Please submit your manuscript via Online Submission System: http://www.zmeeting.org/submission/meae2024
please choose Special Session: Digital Quality Control of Aerospace Equipment
Lilan Liu, Shanghai University, China
Lilan Liu is currently a professor in the School of Mechatronic Engineering and Automation of Shanghai University, and also serves as the director of the Shanghai Key Laboratory of Intelligent Manufacturing and Robotics. She has been recognized as an excellent academic/technical leader in Shanghai, with a main research interest in industrial big data and digital twins, especially in digital quality control for aerospace equipment.
Yan-Ning Sun, Shanghai University, China
Yan-Ning Sun is currently a Lecturer at School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China. His current research interests include the fundamental study of artificial intelligence, complex network and industrial systems, and data-driven decision-making methods in complex industrial processes. He has been serving as a reviewer for many top-tier international journals and conferences in his research field.
Jinsong Bao, Donghua University, China
Jinsong Bao received the M.S. degree in mechanical engineering from Northeastern University, Shenyang, and the Ph.D. degree in mechanical engineering from Shanghai Jiao Tong University, Shanghai, China, in 2002. He is currently a Professor with the College of Mechanical Engineering, Donghua University, Shanghai, China. His current research interests include intelligent manufacturing, industrial artificial intelligence, and computer vision.
Zenggui Gao, Shanghai University, China
Zenggui Gao is currently an Associate Professor at School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China. His current research interests are human-machine collaboration and digital twin systems.
Wei Qin, Shanghai Jiao Tong University, China
Wei Qin is currently an Associate 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 of complex systems, machine intelligence and smart manufacturing.
As a complex giant system engineering, the design, manufacturing, and maintenance of aerospace equipment pose significant challenges in managing its complexity and uncertainty. For example, in the aircraft manufacturing process, it is difficult to control the quality consistency under the coupling of shape and performance, such as the interference of assembly dimensions, the over-deviation of key characteristics, and the uneven distribution of stress. During the service process of the aerospace docking agency, the push rod springs face uncertain impact loads and stress relaxation issues, which pose serious challenges to long-term safe and stable operation. The concept of digital twins originated in aerospace engineering and has become a consensus to solve the above quality control problems. However, it still faces key issues such as high-fidelity modeling of external morphology and internal mechanisms, and dynamic regulatory mechanisms of virtual and digital systems.
This special session focuses on advanced theories, technologies, and methods of digitization, networking, and intelligence to enhance the design capabilities, production efficiency, and product quality of aerospace equipment. In addition, potential emerging technologies such as artificial intelligence-generated content (AIGC), metaverse, large language models (such as GPT), and text-to-video models (such as Sora) to enhance the digital quality of aerospace equipment are also of great interest.
The topics include, but will not be limited to the following:
High-fidelity digital twin modeling of aerospace equipment
Quality consistency control of design and manufacturing for aerospace equipment
Coordinated quality control of aerospace equipment assembly considering shape and performance coupling
Health status evaluation and predictive maintenance of aerospace equipment
Fault diagnosis of aerospace equipment based on data and mechanism fusion
Potential emerging technologies for digital quality control of aerospace equipment
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