Industry 4.0, Smart Manufacturing, and Industrial Engineering : Challenges and Opportunities

Title: Industry 4.0, Smart Manufacturing, and Industrial Engineering : Challenges and Opportunities
Author: Amit Kumar Tyagi, Shrikant Tiwari
ISBN: 1032753277 / 9781032753270
Format: Hard Cover
Pages: 388
Publisher: CRC Press
Year: 2024
Availability: 15-30 days

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Industry 4.0 is a revolutionary concept that aims to enhance productivity and profitability in various industries through the implementation of smart manufacturing techniques. This book discusses the profound impact of Industry 4.0, which involves the seamless integration of digital technologies into manufacturing processes within the realm of industrial engineering.

Industry 4.0, Smart Manufacturing, and Industrial Engineering: Challenges and Opportunities thoroughly examines the intricate facets of Industry 4.0 and Smart Manufacturing, offering a comprehensive overview of the challenges and opportunities that this paradigm shift presents to industrial engineers. It provides practical insights and strategies to help professionals navigate the complexities of this evolving landscape. Fundamental components of Industry 4.0 and Smart Manufacturing, ranging from the incorporation of sensors and data analytics to the deployment of cyber-physical systems and the promotion of sustainable practices are covered in detail. The book addresses the obstacles and prospects brought about by Industry 4.0 in the digital age and offers solutions to issues such as data security, interoperability, and workforce preparedness.

The book sheds light on how Industry 4.0 combines various disciplines, including engineering technology, data science, and management. It serves as a valuable resource for researchers, undergraduate and postgraduate students, as well as professionals operating in the field of industrial engineering and related domains.

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Chapter 1 : Introduction to Industry 4.0
Chapter 2 : Security Concerns and Controls of Intelligent Cobots of Industry 4.0
Chapter 3 : Big Data Analytics (BDA) for Industry 5.0
Chapter 4 : Machine Learning—Enabled Predictive Analytics for Quality Assurance in Industry 4.0 and Smart Manufacturing: A Case Study on Red and White Wine Quality Classification
Chapter 5 : Leveraging Clustering Algorithms for Predictive Analytics in Blockchain Networks
Chapter 6 : Use of Digital Twin and Internet of Vehicles Technologies for Smart Electric Vehicles in the Manufacturing Industry
Chapter 7 : AI Applications in Production
Chapter 8 : IoT-Driven Supply Chain Management: A Comprehensive Framework for Smart and Sustainable Operations
Chapter 9 : Supply Chain Management in the Digital Age for Industry 4.0
Chapter 10 : Artificial Intelligence, Computer Vision and Robotics for Industry 5.0
Chapter 11 : Data Analytics and Decision-Making in Industry 4.0
Chapter 12 : Evolving Landscape of Industrial Engineering in the Modern Era
Chapter 13 : Artificial Intelligence (AI)-Enabled Digital Twin Technology in Smart Manufacturing
Chapter 14 : Smart Manufacturing: Navigating Challenges, Seizing Opportunities, and Charting Future Directions—A Comprehensive Review
Chapter 15 : Industry 4.0 in Manufacturing, Communication, Transportation, Healthcare
Chapter 16 : Artificial Intelligence-Based Anomaly Detection for Industry 4.0: A Sustainable Approach
Chapter 17 : Future of Industry 5.0 in Society 5.0: Human-Computer Interaction-Based Solutions for Next Generation
Chapter 18 : The Future of Manufacturing and Artificial Intelligence: Industry 6.0 and Beyond