Machine learning for sustainable manufacturing in industry 4.0 : concept, concerns and applications / edited by Raman Kumar, Sita Rani, and Sehijpal Singh Khangura.

Includes bibliographical references and index.

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 fourth-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, and scope.

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.

Description based on print version record.

Electronic reproduction. Ann Arbor, MI : ProQuest, 2018. Available via World Wide Web. Access may be limited to ProQuest affiliated libraries.