This book offers a foundational understanding of smart manufacturing (SM) and introduces effective AI methods tailored for smart manufacturing, including supervised, unsupervised, and reinforcement learning techniques. It also features real-world industrial case studies that demonstrate the practical applications of smart manufacturing.

Drawing from the invaluable experiences gleaned from the aviation, healthcare, and semiconductors industries, this book provides an in-depth understanding of how AI is driving transformative changes in the manufacturing landscape.

In the era of rapid technological advancements, the integration of AI into manufacturing processes has emerged as a game-changer. This book serves as an indispensable guide for navigating this transformation, presenting readers with a multidimensional perspective on the diverse applications, challenges, and opportunities that AI brings to the manufacturing sector.

The book explores the emergence of Large Language Models (LLMs) as a valuable tool in manufacturing. It presents how LLMs, especially the GPT series, can process and generate textual data, offering potential applications in areas like smart manufacturing and big-data analysis. It contains detailed case studies, illustrating the practical implementation of smart manufacturing in different industries. The aviation, healthcare, automotive, and semiconductors sectors are examined, highlighting tangible benefits, challenges faced, and lessons learned from each domain.

The book addresses the future prospects of Industry 4.0 and beyondthe interconnected, data-driven evolution of manufacturing. It examines the potential impact of emerging technologies such as the Industrial Internet of Things (IIoT), 5G, and advanced robotics on the manufacturing landscape. Challenges and future possibilities pertaining to research and advancement in smart manufacturing within the domains of Aviation, Semiconductors, and Healthcare sectors are also discussed.

The chapters are written in a tutorial style to allow early-career researchers and industry practitioners an in-depth understanding of the various topics. The book serves as a reference for researchers, engineers, and students seeking to understand the synergy between AI, Industry 4.0, LLMs, and real-world applications.

Written in a tutorial style to allow early-career researchers and industry practitioners an in-depth understanding Covers the synergy between AI, Industry 4.0, large language models (LLMs) and real-world applications Features real-world industrial case studies that demonstrate the practical applications of smart manufacturing

Autorentext

M M Manjurul Islam received his BS in Computer Science and his MS leading to a PhD in Computer Engineering from the University of Ulsan, South Korea. He is a Research Associate in the School of Computing, Engineering, and Intelligent Systems at Ulster University, UK, and a member of the Smart Nano NI consortium project. His research is supported by UK Research and Innovation. Previously, he worked as an Assistant Professor in the Department of Computer Science at the American International University-Bangladesh and as a Postdoctoral Fellow at the Center for Digital Industry of Fondazione Bruno Kessler in Italy. Additionally, he worked as an engineer at Accenture Plc and Telenor ASA for seven years. Dr. Islam has made significant contributions to the fields of artificial intelligence, smart manufacturing, and prognostics and health management, publishing over 50 peer-reviewed papers. He has delivered talks at international conferences in Australia, China, South Korea, Spain, Italy, the UK, Canada, and the USA, and has been invited as a lecturer for the Brain Korea 21 (BK21) program. He actively reviews over 150 publications and grant applications and evaluates funding proposals for institutions such as the National Science Centre in Poland. Dr. Islam serves on the advisory panel for AI and Signals journals, has been involved in several international and national conference technical committees, and has edited special issues for journals including Energies, Frontiers in Energy Research, and Micromachines. Dr. Islam is an Associate Fellow of Advance HE and a Senior Member of IEEE.

Marcia Lourenco Baptista is an Adjunct Professor (Invited) at NOVA IMS and holds a PhD from the MIT Portugal Program (Instituto Superior Técnico). She teaches various topics in big data analytics, data science, and machine learning. Her main areas of research are predictive analytics, explainable artificial intelligence, and neural networks. She has studied maintenance and the industrial field for several years, focusing on interpretability, generative modeling, and prognostics techniques. She lectured on the course Maintenance Modeling and Analysis at TU Delft for three years. She is the author of more than 45 scientific articles, most of which are classified in Quartile 1 or published in high-impact conferences. Before joining NOVA IMS, she was an invited researcher at several international research centers, including NASA Research Center, ITA, the National Institute of Informatics in Tokyo, and Delft University of Technology.

Faisal Tariq, a Senior Lecturer in the James Watt School of Engineering (JWSE) at the University of Glasgow, leads research in applications of AI and data science in wireless communications, smart energy, manufacturing, and healthcare technologies. He also has expertise in designing privacy and security solutions using distributed ledger technology (DLT). He has edited three books on AI for smart healthcare, mobile apps engineering, and emerging 6G wireless technologies. His research is supported by the British Council Researcher Links grant, the Charles Wallace Trusts fund, and the QMUL-GCRF pump-priming grant. He is a Senior Member of IEEE and has published 50 articles, book chapters, and conference papers. He has been involved in several international and national conference technical committees, journal editorial boards, and has given invited and keynote presentations in several countries around the world.

Inhalt

Introduction to Smart Manufacturing.- Artificial Intelligence for Smart Manufacturing: Opportunities and Prospects.- Predictive Maintenance for Structural Integrity with Computer Vision.- Fostering Precession: Unsupervised Anomaly Detection in Industrial Operation.- Deep Reinforcement Learning for Facilitating Human-Robot-Interaction in Manufacturing.- Explaining And Interpreting Machine Wants and Desires in an Industry 4.0 Environment.- Improving Prognostics and Health Management in the Manufacturing and Design of Future Aircraft.- Artificial Intelligence: Applications in the Smart Healthcare Industry.- Efficient Wafer Defect Patterns Recognition Using Transformer Learning.- Manufacturing Technologies for Automotive Industries.- Review and Future Prospects of Industry 4.0.