Artificial Intelligence for Smart Manufacturing and Industry X.0 - Faisal Tariq;Marcia L. Baptista;M. M. Manjurul Islam

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 beyond—the 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 will be 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.