MEAE 2026 | 机械工程与航空航天工程·Mechanical Engineering and Aerospace Engineering

Special Session 2

AI-Native Quality Control for Large Aircraft Mass Production

Large aircraft mass production is evolving from fixed-position assembly to pulse-line assembly and further toward intelligent island-based assembly, driven by advances in industrial robotics, big data, and artificial intelligence. Built upon dynamically reconfigurable production units consisting of aircraft, equipment, and personnel, this emerging paradigm is transforming assembly lines from rigid organizational structures into systems that are more flexible, adaptive, and increasingly autonomous. In this context, quality control is shifting from post-process inspection of individual operations toward real-time sensing, analysis, prediction, and control across complex assembly processes, dynamic resource configurations, and full-process quality formation mechanisms.
This special session focuses on quality control in large aircraft mass production under the coupled influence of human, machine, material, method, and environment factors. Particular attention will be given to the adaptive coordination of assembly quality and production efficiency under conditions such as task switching, takt-time fluctuations, resource reconfiguration, process deviations, and environmental disturbances. The session welcomes original contributions addressing AI-native quality control, intelligent sensing and diagnosis, digital twin-enabled quality management, large-model-driven generation, and agentic AI for industrial execution and collaboration.

Topics of interest include, but are not limited to:

AI-native quality control for large aircraft mass production
Intelligent island-based assembly and quality assurance
Assembly disturbance perception and adaptive control
Digital twin-driven quality management and control
Industrial big data analytics for quality improvement
Intelligent sensing through causal analysis and deep learning
LLMs-based generation and AI-ready corpora construction
Agentic AI-enabled execution and multi-agent collaboration

Submission Guideline:

Please submit your manuscript via Online Submission System: https://easychair.org/conferences/?conf=meae2026
Please choose "Special Session 2"

Organizers:

	Yan-Ning Sun, Shanghai University, China Yan-Ning Sun is a Lecturer and Master’s Supervisor at the School of Mechatronic Engineering and Automation, Shanghai University, where he also serves as the Director of the CIMS Teaching and Research Section. His research focuses on industrial artificial intelligence, intelligent manufacturing for high-end equipment, and industrial decision software. He has authored/co-authored over 50 papers in top-tier international journals and holds more than 10 patents and software copyrights. He is a core member of the Shanghai Key Laboratory of Smart Manufacturing and Robotics and participates actively in academic societies. He has led several national and provincial research projects and has been recognized with awards including the Shanghai Science and Technology Progress First Prize. He also regularly serves as a reviewer for leading international journals and conferences.
	Jin-Hua Hu, Chang’an University, China Jin-Hua Hu is a Lecturer at the School of Construction Machinery, Chang’an University. Her research focuses on network modeling, assembly schedule monitoring, and intelligent workshop scheduling methods for aircraft final assembly in the context of intelligent manufacturing. As a key technical contributor, she has participated in multiple projects on aircraft final assembly scheduling optimization. She has published five papers in leading journals, including the Journal of Manufacturing Systems (JCR Q1, IF:12.1), Advanced Engineering Informatics (JCR Q1, IF:8.0), and Computers & Industrial Engineering (JCR Q1, IF:6.7).
	Zeng-Gui Gao, Shanghai University, China Zeng-Gui Gao is currently an associate professor and doctoral supervisor at the School of Mechatronic Engineering and Automation, Shanghai University. He serves as Deputy Director of the Bionic Intelligent Robot Dreamworks for Special Purposes at Shanghai University and Deputy Director of the Intelligent Manufacturing Industry Software Research Centre. He is also Deputy Secretary General of the Industrial Intelligent Manufacturing Technology Committee of the Shanghai Mechanical Engineering Society and a Director of the journal Computer Integrated Manufacturing Systems. His research interests include human-robot interaction and collaboration, as well as industrial digital twin.
	Li-Lan Liu, Shanghai University, China Li-Lan Liu is currently a professor and doctoral supervisor at the School of Mechatronic Engineering and Automation, Shanghai University. She serves as Leader of the first-class discipline of Mechanical Engineering at Shanghai University. She is also Director of the Shanghai Design Innovation Center and Vice Chairman of the Shanghai Mechanical Engineering Society. Her research focuses on advanced intelligent manufacturing technologies and systems, with an emphasis on addressing the challenges of perception, decision-making, and execution in complex manufacturing environments. Her work centers particularly on industrial digital twin methodologies and their deployment in aerospace, aviation, marine engineering, automotive production, and steel metallurgy. She has published more than 100 SCI-indexed research articles and authored five scholarly monographs.
	Wei Qin, Shanghai Jiao Tong University, China Wei Qin is currently a Professor and the Associate Head of Department of Industrial Engineering at School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China. His current research interests include modeling, control and optimization He serves as the associate editor and guest editor for several top international journals, including the Journal of Intelligent Manufacturing, International Journal of Computer Integrated Manufacturing, and Journal of Cleaner Production.