Machine Intelligence in Mechanical Engineering

1st Edition - January 16, 2024
Editors: K. Palanikumar, Elango Natarajan, S. Ramesh, J. Paulo Davim
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 1 8 6 4 4 - 8
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 1 8 6 4 5 - 5

Machine Intelligence in Mechanical Engineering explains the latest applications of machine intelligence and data-driven decision-making in mechanical engineering industrie… Read more

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Machine Intelligence in Mechanical Engineering explains the latest applications of machine intelligence and data-driven decision-making in mechanical engineering industries. By providing introductory theory, trouble-shooting case studies, detailed algorithms and implementation instructions, this interdisciplinary book will help readers explore additional applications in their own fields. Those with a mechanical background will learn the important tasks related to preprocessing of datasets, feature extraction, verification and validation of machine learning models which unlock these new methods.

Machine Intelligence is currently a key topic in industrial automation, enabling machines to solve complex engineering tasks and driving efficiencies in the smart production line. Smart preventative maintenance systems can prevent machine downtime, smart monitoring and control can produce more effective workflows with less human intervention.

Cover image
Title page
Table of Contents
Copyright
List of contributors
Preface
Chapter 1. Machine intelligence in mechanical engineering: an introduction
Abstract
1.1 Introduction
1.2 Machine intelligence in mechanical engineering
1.3 Conclusion
Funding
References
Chapter 2. A smart production line management system using face recognition and augmented reality
Abstract
2.1 Introduction
2.2 Literature review
2.3 Review of similar work
2.4 Methodology
2.5 Result
Acknowledgment
References
Further reading
Chapter 3. Maintenance planning optimization through equipment performance prediction using machine learning based on inline instrument datasets—a surface condenser case study
Abstract
3.1 Introduction
3.2 Background
3.3 Research methodology
3.4 Results and discussion
3.5 Conclusion and future works
References
Chapter 4. Minimizing intercellular movement of parts and maximizing the utilization of machines using the correlation index-based clustering algorithm
Abstract
4.1 Introduction
4.2 Correlation index-based clustering algorithm for the GT problem
4.3 Illustration of correlation index-based clustering algorithm heuristic for machine-part cell formation
4.4 Conclusion
References
Chapter 5. Application of augmented reality and virtual reality technologies for maintenance and repair of automobile and mechanical equipment
Abstract
5.1 Introduction
5.2 Literature survey
5.3 Working of reality technologies
5.4 Case study I
5.5 Case study II
5.6 Conclusion
References
Chapter 6. Application of machine vision technology in manufacturing industries—a study
Abstract
6.1 Introduction
6.2 What does the term “machine vision” truly entail?
6.3 Applications within the industrial and commercial sectors
6.4 Future developments
6.5 Contribution of machine vision towards Industry 4.0
6.6 The Collaborative Functions of Machine Vision in Manufacturing Industry
6.7 Implementation of intelligent technologies for machine vision
6.8 Utilizing machine vision to increase production
6.9 Integrating machine vision in Industry 4.0
6.10 Important things to consider for both the design and the applications
6.11 Discussion
6.12 The path that machine vision will take in future
6.13 Conclusion
References
Chapter 7. Estimation of wing stall delay characteristics with outward dimples using numerical analysis
Abstract
7.1 Introduction
7.2 Review of literature
7.3 Methodology
7.4 Computer-aided design models
7.5 Grid independence study
7.6 Results and discussion
7.7 Conclusion
References
Chapter 8. An Internet of Things-based integrative safety framework of autonomous vehicles for special needs society
Abstract
8.1 Introduction
8.2 Background study
8.3 An integrated framework for safer navigation and independent mobility of autonomous vehicle for special needs society
8.4 Conclusion and future directives
References
Chapter 9. Motion planning and control for autonomous vehicle collision avoidance systems using potential field-based parameter scheduling
Abstract
9.1 Introduction
9.2 Development of adaptive motion planning and control strategy for vehicle collision avoidance systems
9.3 Results and discussion
9.4 Hardware in loop implementation: experimental verification
9.5 Conclusion
Acknowledgment
References
Chapter 10. Long-term predictive maintenance system with application and commercialization to industrial conveyors
Abstract
10.1 Introduction
10.2 Literature review
10.3 Methodology
10.4 Results
10.5 Conclusion
Acknowledgments
References
Chapter 11. Predicting the mechanical behavior of carbon fiber-reinforced polymer using machine learning methods: a systematic review
Abstract
11.1 Introduction
11.2 Systematic review methodology
11.3 Systematic search results and literature review
11.4 Conclusion
Acknowledgments
Appendix A
References
Chapter 12. Application of computationally intelligent modeling to glass fiber reinforced polymer drilling
Abstract
12.1 Introduction
12.2 Experimental research
12.3 Taguchi methodology application
12.4 Modeling using adaptive neuro-fuzzy inference systems
12.5 Conclusions
References
Further reading
Chapter 13. Applied advanced analytics in marketing of mechanical products
Abstract
13.1 Introduction
13.2 Negative sentiment analysis
13.3 Other applications: product taxonomy
13.4 Deployment and operationalizing machine learning models
13.5 Final words
References
Chapter 14. Information and communication technologies: enablers for the successful implementation of supply chain 4.0
Abstract
14.1 Introduction
14.2 Recent literature
14.3 Identification of enablers
14.4 Framework for implementation
14.5 Conclusion and future scope
References
Chapter 15. A pilot study and development of prediction model for tire compound quality
Abstract
15.1 Introduction
15.2 Material and method of model development
15.3 Conclusion
Acknowledgment
References
Chapter 16. Machine intelligence based learning for ecological transportation
Abstract
16.1 Introduction
16.2 Objectives
16.3 Problem statement
16.4 Literature review
16.5 Methodology
16.6 Results
16.7 Discussions
16.8 Conclusion
Acknowledgment
References
Chapter 17. A review on the social impacts of automation on human capital in Malaysia
Abstract
17.1 Introduction
17.2 Conclusion
References
Chapter 18. Autonomous systems with intelligent agents
Abstract
18.1 Introduction
18.2 Theoretical design
18.3 Link length optimization
18.4 Simulations
18.5 Conclusion
References
Chapter 19. Human-like driver model for emergency collision avoidance using neural network autoregressive with exogenous inputs
Abstract
19.1 Introduction
19.2 Methodology
19.3 Modeling by neural network autoregressive with exogenous input
19.4 Result and discussion
19.5 Conclusion
References
Chapter 20. Secure cloud web application in an industrial environment: a study
Abstract
20.1 Introduction
20.2 Architecture
20.3 Literature survey
20.4 Implementation
20.5 Results and discussions
20.6 Conclusion
References
Further reading
Chapter 21. Deep learning applied solid waste recognition system targeting sustainable development goal
Abstract
21.1 Introduction
21.2 Methods
21.3 Results and discussions
21.4 Conclusion
References
Index

K. Palanikumar

K. Palanikumar is a professor and principal at Sri Sai Ram Institute of Technology, Chennai, India. He has more than 25 years of experience in teaching and research. He received a “National Best Researcher Award” from ISTE and published more than 100 papers in SCI Journals.

Affiliations and expertise

Professor and Principal, Sri Sairam Institute of Technology, Chennai, Tamil Nadu, India

Elango Natarajan

Elango Natarajan is a chartered mechanical engineer (CEng.), who specialized in mechanical engineering design, CAE, optimization, and soft robotics. He has worked for engineering colleges/universities for over 20 years in various academic positions.

Affiliations and expertise

Department of Mechanical Engineering, Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur, Malaysia

S. Ramesh

Dr. S. Ramesh is an Assistant Professor (SS) in the Department of Mechatronics Engineering, Rajalakshmi Engineering College, Thandalam, Chennai. He has completed his Ph.D. degree in Embedded Systems/Machine Learning from VIT University, Chennai in 2020, an M. Tech. Degree in Embedded Systems from SRM University, Chennai, Tamilnadu, India in 2011, B.E. Degree from National Engineering College, Kovilpatti, Tamilnadu, India in 2008. In addition to this, he is currently doing Post Doctoral Research in Malaysia. He has over 13 years of Teaching and Research Experience at various Universities and Engineering Colleges around India.

Affiliations and expertise

Rajalakshmi Engineering College, Chennai

J. Paulo Davim

Prof. (Dr.) J. Paulo Davim is a Full Professor at the University of Aveiro, Portugal, with over 35 years of experience in Mechanical, Materials, and Industrial Engineering. He holds multiple distinguished academic titles, including a PhD in Mechanical Engineering and a DSc from London Metropolitan University. He has published over 300 books and 600 articles, with more than 36,500 citations. He is ranked among the world's top 2% scientists by Stanford University and holds leadership positions in numerous international journals, conferences, and research projects.

Affiliations and expertise

Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal

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Machine Intelligence in Mechanical Engineering

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K. Palanikumar

Elango Natarajan

S. Ramesh

J. Paulo Davim