The book focuses on the recent developments in the areas of error reduction, resource optimization, and revenue growth in sustainable manufacturing using machine learning. It presents the integration of smart technologies such as machine learning in the field of Industry 4.0 for better quality products and efficient manufacturing methods.
The book focuses on the recent developments in the areas of error reduction, resource optimization, and revenue growth in sustainable manufacturing using machine learning. It presents the integration of smart technologies such as machine learning in the field of Industry 4.0 for better quality products and efficient manufacturing methods.
- Focusses on machine learning applications in Industry 4.0 ecosystem, such as resource optimization, data analysis, and predictions.
- Highlights the importance of the explainable machine learning model in the manufacturing processes.
- Presents the integration of machine learning and big data analytics from an industry 4.0 perspective.
- Discusses advanced computational techniques for sustainable manufacturing.
- Examines environmental impacts of operations and supply chain from an industry 4.0 perspective.
This book provides scientific and technological insight into sustainable manufacturing by covering a wide range of machine learning applications fault detection, cyber-attack prediction, and inventory management. It further discusses resource optimization using machine learning in industry 4.0, and explainable machine learning models for industry 4.0. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in the fields including mechanical engineering, manufacturing engineering, production engineering, aerospace engineering, and computer engineering.
Chapter 1
Machine Learning and Sustainable Manufacturing: Introduction, Framework and
Challenges
Chapter 2
Applications of Artificial Intelligence Across Industry 4.0
Chapter 3
ML Techniques for Analyzing Security Threats and Enhancing Sustainability in
Medical Field based on Industry 4.0
Chapter 4
Role of Machine Learning in Cyber-Physical Systems to Improve Manufacturing
Processes
Chapter 5
Environmental Impact of Operations and Supply Chain from Fourth Industrial
Revolution and Machine Learning Approaches
Chapter 6
Machine Learning for Resource Optimization in Industry 4.0 Eco-system
Chapter 7
Applications of Machine Learning in Smart Factory in 4th Generation
Industrial Environment
Chapter 8
Role of Machine Learning in Industry 4.0 Applications: A Review
Chapter 9
Supervised Learning Assisted Models for the Manufacturing of Sustainable
Composites
Chapter 10
Explainable Machine Learning Model for Industrial 4.0
Chapter 11
Applications of Machine Learning in the Manufacturing Sector: Concept,
Framework
Raman Kumar is presently working as an assistant professor, in the department of mechanical and production Engineering, Guru Nanak Dev Engineering College, Punjab, India. He has five years of industry, and sixteen of teaching and research experience. His areas of interest are sustainable manufacturing, energy-efficient machining, optimization of processes, and multi-criteria decision-making. He has taught courses including the strength of materials, machining sciences, manufacturing processes, operation research, industrial automation, and robotics. He has more than sixty research publications in national and international conferences and journals of repute.
Sita Rani is currently working as a professor, at the department of computer science engineering, at Gulzar Group of Institutions, Punjab, India. Her research interests are parallel and distributed computing, artificial intelligence, machine learning, bioinformatics, and the Internet of Things (IoT). She has published articles in renowned journals and conference proceedings and has more than thirty publications. She is an active member of ISTE and IEEE.
Sehijpal Singh is working as a principal, at Guru Nanak Dev Engineering College, Ludhiana, Punjab, India. He has twenty-six years of teaching as well as research experience. His areas of interest are non-conventional machining processes, energy-efficient machining, optimization of manufacturing processes, and decision-making. He has taught subjects like non-conventional machining, metal machining, and manufacturing processes. He has two patents, four books, and more than a hundred research publications in National and International journals of repute, publications appearing in the Journal of Cleaner Production, Materials and Manufacturing Processes, International Journal of Machine Tools and Manufacture, Journal of materials processing technology, and The International Journal of Advanced Manufacturing Technology.
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