and analysis
3 The role of soft computing in IIoT
4 The evolving landscape of engineering
5 Soft computing: A foundation for smart engineering
6 The rise of the Internet of Things
7 Industrial Internet of Things
8 Applications of smart soft computing in engineering
8.1 Manufacturing
8.2 Power and energy
8.3 Transportation
8.4 Civil engineering
8.5 Challenges and future directions
8.6 Conclusion
9 Case studies
9.1 Challenges and future directions
Chapter 2. An enhanced approach for soft computing-based manufacturing optimization using nonlinear intuitionistic fuzzy programming techniques
1 Introduction
1.1 Research gap
2 Fuzzy set terminology
2.1 Fuzzy set
2.2 Nonmembership function
2.3 Intuitionistic fuzzy set
2.4 α-cut
2.5 Height of fuzzy set
2.6 Normal fuzzy set
2.7 Fuzzy number
2.8 Triangular fuzzy number
2.9 Triangular intuitionistic fuzzy number
3 Methodology
4 Mathematical approach
4.1 Applying the distance parameter to membership and nonmembership (sigmoidal)
5 Illustrative example
5.1 By simple linear IFA
5.2 By normalized distance using linear IFA
5.3 By normalized distance using nonlinear IFA
6 Case study
7 Results and discussion
7.1 Role of computational program for multiobjective optimization in manufacturing
7.2 Comparative analysis
8 Conclusion
8.1 Future scope
Chapter 3. Components of smart materials
1 Introduction to smart materials
2 Classification of smart materials
3 Fundamentals of soft computing in smart materials
3.1 Application of soft computing in smart material development
3.2 Challenges and opportunities
4 Modeling and simulation techniques
4.1 Computational approaches for smart material behavior prediction
4.2 Finite element analysis in smart material design
4.3 Machine learning models for smart material behavior prediction
5 Design and fabrication methods
5.1 3D printing and additive manufacturing in smart material production
6 Applications of smart materials in manufacturing
6.1 Smart material-based actuators and sensors
6.2 Bio-inspired smart materials in manufacturing
7 Cutting-edge innovations
7.1 Nanotechnology: Nanoscale components in smart materials
7.2 AI and machine learning integration
8 Industry applications of smart materials
8.1 Limitations and opportunities in smart material implementation
9 Conclusion
Chapter 4. Recent trends and innovation in manufacturing with artificial intelligence/machine learning technologies
1 Introduction
2 An overview of AI and manufacturing engineering
2.1 Problems and challenges in manufacturing
2.2 Opportunities of AI in manufacturing
2.3 Suitability of artificial intelligence application with manufacturing challenges
2.4 AI as key to innovation in manufacturing
3 Artificial intelligence in additive manufacturing
4 Artificial intelligence in solidification process (casting)
5 Artificial intelligence in welding
6 Artificial intelligence in material removal process
7 Conclusion
Chapter 5. Next-gen distributed denial-of-service detection and mitigation in software-defined networking using hybrid machine learning approach
1 Introduction
2 SDN operations and threats
2.1 Topology in SDN
2.2 Packet forwarding
2.3 SDN layers and threats
2.4 Control layer
2.5 Data forwarding layer
2.6 Control channel
3 DDoS attack approach and analysis in SDN
3.1 Classification of DDoS attack
3.2 UDP flooding analysis
4 Machine learning
4.1 Evaluation parameters
5 Contribution
6 Research methodology
6.1 Data preparation and model evaluation
7 DDoS detection through machine learning techniques
7.1 Naïve Bayes
7.2 Logistic regression
7.3 Decision tree
7.4 Support vector classifier
7.5 Gradient boosting classifier
8 DDOS mitigation
8.1 Filtering network traffic
8.2 Redirect network traffic
9 Experimentation result and analysis
9.1 Log loss
10 Future scope
Chapter 6. Soft computing in manufacturing: Trends, innovations, and future prospects
1 Introduction
2 Cloud manufacturing defined by software
3 CMDS 'S reference architecture
4 Big data and industry 4.0
5 The development and meaning of SMSs
5.1 The development of intelligent production systems
5.2 Platforms for modeling and simulating industrial communities
5.3 Core concepts in intelligent manufacturing
5.4 Software-defined networking integration
5.5 The software-defined method
5.6 Explaining and modeling manufacturing processes
5.7 Flexibility of resources and virtualization
5.8 Intelligent production systems
5.9 Iterative enhancement and self-refinement mechanisms
5.10 The use of artificial intelligence with big data analytics
5.11 Edge-cloud collaboration processing
5.12 Privacy and security considerations
5.13 Ongoing education and adjustment
6 Implications of big data in 4.0 industries
6.1 Obtaining automation information
6.2 Converting data
6.3 Modeling and data collaboration
6.4 IoT data
6.5 Real-time access
6.6 Security and privacy
6.7 Data analytics
6.8 Data presentation
7 Big data applications in industry 4.0
7.1 BI, or business intelligence
7.2 Tolerance for faults
7.3 Improving product quality
7.4 Predicting machine health
7.5 Production Planning
7.6 Intelligent Urban areas
8 Three SMS requirements and objectives
8.1 Main goal
8.2 Main goal: Self-sufficient lean function
9 Four SMS components
9.1 The material level
9.2 Level of intelligent interconnection and communication
10 Research obstacles and prospects for the future
11 Conclusion
Chapter 7. Artificial intelligence in industrial applications
1 Introduction
2 Use case of industrial AI in manufacturing
2.1 Product counting
2.2 Supply chain management
2.3 Quality control/assurance
2.4 Predictive maintenance
2.5 Autonomous robotics/cobots
2.6 Warehouse management
2.7 Label detection
2.8 Foreign object detection
3 Industrial AI requirements
3.1 Trainability
3.2 Simulation-based training
3.3 Explainability
3.4 Safety and security of industrial AI system
3.5 Embedding industrial AI in existing system
4 AI applications in different industries
4.1 E-commerce
4.2 Financial services
4.3 Insurance
4.4 Healthcare
4.5 Life sciences
4.6 Telecommunications
4.7 Oil gas and energy
4.8 Aviation/navigation
4.9 Education
4.10 Agriculture
5 Conclusions
Chapter 8. Soft computing and system engineering for smart manufacturing
1 Introduction to soft computing and system engineering
2 Related work
3 Understanding smart manufacturing
4 The role of soft computing in smart manufacturing
5 Applications of soft computing in smart manufacturing
6 System engineering approaches for smart manufacturing
7 Integration of soft computing and system engineering in smart manufacturing
8 Advantages and challenges of implementing soft computing and system engineering in smart manufacturing
9 Case studies and success stories
9.1 Success story
10 Conclusion
Chapter 9. Application of ANFIS for predicting cutting forces, material removal rate, and surface roughness in dry grinding of Ti–6Al–4V alloy utilizing a 2% CNT nanogrinding tool
1 Introduction
2 ANFIS—Adaptive neuro-fuzzy inference system
2.1 ANFIS for material removal rates
2.2 ANFIS for surface roughness
2.2.1 ANFIS for tangential forces
2.2.2 ANFIS for normal forces
2.3 Summary
3 Conclusions
Chapter 10. Testing and analyzing genetic algorithms: Advancing ethical intelligence through integrating interpretability, human-centric design, and continuous learning in smart soft computing systems
1 Introduction
1.1 Interpretability
1.2 Human-centric design
1.3 Continuous learning
1.4 Ethical considerations
2 Related work
3 Proposed methodology
3.1 Interpretability
3.2 Human-centric design
3.3 Continuous learning
4 Experimental results
4.1 System performance
4.2 Interpretability
4.3 Continuous learning
4.4 Ethical considerations
5 Conclusion and future work
Chapter 11. Product life-cycle assessment using machine learning
1 Introduction
2 Literature review
3 Product life cycle assessment
4 Factors in product life cycle assessment
5 Machine learning approaches in product life cycle assessment
5.1 Neural network
5.2 Support vector machine
5.3 Random forest
5.4 Decision tree
6 Discussion and analysis
7 Conclusion and future recommendations
Chapter 12. Ultralow power communication: Challenges and machine learning solutions
1 Introduction
2 Challenges in ultralow power communication
3 Machine learning for power efficiency/machine learning solutions for ultralow power communication
4 Future scope
4.1 Efficient TinyML handwriting recognition deployment
4.2 TinySpeech: Efficient speech recognition on edge devices with TinyML
4.3 Enhancing autonomous mini vehicles with TinyML
5 Conclusion
Index

Sudan Jha

Sudan Jha holds a Ph.D. in CSE and Ph.D. in CE*, with 18 years of experience in Academia, Administration as a Principal in four Colleges in India and Nepal, and Industrial Research and Software Project Development & Management. As a team leader, Dr Jha executed three government-funded projects; sat on the Board of Directors and acted as Technical Advisor for Nepal's National Television (NTV); Chief IT Consultant in Nepal Telecom Authority, the regulating body for telecommunications in Nepal. Dr Jha has published 31 International Journal transaction papers [SCOPUS Indexed-3, UGC Approved-8, Others-20]; 30+ conference papers; 8 major software developed as Team Leader including Govt and non Govt. organization, and are a Editorial board member for several Journals, International Conferences, and books.

Affiliations and expertise

Professor of Computer Engineering, KIIT University, Bhubaneswar, India

Shubhabrata Datta

Dr. Shubhabrata Datta is a Research Professor in Mechanical Engineering of SRM Institute of Science and Technology, Kattanku lathur, Chennai, India. His research interest is in the domain of design of materials using artificial intelligence and machine learning techniques. Dr. Datta has more than 150 publications in journals and peer-reviewed conference proceedings. He was bestowed with the Exchange Scientist Award from Royal Academy of Engineering, UK and worked in the University of Sheffield, UK. He also worked in Dept of Materials Science and Engineering, Helsinki University of Technology, Finland, Dept of Materials Science and Engineering, Iowa State University, Ames, USA and Heat Engineering Lab, Dept of Chemical Engineering, Åbo Akademi University, Finland as Visiting Scientist. He is a Fellow of Institution of Engineers (India), Associate Editor, Journal of the Institution of Engineers (India): Series D, and editorial board member of several international journals.

Affiliations and expertise

Centre for Composites and Advanced Materials SRM Institute of Science and Technology, India

Deepak Prashar

Deepak Prashar received B. Tech in Computer Science and Engineering from Punjab Technical University, Pun- jab, India in 2007 and M. Tech. in Computer Science and Engineering from PEC University of Technology, Chandigarh, India in 2009. Presently he is working as a Professor and Coordinator of School of Computer Science & Engineering at Lovely Professional University, Punjab, India since 2009. He has completed his Ph. D. degree from Punjab Technical University (Punjab). He has published more than 90 research papers in reputed national, international conferences and journals, including SCOPUS and SCIE indexed journals. His research interest includes Wireless Sensor Networks, Soft Computing, Block chain, Machine Learning, IoT, Image Processing and Cyber security. He is involved in the review process of many SCI or SCIE indexed journals like IEEE, Springer, Wiley, Inderscience, etc. He is also the Editorial Board member of recognized journals and serving as a technical program committee member in reputed international conferences in India and abroad.

Affiliations and expertise

Associate Professor, School of Computer Science and Engineering, Lovely Professional University, Deemed University, India

Sarbagya Ratna Shakya

Sarbagya Ratna Shakya is working as an Assistant Professor at Eastern New Mexico University, New Mexico, USA. He received the B. Eng. in Electronics Engineering from National College of Engineering, Tribhuvan University of Nepal in 2009; M. Eng. in Computer Engineering from Nepal College of Information Technology, Pokhara University of Nepal in 2014. He has received his Ph. D in Computational Science (Computer Science) from the School of Computer Science and Computer Engineering, University of Southern Mississippi, USA. He has more than 10 years of teaching experience in undergraduate level courses. His research interest includes machine learning, deep learning, image processing, Internet of things and has published journals papers, conference papers, and book chapters in different domain on applications of machine learning and deep learning.

Affiliations and expertise

Assistant Professor, Eastern New Mexico University, New Mexico, USA

Valentina Emilia Balas

Valentina Emilia Balas is currently a Full Professor in the Department of Automatics and Applied Software at the Faculty of Engineering, “Aurel Vlaicu” University of Arad, Romania. She holds a PhD cum Laude in Applied Electronics and Telecommunications from the Polytechnic University of Timisoara. Dr. Balas is the author of more than 350 research papers. She is the Editor-in-Chief of the 'International Journal of Advanced Intelligence Paradigms' and the 'International Journal of Computational Systems Engineering', an editorial board member for several other national and international publications, and an expert evaluator for national and international projects and PhD theses.

Affiliations and expertise

Full Professor, Department of Automatics and Applied Software, Faculty of Engineering, "Aurel Vlaicu" University of Arad, Arad, Romania

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Soft Computing in Smart Manufacturing and Materials

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Sudan Jha

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Sarbagya Ratna Shakya

Valentina Emilia Balas

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