Artificial intelligence for smart manufacturing and industry X.0

Contributor(s):

Material type: Text

TextSeries: Springer Series in Advanced Manufacturing (SSAM)Publication details: Cham Springer 2025Description: xiv, 226 pISBN:

9783031801532

Subject(s):

DDC classification:

006.3 ISL

Summary: 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 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. (https://link.springer.com/book/10.1007/978-3-031-80154-9)

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	IT & Decisions Sciences	006.3 ISL (Browse shelf(Opens below))	1	Available		009189

Table of contents:
Front Matter
Pages i-xiv
Download chapter PDF
Introduction to Smart Manufacturing
Faisal Tariq, M. M. Manjurul Islam, Marcia L. Baptista
Pages 1-7
Artificial Intelligence in Smart Manufacturing: Emerging Opportunities and Prospects
M. M. Manjurul Islam, Jakaria Islam Emon, Kok Yew Ng, Abdoreza Asadpour, M. M. Rafi Al Aziz, Marcia L. Baptista et al.
Pages 9-36
Advancements in AI-Based Anomaly Detection for Smart Manufacturing
Md. Rashedul Islam, Fahmid Al Farid
Pages 37-68
Deep Reinforcement Learning for Facilitating Human-Robot Interaction in Manufacturing
Nathan Eskue, Marcia L. Baptista
Pages 69-95
Large Language Models (LLMs) for Smart Manufacturing and Industry X.0
Marcia L. Baptista, Nan Yue, M. M. Manjurul Islam, Helmut Prendinger
Pages 97-119
Integrating Prognostics and Health Management in the Design and Manufacturing of Future Aircraft
Marcia L. Baptista, Felipe Delgado, Nathan Eskue, Manuel Arias Chao, Kai Goebel
Pages 121-145
Improved Wafer Defect Pattern Classification in Semiconductor Manufacturing Using Deep Learning and Explainable AI
M. M. Manjurul Islam
Pages 147-164
Enhancing Acoustic Emission Driven Smart Gas-Pipeline Monitoring with Graph Neural Network
Murshedul Arifeen, Md. Junayed Hasan, Ali Rohan, Somasundar Kannan, Anil Prathuru
Pages 165-178
Trustworthy Artificial Intelligence for Industrial Operations and Manufacturing: Principles and Challenges
Md Alamgir Kabir, M. M. Manjurul Islam, Narayan Ranjan Chakraborty, Sheak Rashed Haider Noori
Pages 179-197
Review and Future Prospects of the Smart Factory
Marcia L. Baptista, Elsa M. P. Henriques
Pages 199-224
Back Matter
Pages 225-226
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Editors and Affiliations
School of Computing, Engineering and Intelligent Systems, Ulster University, Derry-Londonderry, UK
M. M. Manjurul Islam

NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Lisboa, Portugal
Marcia L. Baptista

James Watt School of Engineering, University of Glasgow, Glasgow, UK
Faisal Tariq

[https://link.springer.com/book/10.1007/978-3-031-80154-9]

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 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.

(https://link.springer.com/book/10.1007/978-3-031-80154-9)

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