Machine Learning for Sustainable Manufacturing in Industry 4.0
Concept, Concerns and Applications
1st Edition
Published on 3. November 2023
250 pages
978-1-000-98624-2 (ISBN)
Description
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.
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.
More details
Series
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Product notice
Reflowable
Illustrations
10 Tables, black and white; 9 Line drawings, black and white; 24 Halftones, black and white; 28 Illustrations, color; 5 Illustrations, black and white
ISBN-13
978-1-000-98624-2 (9781000986242)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Raman Kumar | Sita Rani | Sehijpal Singh Khangura
Machine Learning for Sustainable Manufacturing in Industry 4.0
Concept, Concerns and Applications
Book
06/2025
1st Edition
CRC Press
€76.20
Shipment within 10-20 days
Raman Kumar | Sita Rani | Sehijpal Singh Khangura
Machine Learning for Sustainable Manufacturing in Industry 4.0
Concept, Concerns and Applications
Book
11/2023
1st Edition
CRC Press
€186.20
Shipment within 10-20 days
Persons
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.
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.
Content
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
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
