Industrial Intelligence: Methods and Applications

Erscheinungsjahr 2025
ISBN: 978-3-031-81476-1
Verlag: Springer Nature Switzerland

Buch, Englisch, 491 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1012 g

Reihe: Springer Series in Advanced Manufacturing

This book explains the AI algorithms, techniques, and application methods used in manufacturing, and how they contribute to the advancement of industrial intelligence.

Industrial artificial intelligence (IAI) is rapidly evolving alongside the development of smart manufacturing, which cannot be achieved without intelligence at its core. IAI enables intelligent and resilient manufacturing systems, making them fault-tolerant, on-demand, and self-organizing. It also provides on-demand manufacturing services to end users by optimally coordinating distributed manufacturing resources, augmented by AI methodologies.

This book will be of interest to researchers and professionals in the manufacturing industry.

Zielgruppe

Research

Autoren/Hrsg.

Liu, Tianyuan

Lu, Yuqian

Bao, Jinsong

Zheng, Yu

Fachgebiete

• Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik Fertigungstechnik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung

Inhaltsverzeichnis

Chapter 1. Industrial Intelligence Overview.- Chapter 2. Basics of Industrial Intelligence.- Chapter 3. Feature Identification and Optimization of Structural Design.- Chapter 4. Structural Analysis of Design Documents.- Chapter 5. Intelligent Design of Assembly Process.- Chapter 6. Intelligent Detection of Industrial Defect Images.- Chapter 7. Manual Work Behavior Detection and Monitoring.- Chapter 8. Human-Machine Collaboration in Manufacturing Process.- Chapter 9. Intelligent Management and Control of Production Operations.- Chapter 10. Equipment Fault Diagnosis and Preventive Maintenance.- Chapter 11. Equipment fault diagnosis and preventive digital twins and industrial intelligence.- Chapter 12. Equipment Fault Diagnosis and Preventive AI+AR-assisted Manufacturing.

Über Autor(innen)

Tianyuan Liu received his doctorate degree in engineering from Donghua University in 2021. He then went to The Hong Kong Polytechnic University for two years of postdoctoral research. He is currently an associate professor and master's supervisor of Donghua University. His research interests include industrial artificial intelligence, computational vision, deep learning, welding automation, and digital twins. He presided over three scientific research projects, including the National Natural Science Foundation of China. He has published more than 40 SCI/EI papers and 2 monographs. In addition, he served as associate editor and editorial board member of more than 10 SCI/ core journals in the field of intelligent manufacturing.

Jinsong Bao received his PhD degree in engineering from Shanghai Jiao Tong University in 2002. He is currently a professor, doctoral supervisor and director of Intelligent Manufacturing Research Institute of Donghua University. His main research interests include intelligent manufacturing, digital twin systems, virtual reality/human-computer interaction technology, intelligent measurement and control and robotics, big data/industrial cloud/information visualization technology for engineering fields. He presided over three National Natural Science Foundation of China projects, one key research and development plan of the Ministry of Science and Technology, and more than 10 other provincial and ministerial projects, in addition to undertaking more than 20 enterprise research projects. He has published more than 160 SCI/EI papers, more than 10 authorized invention patents, 8 software copyrights, and 5 monographs.

Yu Zheng received the B.Eng. degree in 2000, M.Eng. degree in 2007, and Ph.D. degree in 2015 from Shanghai Jiao Tong University, Shanghai, China. She is a Professor with the Institute of Intelligent Manufacturing and Information Engineering, Shanghai Jiao Tong University, where she focuses on intelligent manufacturing systems, digital twin, data mining and knowledge-based engineering. She has led and participated in many research projects funded by National Natural Science Foundation of China (NSFC), Ministry of Science and Technology (MOST), and Shanghai government. Her research contributions were recognized with the Third Prize of Shanghai Science and Technology Progress Award in 2013. She has published over 80 papers and 2 monographs. She serves as the deputy director of the editorial board of Ship Engineering.

Yuqian Lu received his B.Eng. degree in mechatronics engineering from Dalian University of Technology in 2012 and his Ph.D. degree in mechatronics engineering from The University of Auckland in 2016. He is a Senior Lecturer in the Department of Mechanical and Mechatronics Engineering at The University of Auckland, New Zealand, where he leads the Industrial Artificial Intelligence Research Group. His research focuses on manufacturing systems, industrial artificial intelligence, and human–robot interaction. He is on the board of several international scientific committees, journals, and conferences.